Rethink Data

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 1 |

RETHINK

DATA

Put More of Your Business Data to Work—

From Edge to Cloud

WITH RESEARCH AND ANALYSIS BY IDC

A SEAGATE TECHNOLOGY REPORT

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About the Report ............................................................................ 03

Summary of Findings ...................................................................... 04

Survey Highlights ........................................................................... 05

Section I: Global Findings

Chapter 1: The Growth and Sprawl of Data ..................................... 07

Chapter 2: A Treasure Trove of Value ............................................... 13

Chapter 3: The Multicloud ............................................................... 19

Chapter 4: Data Management Challenges and

the Multicloud Ecosystem .............................................. 24

Chapter 5: DataOps: The Missing Link of Data Management ........... 29

Chapter 6: Better Business Outcomes ............................................ 33

Chapter 7: Data Security and Data Management ............................. 36

Section II: Regional Findings

Chapter 1: Asia Pacic and Japan ..................................................... 40

Chapter 2: China ............................................................................... 44

Chapter 3: Europe ............................................................................ 48

Chapter 4: North America ................................................................. 52

Table of Contents

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About the Report

This Seagate Technology report

draws on a global survey sponsored

by Seagate and conducted by the

independent research rm IDC, which

took place in December 2019 and

January 2020. Seagate authored the

following report to share its forecasts

and opinions, based on the survey

ndings, along with analysis from

IDC. Any content, data, analysis, or

views not attributed to IDC or IDC

analysts are those of Seagate. The

quantitative web survey queried 1500

respondents globally (375 in North

America, 475 in Europe, 500 in APJ,

and 150 in China) from mid-sized

businesses to larger enterprises

in Canada, United States, United

Kingdom, France, Germany, Russia,

Australia, Japan, India, South Korea,

Taiwan, and China. The survey

participants’ professional titles

include CIO, CTO, IT VP, director,

executive, COO/LOB, storage

architect, and solution architect.

Seagate Technology has been a global leader offering data storage and

management solutions for over 40 years. Seagate crafts the datasphere,

maximizing human potential by innovating world-class, precision-

engineered data storage and management solutions with a focus on

sustainable partnerships.

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 4 |

Summary of Findings

Today’s unprecedented growth

and sprawl of data result in a

complex movement of data within

an increasingly varied ecosystem

that includes multicloud and the

edge. The complexity of data’s

location compounds business

owners’ data management

challenges. Most data available

to businesses goes untapped.

The survey zeroed in on a solution

to today’s data management

dilemmas: DataOps—a discipline of

connecting data creators with data

consumers. Business leaders who

implement DataOps can count on

better business outcomes.

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Survey Highlights

• Over the next two years, enterprise

data is projected to increase at a

42.2% annual growth rate.

• Only 32% of data available to

enterprises is put to work. The

remaining 68% goes unleveraged.

• On average, organizations now

periodically transfer about 36%

of data from edge to core. Within

only two years, this number will

grow to 57%. The volume of data

immediately transferred from

edge to core will double, from 8%

to 16%. This means enterprises

will have to manage a lot more

data in motion.

• Managing data in the multicloud

ecosystem is a top data

management challenge expected

over the next two years—with

managing data in hybrid cloud a

close second.

• The top ve barriers to putting data

to work are: 1) making collected data

usable, 2) managing the storage

of collected data, 3) ensuring that

needed data is collected, 4) ensuring

the security of collected data, and 5)

making the different silos of collected

data available.

• The solution to a great deal of

data management challenges

is DataOps—the discipline

connecting data creators with

data consumers. Only an average

of 10% of organizations report

having implemented DataOps fully

across the enterprise. A majority of

respondents say that DataOps is

“very” or “extremely” important.

• Along with other data

management solutions, DataOps

leads to measurably better

business outcomes: boosted

customer loyalty, revenue, prot,

and a host of other benets.

• Improving data security is the

most important factor driving the

changes to how organizations

manage central storage needs.

• Two thirds of survey respondents

report insufcient data security,

making data security an essential

element of any discussion of

efcient data management.

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Global

Findings

SECTION ONE

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CHAPTER ONE

The planet’s population is at 7.8

billion and it keeps growing. More

and more people work from home.

Technologies like the Internet of

Things (IoT), the growth of edge

computing, edge data centers,

and articial intelligence (AI)

proliferate. Demand for consumer

endpoint devices is growing.

All these factors result in the

proliferation of enterprise data.

In order to smartly manage the ever-

accelerating amount of information,

business owners need to understand

how and where data is gaining in

volume. Two concepts help illustrate

this trend: growth and sprawl.

Data growth refers to the

percentage by which the

datasphere increases over time.

The datasphere is humanity’s ever-

expanding dimension of living data

that reects and amplies life in

innite ways.

Data sprawl describes the spread

of this growing data through

various congurations—from

endpoints through edge to cloud.

Data is who we are, who we have

been, and who we are becoming.

The Growth and the

Sprawl of Data

2020: Total Enterprise Data

Volume of 1PB

160TB in other locations

390TB in edge & remote locations

407TB in 3rd-party data centers

498TB in cloud repositories

(public, private, industry)

570TB in internally managed

data centers

89TB in other locations

193TB in edge & remote locations

201TB in third-party data centers

221TB in cloud repositories

(public, private, industry)

297TB in internally managed

data centers

2022: Total Enterprise Data Volume

of Approx. 2.02PB

+42.2%

avg. annual growth

over 2 years

Expected Annual Data Growth Rate

FIGURE 1

SEAGATE POV

Source: The Seagate Rethink Data Survey

, IDC, 2020

1 The Seagate Rethink Data Survey by IDC is IDC’s name for the survey whose ndings are discussed in this report.

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 8 |

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

Total NA China Europe APJ TelcoTrans/EV Media Mfg Other

Internally managed

enterprise data

centers

Third-party

managed enterprise

data centers

Edge data centers

or remote locations

where data is

centrally stored

Cloud repositories

(public, private,

industry)

Other locations

Data growth has been and will

continue to be unprecedented in

volume. The survey conducted

shows that in just two years, from

the beginning of 2020 through the

beginning of 2022, enterprises will

see a 42.2% annual increase in the

volume of generated data. In order

to project the volume of enterprise

data, IDC uses enterprise storage

capacity as a proxy (Figure 1).

The survey data indicates that three

factors are the most important

catalysts for the growth of stored data:

1. Increasing use of analytics.

2. The proliferation of IoT devices.

3. Cloud migration initiatives.

Data sprawl reects how business

data is scattered.

Enterprise data doesn’t reside in one

location, which adds complexity to

data management.

Survey respondents indicate

that approximately 30% of

stored data is found in internal

data centers, 20% in third-party

data centers, 19% in edge data

center or remote locations, 22%

in cloud repositories, and 9% in

other locations. This distribution

won’t change signicantly over

the next two years, indicating that

enterprise storage environments

will remain dispersed and complex

for the foreseeable future.

Enterprises will have an ever-

increasing need to manage this

scattered data wherever it exists.

Survey Findings

Where Data Will Be Stored in 2 Years (Average)

FIGURE 2

DATA BY IDC

Source: The Seagate Rethink Data Survey, IDC, 2020

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Innovation is not driven by trends, but by the need to create more

value under constraints—and there are constraints everywhere.

Access to the full value of data is one of them.

Take a look at our data-driven world. Data is growing, and the

rate of growth is accelerating. The sum of data generated by 2025

is set to accelerate exponentially to 175 zettabytes. More data is

created per hour now than in an entire year just two decades ago.

Data is human potential. The sharpest minds attempt to harness

the power of data.

The constraints on capturing the full potential of data are both

systemic and operational. At zettabyte scale, there needs to be

a simple, secure, and economic way to capture, store, and activate

data. People who use the data do not want to worry about this.

How can we solve this data management puzzle at scale and

enable other innovations that change the way we live, work,

commute, and take care of our planet?

If we address the opportunities while navigating these constraints

and enable the most value, then does it matter whether the

innovation used the latest trend or not? Trends may capture our

minds, but if the initial excitement does not translate into value for

companies and for humanity, innovation doesn’t take hold.

When we solve problems at scale, we need to bring multiple

worlds together, and invite their synergy.

DAVE MOSLEY

CEO OF SEAGATE TECHNOLOGY

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Delving Deeper

Additional research done by IDC

as part of the Global DataSphere

sheds light on why enterprises will

see so much data growth.

• The amount of new data

created each year is growing at

a compound annual growth rate

of about 26% between 2015

and 2025.

• The number will be up to 175.8ZB

of new data created in 2025

(compared to 18.2ZB in 2015).

• Enterprise data stored in 2025

will amount to 9ZB—while in

2015 was 0.8ZB.

• The Rethink Data survey backs

up this trend: respondents

consistently indicate that for

their organization, enterprise

data capture and storage

are expanding. It indicates

that the increasing use of

analytics, increasing use of IoT

devices, and cloud migration

initiatives are the top three most

important factors impacting the

growth of stored data.

• Leveraging the Global

StorageSphere

and Global

DataSphere studies, we can

better understand data sprawl.

• The Global DataSphere indicates

that 65% of data created in

2015 was at endpoints, and

35% in the core and edge.

• By 2025, the 44% of data

created in the core and edge will

be driven by analytics, articial

intelligence, and deep learning,

and by an increasing number of

IoT devices feeding data to the

enterprise edge.

• Data is shifting to both the core

and the edge: By 2025 nearly

80% of the world’s data will be

stored in the core and edge, up

from 35% in 2015.

• By 2025, IDC predicts 12.6ZB of

installed capacity—HDD, ash,

tape, optical—will be managed

by enterprises. Cloud service

providers will manage 51% of

this capacity.

Endpoint-Create

Endpoint-Store

Edge-Create

Edge-Store

Core-Create

Core-Store

80%

70%

60%

50%

40%

30%

20%

10%

20112010 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Where Data Is Created and Stored

FIGURE 3

DATA BY IDC

1 Data Age 2025, sponsored by Seagate, with data from Global DataSphere, IDC, May 2020

2 The Global StorageSphere, IDC, 2020

Source: Data Age 2025, sponsored by Seagate, with data from IDC Global DataSphere and StorageSphere, May 2020

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The edge can be found anywhere

and everywhere, in a wide range

of locales, including:

• Floors of manufacturing plants

• Roofs of buildings

• Cell phone towers in the eld

• Barns on farms

• Autonomous vehicles

• Platforms at oil and gas elds

The edge is a location, not a

thing. It is the outer boundary of

the network—sometimes found

hundreds or thousands of miles

from the nearest enterprise or

cloud data center, and as close to

the data source as possible. The

edge is where real-time decision-

making takes place.

What Is the Edge?

More and more data needs

analysis and action at the edge.

A unique mix of technology and

economics will make it practical

to assemble, store, and process

more data at the edge.

The shift of data’s center of

gravity to the edge is driven by

four technologies:

1. AI has become cost-effective

and practical.

2. Billions of IoT devices are being

deployed.

3. Wireless operators are upgrading

their networks to the fth

generation of cellular mobile

communications (5G).

4. Innovations in edge data centers

are solving for the complexities

of distributed facilities and unit

cost economics.

In addition to these technologies,

key factors drive demand for

edge computing: latency; high

data volume accompanied by

insufcient bandwidth; cost; and

data sovereignty and compliance.

As massive amounts of data are

created outside the traditional data

center, the cloud will extend to

the edge. It won’t be cloud versus

edge; it will be cloud with edge.

Giving Data an Edge

SEAGATE POV

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The amount of data stored at the

edge is increasing at a faster rate

than data stored in the core.

This survey found that, on average,

organizations periodically transfer

about 36% of data from the edge to

core. Within only two years this

percentage will grow to 57%.

The volume of data immediately

transferred from edge to core will

grow from 8% to 16% as well. To

accommodate this increase, data

management plans must enable a

much more signicant movement of

data—from endpoints, through edge,

to public, private, or industry clouds.

What does this mean for

enterprises?

It means a greater extent of data

sprawl, which enterprises are

increasingly tasked to manage.

Data sprawl leads to siloed data,

which cannot be accessed by all

who need it. Without automated

methods, managing data sprawl

requires signicant human labor and

redundant tool purchases.

An increasing amount of storage

will be expected to be compute-

conversant—if not, eventually,

offering compute functions.

The edge is expected to store

critical data and insight that

fuels latency-sensitive requests

from endpoint transactions and

services. At the same time, the

edge will make possible distributed

computing to perform analysis of

streaming data.

Streamed data is likely to be cached

in storage media until the servers

complete analytics.

As such, the line between

storage and caching of data at

the edge may become blurred,

especially given the expectation that

data will be stored for only a short

period of time until it is analyzed or

processed before moving relevant

data to the core.

In Focus: Data at the Edge

ANALYSIS BY IDC

Periodically

transferred to core

2020

Immediately

transferred to core

Mix of models

2022

Collected and

stored at edge

2022

2020

2022

2020

2022

2020

2022

2020

14%

38%

15%

36%

57%

16%

Approach to Collecting Data at the Edge

Now vs. Two Years From Now

FIGURE 4

Source: The Seagate Rethink Data Survey, IDC, 2020

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CHAPTER TWO

Winning enterprises know that how

they treat their data can directly

translate into business growth.

To businesses entrusted with

data—for example, cloud

providers, smart devices

manufacturers, hospital networks,

streaming networks, grocery store

chains, just to name a few—data

means investment into collecting,

analyzing, and storing data in

various repositories.

The more they can leverage their

investment by putting that data

to use, the more value they will

derive from it.

The datasphere contains an ever-expanding

kaleidoscope of human endeavors: urgent life-

critical information, histories of knowledge,

operational instructions, manufacturing

processes, chronicles of emotion, etc.

Data is a treasure trove of value.

A Treasure Trove of Value

SEAGATE POV

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Missing Out on Data

The Rethink Data survey has found

that organizations report that much

of their business data is not put

to use, or activated. While the data

offers value, that value too often

goes uncaptured.

• Survey respondents estimated

their organizations collect only 56%

of the data potentially available

through its operations. This means

organizations are missing out on

almost half of data.

• Out of that 56%, only 57% of data

was used by the organization.

• 43% of the captured data went

largely unleveraged.

• This means that only 32% of the

data available to enterprises

was put to work. As much as

68% of data goes unleveraged.

of captured data

is exploited

57%

43%

of data goes

uncaptured

44%

of data is captured

through operations

56%

remains

largely unused

How Much Data Actually

Gets Put to Work?

FIGURE 5

DATA BY IDC

Source: The Seagate Rethink Data Survey, IDC, 2020

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Overall, for many organizations,

data is an understated, intangible

asset that is not represented

on the balance sheet—even

though data is increasingly being

leveraged to drive new sources

of revenue and improve customer

experiences and operational

efciencies. Simply stated, data

can increase enterprise value even

without being formally represented

in nancial statements.

However, to realize the value of

data, it must be put to work. The

survey established that only 32%

of the data available to enterprises

was leveraged.

Putting data to work and unlocking

its value rst requires a method for

measuring the value of the data itself.

In a survey conducted for Seagate in

2018

, IDC found that the workforce

in only 25% of organizations

worldwide had systems and

processes for actively quantifying the

value of enterprise data, at least in

certain situations.

Opportunities abound for

organizations to better leverage

data for competitive advantage.

In the same survey, only 11% of

all organizations reported that

they considered themselves to be

industry leaders in the ability to

leverage the value of corporate data

compared to other organizations

in their industry. The percentage

was even lower for the healthcare

and transportation industry

verticals, where fewer than 10% of

organizations identied themselves

as industry leaders.

One thing is certain: Companies

want to have vibrant data lakes

where fresh data is taken in

and old, stale data is moved to

low-cost storage domains. No

company wants their data lake

to turn into a data swamp where

unleveraged yet potentially

useful data sits dormant on

storage media.

The value of data for any given

enterprise involves numerous

variables, including the industry

within which it is created, the

purpose it serves, and if and how it

is eventually monetized.

Take, for example, the types of

data created and managed by a

hospital: patient data, scheduling,

insurance and billing data, MRIs,

cancer treatments, operations and

nancial data, and advertising.

Regulations require that hospitals

keep data for many years after

the death of a patient (this is likely

dormant data that potentially can

be leveraged). The value of each

dataset will vary, especially data that

is highly secure due to privacy and

compliance requirements. In the

future, video sessions for remote

care, the surgeon’s actions during an

operation, or even robotic operation

procedures will be recorded and

saved for a variety of reasons—even

if only to serve for instructional or

legal purposes. Can one really place

a value on this data?

IDC has yet to fully quantify the

value of the global datasphere.

Nevertheless, making some general

assumptions about the value of a

byte created in a hospital with a staff

of 1000 to 2500 and revenues of

over $1B, IDC believes it is likely the

value of data created at that hospital

could be approaching several

hundred million dollars.

The Value of Data

ANALYSIS BY IDC

1 Worldwide DATCON and VoB surveys, IDC, September 2018

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Every business is a data business. But enterprise data is of little

value if it is not used. To efciently and smartly make sense

of data, we need to see data lakes as reservoirs where many

vibrant rivers meet; the task is to comingle the incoming

data currents. There is a need to share data with other lakes,

in order to cross-reference and run analytics on disparate

streams of data.

Take autonomous cars. To begin with, there’s value in

analyzing data from one vehicle, and within one company.

Cross-analyzing that one vehicle’s data with vehicles from all

autonomous car companies adds another layer of insight. For

a richer picture, zoom out from there to integrating knowledge

derived from that one vehicle’s data with data that proceeds

from the billions of sensors that make up a smart city. The

fuller picture may be useful to the regional government and

city planners who implement better public safety standards

and trafc ows.

The more pieces you put together, the bigger a puzzle you can

solve. You can tackle a much higher-order problem if you share

data, cross-referencing various streams of information for analysis.

That’s why enabling the movement of data matters. Data needs

to move in order to allow for interconnectedness of data—and the

insights that result.

RAVI NAIK

SENIOR VICE PRESIDENT AND CIO

OF SEAGATE TECHNOLOGY

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Organizations must be able to

capture the right data, identify it,

store it where it is needed, and

provide it to decision makers in a

usable way.

Activating data—putting it to

work—starts with data capture.

Given the exponential growth

of data due to growing IoT

applications, enterprises do not

currently capture all available

data. To do so would result in

an overworked IT infrastructure

and tremendous costs. Without a

solid data management solution,

companies need to leverage

data ingest software capabilities

to identify and classify data at

the beginning of data lifecycle.

With proper identication and

classication, automated policies

can keep data through its useful

period, and then delete or archive

it when it is no longer needed. This

data pruning lowers costs and

avoids clogging data management

efforts. However, as data

management solution technology

advances, companies can consider

capturing more data, which can be

utilized for improving their articial

intelligence or machine learning.

Data capture then feeds into

data analysis. Most often, it’s

done in data lakes where data

is assessed by specialized data

analysis software based on industry

or other criteria. Data curators

and scientists use these tools to

mine information from the data to

provide to decision makers. Data

ingest into a data lake eliminates

the silos that separate data and

allow connections to be made from

seemingly unrelated data elements.

This is what leads to competitive

advantage.

Storage is integral to an

organization’s data strategy as it is

an active contributor to the process

of sorting and analyzing information.

The need for accurate, real-time

reporting of stored data to improve

workows, security, and resource

management initiatives continues

to drive demand for advanced data

management and analytics solutions.

Putting Data to Work

“Whether structured,

semistructured,

or unstructured;

generated by humans

or by machines; or

stored in the data

center or the cloud,

data is the new

basis of competitive

advantage.”

PHIL GOODWIN

RESEARCH DIRECTOR, IDC

At the Edge

Edge devices pose a particular challenge for data capture. Often, only the edge application knows what data

needs to be captured and acted upon, and what data is transient and thus can be ignored. A lot of decision-

making has to happen close to where data is created. But the newer centralized data management ingest

applications can leverage articial intelligence (AI) and machine learning (ML) to make the determination. These

programs can often identify sensitive data (like personally identiable information, private health information, credit

card numbers, etc.) and automatically mask them from the view of unauthorized personnel. This reduces the

chance of a data breach or inadvertent data disclosure.

ANALYSIS BY IDC

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Storage Innovation and the Value of Data

How and where data is stored

can greatly affect the value that

organizations can derive from it.

Here are areas in which storage

innovations bear directly on the

value of data.

1. Mass capacity is the enabler of

economies of scale. Analytics

improve as the data sets

that they access grow. Good

data management includes

ensuring that any new AI or ML

breakthroughs have access

to all of the data (as much as

physically possible). That is

why data storage companies

like Seagate focus on the

development of areal density

growth (so devices can store

more on a given unit).

The focus on areal density

innovation is based on data from

the world’s largest clouds.

Companies distribute data

roughly along the 90/10 rule:

90% of their data is stored on

hard drives and 10% on ash

memory devices (SSDs).

2. Another focus of storage

innovation has been delivering

higher bandwidth in order to

catalyze more robust movement

of data among storage,

networking, and compute

functions. This is important

for analytics. The backbone of

today’s analytics is the GPUs

(graphics processing units).

They require high bandwidth

ingest of information. To improve

bandwidth, for example,

organizations use composable

disaggregated architectures in

large-scale AI applications.

3. Security is another area of

innovation. There is continued

investment in device integrity

through open enclaves for

making rmware and compute

carry and house appropriate

protocols to digitally verify

devices. System solutions

benet from securities at the

component and device level.

Networking’s security benets

from the system’s security.

Finally, compute is made more

secure as a result of more

security networking.

4. In the longer term, data

movement architectures will

need to ensure that hardware

acceleration or hardware ofoad

through the storage systems

is done at appropriate points.

Specically, compression,

encryption, and deduplication

of data sets today get done

in compute. As a result, big

architectures are having to scale

because these tasks are done at

the higher level. This need not be

the case if innovation moves the

hardware acceleration and ofoad

to the storage or network layer.

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 19 |

CHAPTER THREE

Enterprises often struggle with

access to some of their data that

resides in the public cloud. They

also have a hard time with moving

data out of the cloud—not to

mention the fees that come with

the retrieval. This can stand in the

way of deriving optimum value from

data in the public cloud context—

because to be valuable, data has

to move. As a result, enterprises

increasingly mix up their storage

options, choosing to manage data

in the multicloud ecosystem.

Multicloud means the use of

more than one public cloud, which

is orchestrated through data

management. It may also include

a private cloud component. In

practice, multicloud can mean that

an enterprise houses some data in

public clouds and some in on-prem

private cloud.

Repatriating some data to private

cloud ensures a mesh of benets

from private cloud and from the

multicloud ecosystem.

1. Predictable economics at scale, which are under an enterprise’s control.

2. IP ownership, protection, and control of where that data physically

resides. This is useful, for example, in cases of data that needs a clear

audit trail or must comply with regulatory requirements.

3. Frequent access to large data sets. This is advantageous because

storage becomes very expensive when businesses need to read and

analyze the data frequently.

4. Compliance with regard to sensitive data sets. For example, life-

critical data that requires a certain provider’s and patient’s locality, as well

as compliance with laws and regulations such as HIPAA and GDPR.

The public cloud has catalyzed the growth of

countless companies. When enterprises reach

scale, however, they nd public cloud alone

too inexible.

The Multicloud

Private cloud offers the following advantages:

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 20 |

1. Fast growth and scale. In the multicloud, teams can build and deploy

an application or service in a common environment—plus benet from

the resulting revenue stream—without an investment in CapEx to do so

globally.

2. Access to a broad catalog of services that include applications,

compute, and monitoring resources. The multicloud features the ability

to turn on a high-performance compute that would be unaffordable

in most private clouds. GPUs can run between $500 thousand and

$5 million. Access to the public cloud means businesses can rent

them, solve a puzzle, and be done with them without leaving them to

sit idle. Access to public cloud compute means maximum compute

horsepower that enables an efciency growth and elimination of

stranded resources.

The public cloud benets that extend to the

multicloud ecosystem include:

Hybrid cloud seamlessly fuses

the resources of private and

public clouds to deliver an

integrated infrastructure as a

service. Hybridization ensures

communication between clouds

and interoperability, or the ability to

work across resource boundaries.

What does it look like? For

example, a company buys an OEM

enterprise service and puts it in a

colocation environment in a shared

building and connects to a cloud

service provider’s protocol on the

back end.

Hybrid cloud tends to be under one

software management portal, which

does the heavy lifting of connect;

the application knows how to talk to

the lead software layer.

In multicloud, the application,

which tends to be built as cloud-

native, knows how to use and run

the resources. Hybrid clouds often

have to gure out how to take

legacy systems and connect them

to the benets of public cloud.

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 21 |

50%

Public Cloud

51%

Enterprise Private Cloud

36%

Hosted Private Cloud

37%

Multicloud

37%

Hybrid Cloud

17%

Industry Cloud

Other

IDC analysts describe the multicloud

as “an organizational strategy or

the architectural approach to the

design of a complex digital service

that involves consumption of cloud

services from more than one cloud

service provider.” These may be

directly competing cloud services,

such as public object storage from

more than one public cloud service

provider, or IaaS and SaaS from one

or more cloud service providers.

According to IDC, in both of these

contexts, multicloud encompasses

a much larger universe than

does hybrid cloud and is only

gated by the cost and complexity

associated with enabling consistent

management and governance of

many different cloud options.

Multicloud as Organizational Strategy

Which of these cloud types is used in the

organization’s IT infrastructure?

FIGURE 6

Managing Data in More Than One Cloud

Survey respondents indicate a

wide range of cloud deployments,

including adoption of multiple cloud

service providers:

• The study reveals an equal

adoption of both multicloud and

hybrid cloud deployments (37%

each).

• Hybrid cloud use is typically

application-driven. The use of more

than one cloud develops organically

over time, with different lines of

business purchasing different cloud

providers for specic tasks.

• Businesses using multicloud

report that they do not necessarily

have a cohesive integration plan

for the long term.

• Multicloud and hybrid cloud

environments solve a number

of different problems. They

can make access to data and

analysis easier, cut costs, offer

administrators better control, and

boost data security.

But multicloud and hybrid cloud

ecosystems also pose some

challenges when it comes to

data management.

DATA BY IDC

ANALYSIS BY IDC

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 22 |

• Managing data in multicloud

environments is the number one

data management challenge

expected in the next two years

by survey respondents.

• Managing data in hybrid clouds

is listed as the second most

important data management

headache.

Takeaways

“Multicloud deployments are much more

difcult to orchestrate and manage

consistently, as the infrastructure tools native

to each cloud platform are typically designed

to operate within the connes of the specic

platform. Furthermore, adjacent services

offered by public cloud providers for data

management or analytics are usually designed

to operate alongside the native public cloud

infrastructure, and may not integrate or be

able to provide full functionality on other

public cloud platforms.”

ANDREW SMITH

RESEARCH MANAGER, IDC

Managing data in multicloud

environments

25% 36% 25% 13%

Managing data in hybrid-cloud

environments

24% 33% 28% 14%

Managing the data connections

between edge and core environments

22% 34% 30% 13%

Deciding what data to keep in what

environments (cloud, on-premise,

edge, etc.)

22% 34% 29% 14%

Getting buy-in to get the resources to

successfully manage data beyond

enterprise data centers

21% 34% 30% 14%

Building or nding the expertise

needed to successfully manage data

beyond enterprise data centers

22% 34% 30% 13%

ChallengingExtremely challenging Somewhat challenging

Not a challenge

Managing Data in More Than One Cloud

Over the Next Two Years

FIGURE 7

DATA BY IDC

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 23 |

According to IDC’s IaaSView

research

, the most common use

case for multicloud is to have an

application on one public cloud

IaaS that regularly interacts with

applications on another public

cloud IaaS. This arrangement may

work at the application level.

But at the data management level,

signicant challenges remain:

• separate workows

• disparate management tools

• lack of unied security

management

• the difculty of sharing and

moving large amounts of data

across multiple cloud providers

These challenges add complexity

and time to even the simplest

data-related tasks, like dashboard

creation and reporting in

multicloud environments.

Delving Deeper

Has applications on one public cloud IaaS

that regularly interacts with applications on

another public cloud IaaS

Has applications on one public cloud IaaS

that regularly interacts with applications on

dedicated infrastructure

Has applications that were ported

and/or migrated from one public cloud

IaaS to the other

Has a single set of provisioning,

management, and monitoring tools that

works across all providers

Has similar provisioning and approvals

process across most providers

Is disconnected, with different teams,

provisioning, approvals, management, and

applications for each provider

48%

46%

26%

25%

21%

15%

Level of Integration of Multiple Public Cloud IaaS Providers

FIGURE 8

ANALYSIS BY IDC

Source: IaaSView Survey 2019, IDC

1 IaaSView Survey, IDC, 2019

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 24 |

CHAPTER FOUR

So far, this report has

established that:

• Business data contains a great

deal of value.

• Much of this value goes

untapped, costing business

owners potential revenue.

• An impediment to deriving

optimum value from data lies in

businesses not collecting much

of their data.

But there are other reasons why

data management is a challenge:

issues inherent to the multicloud

ecosystem. This chapter sheds

light on these challenges.

Data Management

Challenges and the

Multicloud Ecosystem

Managing Data

The survey has found that when

it comes to managing one of their

most valuable resources—data—

enterprises report being unable to

successfully control the growing

complexities on their own. They

report that they need help with data

management.

A key reason for this is a wide and

relatively even distribution of data

across edge and cloud repositories.

There is some variation: the

transportation and electric vehicles

industry shows most data at the

edge, while the manufacturing

sector stores more data in internally

managed data centers.

DATA BY IDC

Internally managed

enterprise data centers

Third-party managed

enterprise data centers

2020

30%

Other locations

20%

Cloud repositories

(public, private, industry)

22%

Edge data centers

or remote locations where

data is stored

19%

Where Data Is Stored Currently

FIGURE 9

SEAGATE POV

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 25 |

When asked what was the most

important factor driving the changes

being made to the organization’s

approach to how it manages central

storage needs:

• 17% of respondents pointed to

improving data security

• 14% quoted increasing access

for data analytic and management

services (AI/ML, IoT, etc.)

• 14% noted increasing visibility

and manageability of IT

infrastructure operations

• 11% listed reducing cost/TCO of

infrastructure

• 10% mentioned providing faster

access to data for applications

and business units

Third-party managed

enterprise data centers

Internally managed

enterprise data centers

2020

2022

Edge data centers

or remote locations where data is stored

Cloud repositories

(public, private, industry)

Other locations

2022

2020

2022

2020

2022

2020

2022

2020

2022

2020

22%

25%

19%

30%

28%

20%

Where Data Is Stored Now vs. Two Years From Now

FIGURE 10

Driving Changes in

Data Management

Improving data security

17%

14%

11%

10%

Increasing access for data analytic and

management services (AI/ML, IoT, etc.)

Increasing visibility and manageability of

IT infrastructure operations

Reducing cost/TCO of infrastructure

Providing faster access to data for

applications and business units

Meeting increases in data

capacity requirements

Improving ease of use

Improving availability and

increasing uptime

Integrating third-party services to use

alongside modern infrastructure

Driving Factors in How Data Is Stored

FIGURE 11

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 26 |

Before business owners

implement the steps to better

manage data, they need to

understand their own data.

Unfortunately, most organizations

have a tendency to collect and

dump data into large repositories.

But when they merely collect

tons of data, they will struggle

to make sense of it. If they don’t

understand their company’s data,

then they don’t know what data

they need to collect and what

intelligence they can get out of it.

Business leaders’ rst task is to

dene why they want to collect data

and what insights they are trying to

gain. Only once they are clear on

that agenda should they go after

that particular goal—as opposed to

the kitchen sink approach.

Collecting data is easy.

Intelligence is hard.

To smartly collect and sort through

data, enterprises need to deal with

challenges of overlapping tools, data

complexity, data integration, ensuring

that data is “clean,” ensuring proper

data correlation, etc.

Collecting data has to be about

what enables the business objective

(what business owners want to

learn). Unless they gure this out,

amassing data won’t provide them

with the value they expect.

Take, for example, manufacturing.

Many industries adopted large

volumes of IoT devices and

platforms, endpoint devices with

sensors, as well as ML and AI

to manage their manufacturing

ecosystems. Data collection

happens at unprecedented

volumes. Business owners can

collect a great deal of data. But

without understanding what they

want out of their data, it can be

difcult to accomplish basic goals—

for example, to improve throughput

of manufacturings—if the factories

are ooded with tens of thousands

of uncoordinated IoT devices

deployed on different platforms.

Business Owners, Stop (Merely) Hoarding Data

Another way to achieve successful

data management is through

smart data storage solutions.

Challenges arise as a result of a

conuence of factors:

• Nonstandard architectures

• The proliferation and

coexistence of different storage

technologies

• Difculties managing the storage

technology footprint

• Lack of visibility, or the so-called

single pane of glass, of stored

data’s management (object and

le storage show up differently)

• Deciding which data goes where

• Prohibitive costs play a role in

decision-making about storage

solutions

• Inability to look at a multicloud

storage pool as a whole

between on-prem and cloud

A key solution to these data

storage management challenges

has to do with how business

owners see the stored data. The

idea is to see it—all of it—as if

through a single pane of glass. It

goes beyond data democratization

and into storage unication. CIOs

should be able to look across the

multiple cloud ecosystems in a

seamless manner.

Data Storage, Through a Single Pane of Glass

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 27 |

This challenge, meaningfully

shown by the shift of data to the

multicloud ecosystem, can be

solved by storing data in clouds

that allow enterprises control

over their data (on premises,

private clouds, or the rare public

clouds that do not come with

data lock-in).

Another solution on the horizon

is storage virtualization, which a

number of companies are working

to innovate. In this scenario,

the single pane of glass would

be a data storage management

software layer.

Bottom line for business owners:

ownership of your data starts with

an unobstructed, easy view into it—

as if through a single pane of glass.

Data Management

Challenges

Organizations face ve key

challenges that they believe limit

their ability to exploit the full

potential of collected data:

1. Making collected data usable

2. Managing the storage of

collected data

3. Ensuring that needed data is

collected

4. Ensuring the security of

collected data

5. Making the different silos of

collected data available

DATA BY IDC

These considerations are put

in the language of IT. But they

should matter to business owners

because they directly affect the

value of data that businesses can

uncover, which affects revenue (as

we’ll see in chapter 6.) Modern

data management solutions

should focus on resolving these

challenges to provide the most

effective experience possible

for both business owners and

customers, and begin to help

businesses chip away at the

percentage of data that they are

unable to exploit.

In the following chapter we learn

about the one reported missing

link of data management that can

help make this happen.

Making collected data usable

39%

37%

36%

35%

30%

28%

26%

25%

22%

Managing the storage

of collected data

Ensuring that needed

data is collected

Ensuring the security

of collected data

Making the different silos of

collected data available

Getting the required resources

to manage collected data

Having the technology in

place to analyze data

Establishing data management

governance and processes

Building the people resources

needed to analyze data

Connecting the curated

data with data users

Barriers to Putting Data to Work

FIGURE 12

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 28 |

In our survey data, the

manufacturing industry has

distinguished itself as a data

management laggard of sorts.

Perhaps counterintuitively, the

sector shows the lowest level

of task automation in data

management, and lowest rate for

full integration (single platform) of

data management functions.

• Manufacturing lags in both

multicloud and hybrid cloud

adoptions.

• Along with the telco and CDN/

media industries, it indicates

below-average satisfaction with

its data management approach.

• Along with telco, respondents

in manufacturing indicate

low satisfaction with data

management tools.

• Manufacturing’s greatest data

management challenge is storage

management.

Additionally, manufacturing

indicated one of the lowest rates

of data growth of any industry

(37% vs 42.2% on average).

However, the sector has the highest

enterprise data center footprint (on

premises) of any surveyed industry.

This may provide insight into why

manufacturing has a lower rate of

data growth: they are slowed down

by on-premises infrastructure that

is likely more difcult to expand

in terms of capacity (especially

when compared to scalable cloud

infrastructure).

An argument can also be made

that while manufacturing generates

a signicant amount of sensor and

device-related data, much of it is

produced at the edge and discarded,

rather than transferred to a core

environment for long-term storage.

Manufacturing also indicated the

lowest levels of automation of data

management functions—and the

lowest rate for full integration of data

management functions on a single

platform (just 9%, compared to 19%+

in all other industries). Both of these

data points may be driven by the

sheer number of connected assets

entering modern manufacturers.

As IDC’s Manufacturing Insights

2018 IT and OT Integration

Survey showed, nearly 80% of

instrumented production assets

are digitally connected in some

form. The question is why does

this disconnect between digital

assets and data management

exist within the manufacturing

industry? IDC research has identied

two important challenges to consider

alongside this survey analysis:

1. Manufacturing faces a major skills

gap. If skilled people represent

the ultimate opportunity for the

factory of the future in developed

economies, the lack of adequate

skills is one of the toughest

barriers that companies need to

address. They are dealing with

aging workforces and challenges in

nding new skilled employees willing

to work on the plant oor when it

comes to hard and soft IT skills.

2. Most manufacturing plants are

lucky if they can connect to half

of their assets on the oor. This

isn’t just about applications, data

centers, and networks; it’s also

about core enterprise architecture

and infrastructure decisions

such as IT/OT integration and

security. In many cases, legacy

infrastructure simply won’t be

able to keep up with the amount

of connected assets entering the

plant. As a result, many plants

may implement ad hoc processes

to connect and manage assets

without being able to rely on

underlying infrastructure for

comprehensive management.

Manufacturing: An Outlier

ANALYSIS BY IDC

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 29 |

CHAPTER FIVE

In the age when the volume of data

is proliferating at unprecedented

speed, how do enterprises

manage all this data in a way that

taps its value and satises both

shareholders and customers?

The survey identied a key solution

to this data management quandary:

DataOps. Throughout the global

business landscape, DataOps

looms as the missing link of data

management—and the solution to

business owners’ data headaches.

DataOps, dened by IDC as the

discipline connecting data

creators with data consumers,

should be part of every successful

data management strategy.

DataOps is part of data

management. In addition to

DataOps, data management

includes data orchestration

from endpoints to core, data

architecture, and data security.

The goal of data management is

to facilitate a holistic view of data

and enable users to access and

derive optimal value from it—both

data in motion and at rest.

Given what we’ve learned in the preceding

sections, a conundrum emerges.

DataOps: The Missing Link

of Data Management

DataOps

Across regions and industries,

only an average of 10% of

organizations report having

implemented DataOps fully across

the organization. The opportunity

is there for the taking.

DataOps is neither a technology

nor a process, but rather an

emerging discipline of connecting

data consumers with data creators

to enable collaboration and

accelerate innovation.

DataOps capacity

has been fully implemented

across the organization

DataOps capacity

has been partially

implemented

Have started to build

DataOps capacity

Considered and

planning to build

DataOps capacity

Not considered this

separately at all

Tota l

33% 30%7% 21% 10%

Transportation/EV 39% 28%7% 17% 9%

Telecommunications

31% 35%8% 17% 9%

Media

28% 32%9% 18% 12%

Manufacturing

39% 32%5% 19% 5%

Other

32% 27%6% 25% 10%

The State of DataOps

FIGURE 13

DATA BY IDC

SEAGATE POV

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 30 |

Data consumers are people

within business units who

are responsible for driving

the organizational decision-

making—whether toward product

development, product distribution

and marketing, cost control,

operations, etc. Common data

consumers are general managers,

VPs, CXOs, and the people who

support them. Data consumers

really don’t need data; instead,

they need actionable information.

Data creators can be both

machines—such as endpoint

and IoT devices—and people

who generate the reports and

information that is fed to decision-

makers. The challenge for data

creators is often to determine

what data must be collected for

prompt activation and what should

be collected for retention. For

example, the health of a device

(e.g., whether it’s running) may

not necessarily require immediate

processing and can be moved to

long-term archiving. However, data

about the device’s operations (e.g.,

temperature, capacity, speed, etc.)

may need immediate analysis or

coordination. This sort of data can

lead to better predictive analytics,

event correlations, and so on.

DataOps can exploit technology,

in particular AI and ML, to assist

in correlating data from core,

cloud, and edge data sources.

DataOps also utilizes ELT-like data

ingest functionality (Extract. Load.

Transform.) to pull data from multiple

sources and load into a common

structure, frequently as a data lake.

AI can be a key for transforming

data into the information needed by

decision makers.

Being able to correlate data from

disparate sources is a capability

not easily available through other

means. Because it is difcult,

those organizations able to master

it can expect to have an edge over

the competition.

The survey revealed that most

organizations have multiple tools

to perform a similar function,

which makes enterprise data

management a challenge.

In fact, only about one third of

organizations reported having

a single solution for a single

function. This may be caused

by a variety of reasons: different

buyers within the organization,

incompatibility of solutions across

platforms, or simply historical

evolution of the systems.

Back up/Recover

Container orchestration

Policy management

Data discover

Data classication

Metadata management

Recovery orchestration

Data security for data being stored

Data security for data on the move

Data migration, tiering, or placement

34% 47% 16% 3%

33% 41% 20% 6%

38% 41% 18% 3%

33% 46% 19% 3%

33% 47% 17% 3%

33% 44% 19% 4%

32% 45% 18% 5%

34% 45% 18% 3%

34% 46% 17% 3%

Not sure

Apps deployed in

some locations

or for some

applications

Multiple apps

deployed enterprise-

wide in all locations

Single app deployed

enterprise-wide

in all locations

Approach Used to Deploy Tools or Applications for

Data Management Functions

FIGURE 14

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 31 |

While it may be necessary to have

point products at a system level,

it is essential to have a unied

data management capability.

DataOps is the practice of bringing

disparate data systems into an

understandable entity. The core

functionality needed for DataOps

is metadata management,

data classication and policy

management. As data is ingested,

the metadata management function

allows data to be associated

and managed based on its

characteristics. This metadata,

along with data classication

capabilities, can identify specic

types of data, such as personally

identiable information (PII) and data

governed by the Health Insurance

and Portability and Accountability

Act (HIPAA). Once data is classied,

AI algorithms can be developed to

automatically recognize data and

make associations.

DataOps is particularly well suited

to the iterative learning approach

required by AI-driven applications.

This approach is the reverse

of the traditional data analytics

approach. Traditional analytics

takes a problem and searches

for an answer; DataOps makes

data associations and searches

for insights. For example, data

may reveal that consumers make

seemingly unrelated product

purchases together, leading to

better merchandizing or product

placement; or it may spot trends

within certain demographic groups

leading to focused micromarketing.

It is not always technology only

that stands in the way of effective

DataOps. Business owners need

to be aware of culture and people

challenges.

When organizations operate

in silos, too often different

competing groups work toward

their own objectives. They want

to gain and keep control of data.

The mindset is, if they lose control

of the data, they lose power,

because data is power.

As a result, data is stored,

managed, and analyzed in silos.

If various groups within an

organization access the same raw

company data and each group

does analysis, their reports do

not often match because they

don’t avail themselves of a global

repository of data.

The solution to this people problem

needs to start with the business

owner’s strategy. That strategy

needs to institute global standards,

global data architecture, global

data management, and the same

access to the same analytical tools

by global teams.

Rolling back reporting functions

to IT can provide global tools,

capabilities, and solutions

that every group can leverage.

The various groups within the

enterprise should get out of siloed

management of their own data,

and allow the IT-instituted tools to

do that globally.

In doing so, the teams will be

freed to make decisions based

on insights from reliable, global,

accessible pools of data.

DataOps: The Human Element

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 32 |

• Majority of respondents say

that DataOps is “very” or

“extremely” important

• DataOps is seen as most

important in North America and

China (see Section II for the

regional analysis)

• While DataOps is considered

important across all industries,

transportation shows a slightly

higher need for it than other

industries

• While this survey was done

prior to the COVID-19

pandemic, DataOps is going

to be even more needed as

more people continue to rely on

working from home, which has

already accelerated migration to

cloud services.

Business leaders all over the globe agree with the need for DataOps.

Not very

important

Somewhat

important

ImportantVery

important

Extremely

important

Total

China

Europe

APJ

42% 24%23% 10% 1%

40% 17%34% 9% 1%

52% 15%29%

44% 28%15% 11% 3%

37% 29%21% 12% 1%

Importance of DataOps

FIGURE 15

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 33 |

CHAPTER SIX

The ndings make clear that in

large part the solution to data

management puzzles can be

found in implementing DataOps—

the discipline that connects data

consumers with data creators,

and the processes that go with

it. How do we know DataOps is

useful? The survey results point

to a signicant result.

Along with other data

management solutions—

analytics-enabled data

orchestration and well-functioning

data architecture—DataOps

leads to measurably better

business outcomes. Among them:

improved customer loyalty and

satisfaction, better prots, higher

revenues, and greater employee

retention and productivity.

DataOps therefore has a

demonstrated bearing on an

enterprise’s competitive edge.

At last, we come to the truly good news that the

survey delivers to business owners.

Better Business Outcomes

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 34 |

A Competitive Edge

Efcient DataOps is the foundation

for rapidly building and training AI

models and for deploying analytics

at scale. These advanced

analytical results can lead to

competitive advantage.

The survey found that better data

analysis leads to measurably better

business outcomes.

The following chart points out

the areas of improvement from

organizations that cited having

improved data analytics. Note that

speed of improvement makes a

difference, since most organizations

can say they improved over time.

The companies that improve fastest

can expect better business results

compared to peers. Though the

results in Figure 16 do not reect the

effect of DataOps alone, DataOps is

the latest and leading-edge method

for improving data management.

For most organizations, the trifecta

of better business outcomes is the

following:

1. Better revenue.

2. Better prot.

3. Better customer satisfaction/loyalty.

But better results due to data

management and analytics were

seen across the enterprise and

also included:

• Better employee productivity.

• Better employee retention.

• Lower costs.

• Improved regulatory compliance.

• Better new customer acquisition.

Among the more proactive

results is better new customer

acquisition, which obviously is the

key to growing revenue.

DATA BY IDC

Employee productivity

Revenue

Prot

Customer satisfaction/loyalty

Customer retention

Employee retention

Adherence to regulatory compliance requirement

Operational costs

Shorter time to market for new products and services

New customer acquisition

Number of new products and services offerings

Reduction in CapEx requirements

73%

72%

71%

70%

69%

68%

66%

65%

61%

57%

Indicators Used to Measure the Success of Investments in

Data Management and Analytics in Improving Performance

of the Organization

FIGURE 16

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 35 |

DataOps: Getting to Customer Satisfaction and Prot Through Data

This survey established that a

smooth-functioning DataOps is key

to data management that enables

organizations to get more value

out of data and boosts business

outcomes such as prot and

customer satisfaction.

How can businesses get there?

As noted in the preceding section,

the human element of the equation

cannot be overstated. It’s the

people who keep data in silos.

Consequently, the way to institute

effective DataOps is not just about

having the right tools. To be sure,

the right tools are key. Virtualization

tools are immensely useful, if not

necessary, in that they allow for the

retrieval and manipulation of data.

Whether it’s a software virtualization

plane (such as Kubernetes, “a

portable, extensible, open-source

platform for managing containerized

workloads and services, that

facilitates both declarative

conguration and automation”)

or virtual machines that are an

abstraction mechanism for app

deployment, virtualization layers can

streamline data management.

But before DataOps avails itself

of virtualization, it has to start with

decisions about data. Putting tools to

work is the easy part. It’s trickier for

enterprise leaders to make decisions

about data via data governance and

processes around it.

Business owners need to start

with their goals—which commonly

include boosting customer

satisfaction and prot. To get

there, they need to interrogate

the data at their disposal.

They need to coordinate data

from various sources and solve

governance issues such as:

• Who has access to what data?

• How to classify data?

• Which data to keep and where?

• What to do with data after it’s

analyzed?

• How to make data available?

• How to interconnect data?

In order to make these

determinations, business owners,

data administrators, or CIOs

need to consult subject matter

experts (SMEs). Along with

SMEs, they need to identify,

evaluate, cleanse (detect and

correct inaccurate records),

and validate data. SMEs need

to be at the table because only

they possess deep knowledge of

certain types of data.

It’s with the involvement of the

SMEs, under the direction of

business owners, and facilitated

by data administrators that

determinations need to be made

regarding a key question: What

intelligence do you want out of

what data? What do you want it to

tell you? How do you want to use it?

There can be about 10,000 entry

point parameters collected for a

single product that a company

manufactures. If the company is to

retain all these points of information

for just one product, without a

clear data architecture dening

where it will all be stored and how

it will ow between environments,

the data risks drowning in a data

swamp. The decision-makers

need to confer with product design

engineers and quality engineers,

and ask: Which of the 10,000

parameters are the most critical?

Then, through interrogating and

tracking the curated data, they are

on a more efcient path to building

components and solutions.

The upfront work of data

governance—involving

coordination, discussion, analysis,

agreements around language,

and data classication (more on

this in the following chapter)—can

give way to the mostly-automated

downstream processes facilitated

by virtualization tools.

This twofold process of DataOps

can then reap the results of

customer satisfaction because

optimizing the governance and

ow of data leads to better quality

of offerings, which directly affects

how customers feel about their

purchases. As IDC analysts pointed

out in the previous chapter, speed

of delivery matters too. Getting

to results faster means greater

customer satisfaction—because

getting to data faster means

customers and business owners

can make decisions faster.

This is how a process that starts

with business owners setting out

to increase customer satisfaction

and prot attains these very goals

through optimizing data via DataOps.

It’s about being intentional on

making the most of data.

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 36 |

CHAPTER SEVEN

In addition to DataOps, a unique part of data

management that deserves a special examination

is data security.

Data Security and Data

Management

Two thirds of survey respondents

report insufcient data security,

making data security an essential

element of any discussion of

efcient data management.

Data security is consistently rated

among the highest concerns and

priorities of IT leaders and business

leaders alike. Data breaches lead

to direct nancial losses, signicant

regulatory nes, reputational

loss and embarrassment, lost

customers, and more. Malware

attacks can result in the theft of

corporate secrets, lost employee

productivity, unrecoverable data,

and, in the case of ransomware,

nancial loss and embarrassment.

However, many organizations

surveyed have not implemented

common data security practices

enterprise-wide (Figure 17).

The results in Figure 17 do

not represent an all-or-nothing

approach to enterprise security.

That is, a respondent might answer

“yes” to encryption of data at rest,

but “no” to all others, for example.

Thus, it is highly probable that

the vast majority of organizations

have at least some signicant

vulnerabilities in their environments.

DATA BY IDC

Encryption of data in ight

Physical security of

data storage facilities

Employee training for handling

sensitive information

Encryption of data at rest

Masking of data

Location and movement

restrictions on data

36%

34%

32%

Percentage of Organizations Fully

Implementing Key Security Practices

FIGURE 17

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 37 |

The biggest myth about enterprise

data security is that the technology

is the hard part; it’s not.

The challenge is getting alignment

on classication of data by risk

and on how data should be stored

and protected. That, as in the case

of DataOps, is the human element.

Technology boils down to

business owners’ purchasing

decisions. You want to protect

data at rest? Buy self-encrypting

drives; problem solved.

Educating data users—those

within the company who

create, touch, analyze, and

move the data—is a different

matter. Business leaders should

remember that the more data

creators and consumers within

their company are educated, the

better for the business. Business

owners’ own education about

data security is key to success,

because it ensures their buy-in.

The education about data security

should be democratic. Protecting

data (and the education that

comes with it) ought to be built

into other stacks. It cannot merely

be the domain of CISOs, CIOs,

IT admins, security, and the legal

teams. Data owners, too, should

be at the table.

Bottom line: If you’re a business

leader, don’t wait for a data breach

or data loss to grasp the reality

that data security is foundational

to the value your business can get

out of data.

Data Security: A People Issue

1. Data Classication. A daunting task that requires a lot of cross-

organization communication, classifying data is imperative. Data

creators, owners, and users within the organization must align on data

classication standards, and map data types to those classications.

Without this step of dening and aligning, any data protection program

will fail. Keep it simple.

For example, at Seagate Technology, team members aligned

to broader standards. As a result, the team arrived at four data

classications: restricted, condential, internal, and public.

2. Data Flows. Business owners must understand where the company’s

data is owing—either by design/intent or against design/intent.

Determining what is right or valid is an important step of dening controls.

Understanding data ows will help identify the biggest areas of risk.

3. Access Control. Role-Based Access Control (RBAC) may be the

most basic form of access control, but it is rarely comprehensive.

The idea that only those who need access should have access is

easy to grasp, but the discipline to implement and maintain is harder.

The higher the sensitivity of the data (per data classication), the

stricter the implementation. Beyond RBAC, other controls that can be

considered include:

• Information Rights Management (IRM) controls at the le-level

encryption, using granular access controls (restricting functions

like print, edit, copy/paste per given asset)

• Other access mechanisms like browser-only access (no

download) and watermarking to avoid screenshots/capture

Key Steps of Data Protection

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 38 |

A well-implemented data security program can seem intimidating. And

it’s a good idea to not let the perfect be the enemy of the good. What

if you’re an overwhelmed business owner who can’t quite do it all right

now, but wants to rst take at least the most necessary steps, and take

care of the less-urgent protections later?

Here are the must-haves of data security that protect against the

greatest risks:

1. Encrypt data in ight. At a minimum, only use secure protocols and

services—HTTPS over HTTP, SFTP over FTP, IPSec, or SSL VPN for

all remote access. This is not hard: Only leverage tools that use secure

communication, congure them correctly, make it the standard, and

enforce compliance.

2. Encrypt data at rest. Laptops and mobile devices are the biggest risk

to data. Your storage array or server is generally in access-controlled

data centers; there’s little risk of it being stolen from a car or falling

out of a pocket in a cab. Address the biggest risks rst. Leverage

enterprise-managed full-disk encryption for laptops. Enforce le-system

encryption for mobile devices in mobile device management policies.

3. Educate users. As this report notes throughout, users will either be

your strongest or weakest control. Business owners need to ensure all

employees are educated on best practices and risk. Stop. Think. Protect.

Basics of Data Security

SEAGATE POV

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 39 |

SECTION TWO

Regional

Findings

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 40 |

CHAPTER ONE

APJ respondents included those based in

Australia, Japan, India, South Korea, and Taiwan.

For the purposes of this survey, China is treated as

its own region and not included in this category.

Asia Pacic and Japan (APJ)

To take a region like APJ and

characterize it as an entity is

increasingly difcult. Technological

advancement is not evenly

distributed across this diverse

region. With that said, the survey

includes ve of the region’s most

technologically developed countries.

Even given this sample, it is fair

to conclude that the region’s

integration of data management

functions is still developing.

The following survey ndings

distinguish the region from the others:

• APJ trails North America (NA) and

Europe with 34% of organizations

reporting having a single

enterprise-wide application for

policy management (Figure 1).

• 87% of respondents say that

DataOps is either “extremely

DATA BY IDC

Back-up/recovery

China NA EuropeAPJ

33%

40%

36%

32%

China NA EuropeAPJ

29%

35%

39%

31%

China NA EuropeAPJ

34%

36%

45%

38%

China NA EuropeAPJ

30%

33%

38%

31%

China NA EuropeAPJ

27%

36% 36% 36%

China NA EuropeAPJ

30%

34%

36%

34%

China NA EuropeAPJ

29%

28%

35%

China NA EuropeAPJ

28%

34%

38%

37%

China NA EuropeAPJ

28%

39%

38%

35%

China NA EuropeAPJ

30% 27%

41%

34%

Container orchestration Policy management Data discovery Data classication

Metadata management Recovery orchestration Data security for data

being stored

Data security for data

on the move

Data migration, tiering,

or placement

Proportion of Respondents Reporting Their Organizations

Have a Single Enterprise-Wide App for Policy Management

FIGURE 1

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 41 |

important,” “very important,” or

“important” (Figure 2).

• APJ sees the increase of data

growth in the next two years as

a result of more widespread use

of advanced data analytics and

the IoT devices to automatically

gather data. Unlike their

counterparts in other regions,

companies in APJ do not expect

as much of this growth to

happen as a result of the move

to the cloud (Figure 3).

• APJ organizations are least

daunted by the challenges to

managing data that they see in the

coming couple of years (Figure 4).

• APJ’s enterprise data does not

appear to be very dynamic. It

does not move as much as data

does in other regions. Still, within

the next two years, 7% of APJ’s

enterprise data is expected to

move to the cloud (Figure 5).

Not very

important

Somewhat

important

Important

Very

important

Extremely

important

NA China Europe APJ

40%

17%

34%

52%

15%

29%

44%

28%

15%

11%

3%0%

37%

29%

21%

12%

Increased use of advanced

data analytics

Increased use of IoT devices to

automatically gather data

Migrating on-premise

applications to the cloud

Increased use of SaaS

applications

Increase in edge application

development

Modernizing legacy applications

Increase in on-premise

app deployments

Changing operating systems

(OS) environments

ChinaAPJ

54%

51%

44%

37%

45%

36%

40%

32%

38%

26%

29%

32%

33%

17%

Importance of DataOps

FIGURE 2

Factors Impacting Data Growth

FIGURE 3

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 42 |

There are no region-wide laws or

regulations in APJ that impede the

movement of data across borders—

unlike, for example, Europe’s

General Data Protection Regulation

(GDPR). Data sovereignty is

less of an issue, or at least it is

treated differently. A unied policy

mechanism is the most efcient,

automated way to manage data

consistently at scale.

The fact that it is reported by

only a quarter of the respondents

means that most of the region’s

enterprises struggle with unifying

data management functions.

One potential factor impeding the

movement of data in APJ may

be the variety of languages and

laws that make the region very

heterogenous, with signicant

translation and compliance factors.

When looking at the proportion of

respondents indicating “extremely

challenging” or “challenging” to the

following data management issues

(Figure 4), APJ is consistently lower

than the overall survey average. Why

is it that APJ respondents appear less

pressured by the challenges related to

data management? Our hypothesis is

that data management maturity may

play a role here.

Compared to other regions, APJ

indicated the lowest average levels of

satisfaction with their organizations’

general approach to data

management. APJ also indicated the

lowest average levels of satisfaction

with data management tools. These

low levels of satisfaction with data

management operations and tools

may be indicative of earlier-stage

maturity of tools and processes in

the region. If APJ respondents are

not satised with their operations and

tools, it seems likely that there would

be a lower proportion of organizations

in the region pursuing advanced data

management tasks (e.g., managing

data in multi- and hybrid-cloud

environments). APJ organizations

may simply still be working to improve

the operational and technological

aspects of their data management

strategy, before moving on to more

advanced tasks.

With that said, APJ (tied with China)

leads the way when it comes to data

analytics and IoT being the top factor

propelling the growth of data (Figure

3). This could be because of the

growing use of devices by various

manufacturers, such as the OEM

suppliers in the region.

What It Means

ANALYSIS BY IDC

Although enterprise data

management is not as integrated in

APJ as in other regions, business

leaders should make sure it is agile

to better cope with future needs

such as a global pandemic where

the digital behavior completely shifts.

There is a need for an enterprise

data management to 1) encourage

business owners to classify data

properly and gure out what

goals it should accomplish; 2) put

analytics at the heart of decision-

making; and 3) prepare the

workforce for a data-driven future.

Room for Growth—and Education

SEAGATE POV

57.5%

78%

China

54.8%

Europe

51.7%

APJ

Proportion of Regions Expecting

Extreme Challenges or Challenges in

Managing Data in the Next Two Years

FIGURE 4

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 43 |

As IDC points out, a unied policy

mechanism is the most efcient and

automated way to manage data

consistently at scale. Why are both

a unied policy and DataOps not as

prioritized in APJ as in other regions?

First, except for privacy

protections, many APJ

governments historically have not

interfered with private companies’

management of their data.

Second, the companies in the

majority of the region’s countries

still need to raise their awareness

around optimizing the value of

data through company-wide data

policies. Business leaders need to

become familiar with the benets of

unied data policies and DataOps—

and they need to institute them.

Third, a company’s IT infrastructure

needs to be customized to support

the use of AI and, in particular,

the edge devices that enable

real-time data gathering, analysis,

and use. It’s important to have a

robust infrastructure that includes

hardware and software platforms

that gather and manage huge

amounts of data from automation.

Business owners should work

closely with their IT departments

to secure the most appropriate

platforms and ensure they’re

located where they need to be.

For many, that means having edge

devices close to factory equipment,

rather than offsite.

Fourth, employees must be

retrained and reskilled to be

effective. It’s about enabling data

agility: organizations have to

build a talent base that’s adept at

using big data to make decisions,

whether sourcing new talent with

the right skills or retraining and

reskilling employees. The result

would be prepared employees

who thrive in a data-driven future.

Fifth, organizations should

consider longer-term investments

to get ready for the future. As the

COVID-19 pandemic has shown

us, readiness for the long term

matters a great deal.

In sum, APJ businesses are still

growing in their understanding

of how data management can

boost the value of data. The

region appears to be slightly more

conservative, for example, when

it comes to putting data in the

cloud or changing data locations.

The region’s greatest catalyst for

change is improving data security.

There’s a reluctance—whether

associated with means or with

lack of data education—to get out

of a comfort zone when it comes

to data processes: APJ has the

opportunity to invest in DataOps

in order to maximize the value of

its growing data.

In the aftermath of the onset of a

global pandemic that resulted in

many more employees working

from home, it is even more critical

for enterprises to take data

management more seriously.

NA China

Europe

APJ

Internally managed

enterprise data

centers

Third-party managed

enterprise data

centers

Edge data centers

or remote locations

where data is

centrally stored

Cloud repositories

(public, private,

industry)

Other locations

-5%

-1%

-12%

20%

-10%

-13%

-3%

-9%

14%

-4%0%-1%

-10%

10%

Expected Changes in How Data Is Stored—

Over the Next Two Years

FIGURE 5

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 44 |

CHAPTER TWO

For the purposes of this survey, China is a stand-

alone region. This makes sense because of its

expansiveness, homogeneity, and geopolitical

uniqueness.

China

The survey shows that China

is the most progressive region

when it comes to taking on data

management challenges.

• Of all regions, DataOps is most

likely to be seen as “very” or

“extremely” important in China.

When asked how important they

felt the concept of DataOps

was, 52% of respondents said

“very important” and 29% said

“extremely important”—a total of

81% (Figure 6).

• When it comes to factors

inuencing data growth, China

is most aggressive about data

analytics (54% of respondents,

tied with APJ) and least focused

on modernizing legacy apps

(Figure 7).

• In the coming two years,

respondents project that 20%

more enterprise data will move

to cloud repositories (by far the

biggest percentage among the

regions) and 9% more will migrate

to edge data centers (Figure 8).

• Enterprise data management is

more centralized in China than in

other regions. A high percentage

DATA BY IDC

Not very

important

Somewhat

important

Important

Very

important

Extremely

important

China

Europe

APJ

40% 17%34% 9% 1%

52% 15%29%

44% 28%15% 11% 3%

37% 29%21% 12% 1%

Importance of DataOps

FIGURE 6

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 45 |

(67%) of enterprises in China use

just one dedicated centralized

group as a data management

function (Figure 9).

• China is behind other regions

when it comes to integration of

data management—but it is most

interested in integration (Figure 10).

• China reports the highest degree

of challenges in exploiting data’s

full potential—including making

collected data usable, managing

the storage of collected data,

ensuring data’s security, and

making different silos of collected

data available (Figure 11).

Managed in an

ad-hoc way

Managed separately

for each application

or infrastructure type

Organized by

business unit/

subsidiary

One dedicated

centralized group

across the enterprise

China

Europe

APJ

67%

44%

43%

40%

37%

41%

35%

18%

11%

23%

22%

Increased use of advanced

data analytics

Increased use of IoT devices to

automatically gather data

Migrating on-premise

applications to the cloud

Increased use of SaaS

applications

Increase in edge application

development

Modernizing legacy applications

Increase in on-premise

app deployments

Changing operating systems

(OS) environments

China

APJ

54%

51%

44%

37%

45%

36%

40%

32%

38%

26%

29%

32%

17%

32%

33%

Factors Impacting Data Growth

FIGURE 7

Internally managed

enterprise data

centers

Third-party managed

enterprise data

centers

Edge data centers

or remote locations

where data is

centrally stored

Cloud repositories

(public, private,

industry)

Other locations

-5%

-12%

20%

-10%

Expected Changes in How Data Is

Stored Over the Next Two Years

FIGURE 8

Source: The Seagate Rethink Data Survey, IDC, 2020

How Data Management Functions

Are Organized

FIGURE 9

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 46 |

Chinese organizations’ data

management capabilities are

growing fast. Its enterprises

understand the power of DataOps.

Centralized data management may

improve the ability to exploit data by

reducing silos. While centralization

of policy management is important,

integrating data management

functions is the next level of data use.

China doesn’t perform as well in

integrating data, but respondents

see data integration as a key goal in

the next two years.

Several large cloud providers are

based in China, so the local market

is ready for cloud adoption. This

may be why the region reports the

highest levels of migration to the

cloud (public, industry, and private).

A lot of this data is generated by

the proliferation of IoT devices,

sensors, and AI algorithms.

Growing adoption of public cloud

Iaas resources offered by regional

providers is also driving the move

to the cloud.

China is most driven to increase data

access and analytics and least driven

to reduce costs. Because of the state

of China’s IT maturity, we believe

the region has fewer legacy apps to

burden its competitive efforts.

What It Means

ANALYSIS BY IDC

Fully integrated

on one platform

Mostly

integrated

Majority are

integrated

Some are

integrated

None are

integrated

Integrated Data Management

China NA Europe APJ

Interest in Integrated Data Management

10%

45%

28%

15%

25%

41%

26%

32%

39%

18%

10%

39%

33%

15%

China NA Europe APJ

10%

35%

25%

5%5%

23%

39%

32%

30%

37%

22%

10%

33%

35%

18%

Fully integrated

on one platform

Mostly

integrated

Majority are

integrated

Some are

integrated

None are

integrated

FIGURE 5

Integration of Data Management—Current vs. in Two Years

FIGURE 10

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 47 |

In China, data is on the move—from

on premises to cloud and the edge.

China is in an invest-to-grow

phase of economy, which is true

for a lot of Chinese companies.

When enterprises are in this

mode, they have less focus on

prot and loss (P&L). They instead

focus on prot for the long term.

This means they choose to invest

in growth so they can eventually

see prot at scale.

Chinese enterprises do not have

to concern themselves with

updating legacy applications—

apps developed and architected in

the client server era, typically on-

prem apps that are single-tenant.

(Examples include hardware in

power plants, manufacturing

machines controlled by computers

running MS-DOS, or outdated

nancial systems.) This means that

their budget is freed up to invest in

data movement (to the cloud and

edge) and to exploit the value of

data with streamlined DataOps.

Chinese respondents report the

highest degree of difculty when

it comes to taking advantage

of data: this may simply be a

reection of Chinese businesses’

ambitions and their intense

focus on growth, plus the reality

that they nd themselves in the

trenches of DataOps adoption.

It may be that China lags most

“behind” when it comes to integrating

data management functions precisely

because of its centralization—and

because there are no legacy apps to

worry about integrating. China is a

growing economy strongly driven

by government and legislation,

as opposed to many Western

economies.

AI is a huge initiative in China,

partly because of the massive

volumes of data generated by the

enormous population of around 1.4

billion. AI is touted as one of the

leading initiatives that China uses

to compete on a global scale. The

country’s government supports the

development of AI, 5G, hyperscale

data centers, and other means of

accelerating data on the move.

As a result of these factors,

China’s infrastructure is a lot more

pliable. China has implemented

its New Infrastructure project

to boost the digital economy.

Combined with technology, this

initiative could also translate into

new business opportunities for

enterprise data management.

Data on the Move

SEAGATE POV

Making collected

data usable

China NA Europe APJ

Managing the storage

of collected data

Ensuring that needed

data is collected

Ensuring the security of

collected data

Making the different silos of

collected data available

Getting the required

resources to manage

collected data

Having the technology in

place to analyze data

Establishing data

management governance

and processes

Building the people resources

needed to analyze data

Connecting the curated

data with data users

37%

41%

39%

38%

China NA Europe APJ

39% 39%

35% 35%

China NA Europe APJ

39%

37%

38%

33%

China NA Europe APJ

39%

38%

33%

35%

China NA Europe APJ

41%

32%

24%

31%

China NA Europe APJ

37%

30%

26%

27%

China NA Europe APJ

27%

24%

23%

29%

China NA Europe APJ

29% 29%

22%

24%

China NA Europe APJ

19%

23%

22%

21%

China NA Europe APJ

11%

6% 6%

Source: The Seagate Rethink Data Survey, IDC, 2020

Main Challenges in Exploiting Data’s Potential

FIGURE 11

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 48 |

An important note while examining

the European data is that the region

tends to respond conservatively

versus other regions in nearly all

global surveys across a variety of

subjects. The region’s surveyed

countries include three Western

European powers and Russia.

• Europe has the least-implemented

DataOps discipline of all regions.

Only 18% of the region’s

respondents report that its

DataOps capacity has been fully or

partially implemented (Figure 12).

• But European companies

understand the need for DataOps.

As many as 86% of respondents

say that DataOps is “extremely

important,” “very important,” or

“important” (Figure 13).

• The region’s data growth rate

is also the lowest compared to

other regions (Figure 14).

DATA BY IDC

CHAPTER THREE

The survey queried enterprises from four

European countries: the United Kingdom, France,

Germany, and Russia.

Europe

Have started to build DataOps capacity

30%

13%

DataOps capacity has been

partially implemented

40%

Considered and planning to

build DataOps capacity

12%

Not considered this

separately at all

DataOps capacity has been fully

implemented across the organization

Europe

Source: The Seagate Rethink Data Survey, IDC, 2020

State of DataOps in the Organization

FIGURE 12

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 49 |

• European organizations are

moving data to the cloud. Over

the next two years, 14% of

enterprise data is going to shift

to the cloud (Figure 15). Factors

impacting data growth include

migrating on-prem apps to the

cloud and increase in on-prem

app deployments (Figure 16).

• When it comes to factors deciding

how data is stored, Europe is

driven to increase security and

reduce costs when determining

its data storage (Figure 17).

52%

37%

40%

44%

15%

29% 12% 1%

17% 1%

28% 3%11%

21%

29%

34%

15%

Not very importantSomewhat importantImportant

Very importantExtremely important

Europe

China

APJ

Europe

avg.

China

APJ

38%

49%

43%

Less than 10%

10% to 19%

20% to 29%

30% to 39%

40% to 49%

50 to 59%

60 to 69%

70 to 79%

80 to 89%

90 to 99%

100% or

NA China

Europe

APJ

Internally managed

enterprise data

centers

Third-party managed

enterprise data

centers

Edge data centers

or remote locations

where data is

centrally stored

Cloud repositories

(public, private,

industry)

Other locations

-5%

-1%

-12%

20%

-10%

-13%

-3%

-9%

14%

-4%0%-1%

-10%

10%

Importance of DataOps

FIGURE 13

Expected Annual Data Growth Rate

FIGURE 14

Expected Changes in How Data Is Stored—Over the Next Two Years

FIGURE 15

The less time organizations

spend focused on legacy apps

(e.g., updating them), the more

resources they can put toward new

innovations. The reverse also holds.

Europe is focused on updating

existing applications and on on-

prem applications. On-prem

apps, because of their inherent

architecture and dependence

What It Means

ANALYSIS BY IDC

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 50 |

In addition to the more mature

region’s preponderance of legacy

apps, data sovereignty and data

privacy play a great role in Europe

because of the region’s stricter

regulatory system and geopolitical

diversity. There’s a burden to do

well by one’s investments—in

this case, investments made

in the existing, older structures

and apps. It’s a kind of path

dependency, as is the logic of

wanting to recoup already-incurred

costs rather than venturing out

into new, costlier arrangements.

The value placed on data privacy

outweighs the additional costs and

it may slow the migration of data

to the cloud, which nevertheless

remains strong at 14% projected

in the next two years.

A Mix of Legacy Apps and DataOps Readiness

SEAGATE POV

on their in-place local compute

and storage resources, generally

have less agility than cloud apps.

COVID-19 has brought this issue

to the fore, as organizations are

looking to use the cloud to quickly

adapt. Europe may not be as agile

in infrastructure as other regions.

This can hinder the movement of

data, and its value.

Pan-European organizations may

use common apps, but because

of GDPR and other regulations

they cannot share that data across

borders. Thus, regulations—more

than data management technology—

may inhibit their ability to fully leverage

data compared to, say, APJ-based

organizations. Organizations bound

by regulation will need to expend

more effort and be more creative to

exploit the value of the data available

to them. DataOps can provide an

avenue for this.

Because European companies need

to maintain legacy applications while

complying with local regulations, they

become quite TCO-conscious. Issues

and regulations related to GDPR, data

privacy, and the right to be forgotten

complicate data management—and

are all rather costly. Privacy protection

is often achieved through secure

data silos, which can stie data

management innovation.

There are different motivations

for moving to the cloud. One is

to increase organizational agility,

the other is to reduce the cost

of hosting applications or storing

data. One hypothesis is that

Europe’s organizations are more

driven by the latter.

Migrating on-premise

applications to the cloud

Europe NA China APJ

40%

39%

45%

37%

Increase in on-premise

application deployments

Europe NA China APJ

38%

32%

29%

26%

Two Factors Impacting Data Growth by Region

FIGURE 16

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 51 |

Europe’s enterprise data is growing

at 38%. This may be slightly slower

than other regions, but faster than

the predicted general data growth

for all sectors, which IDC had

previously put at 32%. Considering

the maturity and regulatory

conservatism regarding putting data

to work in Western Europe and the

relative newness of IT development

in some parts of Russia, the 38% is

quite impressive.

According to Seagate-sponsored

IDC report The EMEA Datasphere

nearly a third of the global

datasphere growth results from

growth of video surveillance,

signals from IoT devices,

metadata, and entertainment.

Whether it’s surveillance growth in

the United Kingdom and France,

manufacturing in Germany, or

growth in Russia’s mining industry,

data is playing a much more

critical role these days. In order

to make better use of this data,

the region’s organizations would

do well to avail themselves of the

power of DataOps.

The survey found that, in addition

to improving data security, TCO

is the main driver in decisions

regarding where the data should

be stored (Figure 17). This could

explain the migration to the cloud.

As European enterprises were

too focused on maintaining and

updating the legacy apps, they

could not invest signicantly in

innovation, because those apps

lack agility compared to cloud

ones. To quickly adapt to changing

business conditions, Europe’s data

infrastructure needs to be more

agile. Migration to the cloud could

also be the solution to quickly

and cost-effectively adapt to

new business environments and

challenges like the COVID-19 era.

Cloud migration can in many

cases boost business innovation,

making it more agile. But the

challenge that the European

companies now face is data

management. As they store data

in hybrid cloud and in multicloud,

efcient data management will be

a must in order to derive optimal

value from data.

This need could explain the nding

that 40% of Europe’s enterprises

are planning to build DataOps

capacity and 30% already are in

the process of building it. Europe

is conscious of the urgency to

implement DataOps in order to

get the most out of data.

Improving data security

Europe NA China APJ

Increasing access for data

analytic and management

services (AI/ML, IoT, etc.)

Increasing visibility and

manageability of IT

infrastructure operations

Reducing cost/TCO

of infrastructure

Providing faster access

to data for applications

and business units

Meeting increases in data

capacity requirements

Improving ease of use Improving availability and

increasing uptime

Integrating third-party

services to use alongside

modern infrastructure

Europe NA China APJ Europe NA China APJ Europe NA China APJ Europe NA China APJ

11%

10%

Europe NA China APJ Europe NA China APJ Europe NA China APJ Europe NA China APJ

16%

20%

15%

18%

11%

13%

23%

15%

11%

15%

13%

15%

12%

10%

12%

11%

13%

Factors Driving the Changes in How Data Is Stored

FIGURE 17

Source: The Seagate Rethink Data Survey, IDC, 2020

1 The EMEA Datasphere, IDC, 2019

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 52 |

CHAPTER FOUR

The survey queried enterprises in the United

States and Canada.

North America (NA)

NA is leading other regions when

it comes to the integration of data

management functions.

• 45% of NA organizations report

having a single, enterprise-

wide data policy application,

compared to 38% in Europe and

34% in APJ (Figure 18).

• NA is the region with the most

projected data growth. The

region’s enterprise datasphere

is set to grow at nearly 50%

(faster than the global average

of 42.2%) (Figure 19).

• NA has the most advanced

DataOps implementation.

The region has the greatest

number of respondents who say

DATA BY IDC

Back-up/recovery

EuropeChina

40%

36%

32%

China

35%

39%

Europe

31%

China

36%

45%

Europe

38%

China

33%

38%

Europe

31%

China

36%

Europe

36%

China

34%

36%

Europe

34%

China

28%

35%

Europe

35%

China

34%

38%

Europe

37%

China

39%

38%

Europe

35%

China

27%

41%

Europe

34%

Container orchestration Policy management Data discovery Data classication

Metadata management Recovery orchestration Data security for data

being stored

Data security for data

on the move

Data migration, tiering,

or placement

APJ

33%

APJ

29%

APJ

34%

APJ

30%

APJ

27%

APJ

30%

APJ

29%

APJ

28%

APJ

28%

APJ

30%

Source: The Seagate Rethink Data Survey, IDC, 2020

Proportion of Respondents Reporting Their Organizations

Have a Single Enterprise-Wide App for Policy Management

FIGURE 18

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 53 |

avg.

Tot a l

China

APJ

Europe 38%

49%

42.2%

43%

Less than 10%

10% to 19%

20% to 29%

30% to 39%

40% to 49%

50 to 59%

60 to 69%

70 to 79%

80 to 89%

90 to 99%

100% or more

that DataOps—the discipline

connecting data creators

with data consumers—has

been either fully or partially

implemented: a total of 45% of

respondents (Figure 20).

• When it comes to determining

how their data is stored, NA

enterprises are most driven by

improving data security (20%)

(Figure 21).

• NA organizations are farthest

along in getting the most value

out of their data (Figure 22).

• NA’s greatest challenges in

taking full advantage of data

are making collected data

usable, managing the storage

of collected data, ensuring the

security of collected data, and

ensuring that needed data is

collected (Figure 23).

DataOps capacity

has been fully implemented

across the organization

DataOps capacity

has been partially

implemented

Have started to build

DataOps capacity

Considered and

planning to build

DataOps capacity

Not considered this

separately at all

22% 31%3% 29% 16%

China

42% 25%3% 19% 11%

Europe

40% 30%12% 13% 5%

APJ

33% 30%6% 23% 9%

A unied policy mechanism is the

most efcient, automated way

to manage data consistently at

scale. Arguably, it is the only way

to manage data consistently, as

manual efforts are not realistic

for medium- or large-scale

organizations. Policy management

is foundational to data management

and to leveraging that data.

Based on this survey, it appears

that NA organizations are moving

most aggressively to implement

unied data policies throughout

organizations and are most adept at

using DataOps to derive maximum

value from data.

In general, an enterprise-wide data

management policy application (i.e.,

a way to manage policies across

the enterprise) indicates a higher

level of data management maturity.

Therefore, the organizations that

implement it are better equipped

to fully leverage the value of the

data available to them. Certainly,

having an enterprise-wide policy

application is not assurance of

competitive advantage, but it

What It Means

ANALYSIS BY IDC

Expected Annual Data Growth Rate

FIGURE 19

State of DataOps in the Organization

FIGURE 20

Source: The Seagate Rethink Data Survey, IDC, 2020

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 54 |

is a key indicator that would be

used in a benchmark to compare

organizations.

Organizations focused on reducing

costs may not be making all the

necessary investments needed to

keep up with organizations that are

focused on improving data access

and extracting value from data.

NA organizations appear to be

focused on data security, perhaps

for regulatory reasons and/or in

order to maintain IP protection.

In the next two years, enterprise

data is projected to grow at the

average annual data growth rate

of 42.2%. That’s a lot more than

32% the projected annual average

(for all kinds of data, not just

enterprise) previously projected by

IDC

. What sets the NA region

apart is that it is driving up

the 42.2% global rate with an

astonishing 49% of expected

annual data growth in NA over

the next two years.

We can safely conclude that the

NA enterprises are drivers of data

growth. The data growth in this

region is coming from an increased

use of IoT devices to automatically

gather data, increased use of

advanced data analytics, and

increased use of SaaS applications.

When it comes to how NA

businesses determine where to

store data, data security goals

outbid all others. Ascertaining

security risks is a key part of

smoothly functioning DataOps,

as concerns related to hackers,

industry espionage, intellectual

property, and geopolitical defense

A Data Growth Leader

SEAGATE POV

Improving data security

APJ

Increasing access for data

analytic and management

services (AI/ML, IoT, etc.)

Increasing visibility and

manageability of IT

infrastructure operations

Reducing cost/TCO

of infrastructure

Providing faster access

to data for applications

and business units

Meeting increases in data

capacity requirements

Improving ease of use Improving availability and

increasing uptime

Integrating third-party

services to use alongside

modern infrastructure

APJ APJ

11%

10%

Europe

16%

20%

China

15%

18%

Europe

11%

13%

China

23%

15%

11%

15%

13%

15%

Europe

15%

China

12%

10%

12%

11%

13%

APJEuropeNA China

FIGURE 4

1 Data Age 2025, sponsored by Seagate with data from Global DataSphere, IDC, May 2020

Source: The Seagate Rethink Data Survey, IDC, 2020

Factors Driving the Changes in How Data Is Stored

FIGURE 21

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 55 |

are on the minds of owners of

NA organizations. The drive to

protect business data also has

to do with something a lot more

mundane, but just as important:

business continuity. In the US,

there is a great deal of education

and publicity around these data

security issues as NA’s data is

protected at a company level;

there is no national rewall.

With many companies

implementing DataOps or at least

intending to, NA respondents

indicated that they are the

furthest along when it comes to

exploitation of data (Figure 22).

At the same time, they recognized

that the greatest challenges in

taking full advantage of data

included making collected data

usable, managing the storage

of collected data, ensuring the

security of collected data, and

ensuring that needed data is

collected (Figure 23).

Knowing the appropriate storage

environment for various data sets,

classifying data correctly based

on communication between data

creators and business owners,

setting goals for various types of

data, making sure that the needed

data is collected, securing data, as

well as converting collected data to

usable data—these are all concerns

that comprise DataOps.

Respondents from NA in particular

stood out from other regions by

noting their need to improve the

ease of data’s use (Figure 21). This

is useful self-awareness. While

the NA region is fairly advanced in

adopting DataOps and relatively well

educated on the need to implement

it, more education and practice of

the discipline are needed to better

take advantage of available data.

41%

39%

37%

38%

32%

30%

24%

29%

23%

Making collected

data usable

Europe APJChina

37%

39%

38%

Managing the storage

of collected data

Europe APJChina

39%

35% 35%

Ensuring that needed

data is collected

Europe APJChina

39%

38%

33%

Ensuring the security of

collected data

Europe APJChina

39%

33%

35%

Making the different silos of

collected data available

Europe APJChina

41%

24%

31%

Getting the required

resources to manage

collected data

China

37%

Europe

26%

APJ

27%

Having the technology in

place to analyze data

APJChina

27%

Europe

23%

29%

Establishing data

management governance

and processes

Europe APJChina

29%

22%

24%

Building the people resources

needed to analyze data

Europe APJChina

19%

22%

21%

Connecting the curated

data with data users

Europe APJChina

11%

More than 75%

of its potential

100% of its

potential

50-75% of its

potential

25-50% of its

potential

10-25% of its

potential

Less than 10%

of its potential

China

Europe

24%

23%

19%

15%

36%

33%

34%

20%

34%

27%

31%

11%

13%

12%

Source: The Seagate Rethink Data Survey, IDC, 2020

Percentage of Potentially Available

Data Exploited

FIGURE 22

Source: The Seagate Rethink Data Survey, IDC, 2020

Main Challenges in Exploiting Data’s Potential

FIGURE 23

RETHINK DATA: PUT MORE OF YOUR BUSINESS DATA TO WORK—FROM EDGE TO CLOUD | 56 |

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