For: Enterprise
Architecture
Professionals
Four Steps To A Data Management
Strategy In Light Of Big Data
by Mike Gualtieri and Nasry Angel, February 26, 2015
Key Takeaways
Data Management Powers Apps And Analytics
DM must give your business fast access to all of the data and analytics that it needs to
grow revenue and profits, both now and in the near future. Data is critical to virtually
every function of the business, including R&D, operations, customer experience,
marketing, sales execution, customer service, and finance.
Your Data Management Strategy Must Mirror Your Business Strategy
Your next-gen DM strategy must be informed by the current and future requirements of
your entire business or it will be doomed to fail. But, it also must be pragmatic for you to
implement it successfully.
Your Data Management Platform Must Be Built For Speed And SelfService Delivery
A real-time DM platform is not a nice-to-have. It’s a necessity in the age of the customer.
In order to satisfy customers at their greatest moments of need, you need a strategy
that enables people to self-serve on the data and insights they need to make informed
decisions. Your platform must help you be agile enough to keep pace with the market.
Follow Four Steps To Formulate Your Firm’s DM Strategy
A good strategy is an action plan that all stakeholders clearly understand and is
actionable for those who will implement it. You must decide where you are, where you
need to go, and what you must do to get there. This report will guide you through a stepby-step process to get you there.
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For Enterprise Architecture Professionals
February 26, 2015
Four Steps To A Data Management Strategy In Light Of Big
Data
Strategic Plan: The Data Management Playbook
by Mike Gualtieri and Nasry Angel
with Holger Kisker Ph. D., Leslie Owens, and Elizabeth Cullen
Why Read This Report
Data is the lifeblood of your entire organization. It should enlighten every function of the business,
including customer experience, operations, marketing, sales, service, and finance. A data management
(DM) strategy is critical. The goal should be clear: provide all business functions with quick and complete
access to all of the data and analytics that they need, both now and in the future. But, it’s easier said than
done, and that’s where strategy comes in. This report provides a four-step process for formulating a nextgeneration DM strategy that will be both visionary and pragmatic. This report was originally published
on October 1, 2013; Forrester reviewed it for continued relevance and accuracy, found that some changes
were needed, and updated it accordingly as of February 2015.
Table Of Contents
Notes & Resources
2 Ubiquitous, Fast, And Secure Access To All
Your Enterprise Data Is Key
This report is based on ongoing research
into the key success factors and strategies
of high-performance data management
programs.
3 Take Four Steps To Formulate Your Data
Strategy
Step 1: Assemble The Right Data Stakeholders
Related Research Documents
Step 2: Perform A Needs Assessment
Predictive Analytics Can Infuse Your
Applications With An “Unfair Advantage”
August 27, 2014
Step 3: Investigate Your Strategic Options
Step 4: Prioritize Your Strategic Options
WHAT IT MEANS
9 The DM Strategy Of The Future Delivers On
Speed, Agility, And Context
9 Supplemental Material
Design Tomorrow’s Data Management For
Agility In Context
May 7, 2014
The Forrester Wave™: Big Data Hadoop
Solutions, Q1 2014
February 27, 2014
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For Enterprise Architecture Professionals
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Four Steps To A Data Management Strategy In Light Of Big Data
Ubiquitous, Fast, And Secure Access To All Your Enterprise Data Is Key
Without rich data and analytics, everyone in your business is flying blind. A comprehensive data
management strategy ought to shed light — lots and lots of light — on every function of the
business, including operations, customer experience, marketing, sales, service, and finance. Your
data management strategy should:
■ Continuously source new data. An onslaught of new data is being generated by your internal
applications, public sources (such as social media), mobile platforms, and data services. Your DM
strategy must assume that the volume, velocity, and variety of new data will continue to increase.
It must continuously identify new sources and incorporate them into your DM platform.
■ Capture, manage, and store all enterprise data to preserve history and context. Data without
context is like navigating a museum that has artifacts with no labels. You don’t know where
it came from, how it’s best used, who it can benefit, or if it’s legitimate. It’s often impossible to
judge what data is valuable and what isn’t. In the age of big data, you must capture and store it
all. Data that might seem completely irrelevant to your business now, such as mobile GPS data,
might be pertinent in the future. The effort and cost of capturing and storing all data have often
forced decisions on what to store and what to throw away. But new lower-cost technologies,
such as Hadoop, have made it possible to capture and store lots more data cost-effectively.1
■ Scientifically analyze data to enrich it and find non-obvious insights. The goal is not to
just report on what happened. This will help you understand the why, what, and how through
descriptive, predictive, or prescriptive analytics. For example: you can know in real-time that
your customer is standing in a dressing room preparing for an upcoming wedding. Today’s
advanced analytic capabilities not only retrieve past sales records, but also help you predict what
your customer likes, what she’s going to do next, and what you can offer her. You also need data
scientists who use machine learning algorithms and advanced visualization tools to uncover
non-obvious gems about customers that can give you competitive advantages.2
■ Deliver data quickly and liberally with all those who need it. Organizations can use data
to dramatically improve virtually every function of the business, including product research,
design, and development; advertising and marketing management; sales; and the customer
experience. Data is often in silos, making it very difficult to share it across the organization. A
next-generation DM capability must make data available quickly and to everyone that could get
value from it — and because it may not be obvious who can get value from the information, you
should make all of it available. For example, marketing data may be very useful to both R&D
and customer service but be siloed in a customer relationship management (CRM) system or
marketing database.
The bottom line is that your DM strategy must provide your business with quick and complete
access to all of the data and analytics that it needs, both now and in the near future. The key
question is: How do you formulate a next-gen strategy that is both visionary and pragmatic?
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Four Steps To A Data Management Strategy In Light Of Big Data
Take Four Steps To Formulate Your Data Strategy
Your strategy is a high-level action plan. A good one will clearly align with the core capabilities set
forth in this report and with your specific DM goals. All stakeholders must be able to understand it,
and everyone who will be involved with implementing DM should be able to act on it. As with any
strategic planning process, you have to decide where you are now, where you need to go, and what
you need to do to get there. Follow the four steps outlined below to formulate a specific data strategy
for your company.
Step 1: Assemble The Right Data Stakeholders
No one person has complete visibility into the data requirements across your entire business. While
functional areas — including product R&D, operations, marketing, sales, finance, and customer
experience — have many requirements in common, they all have unique requirements as well.
Furthermore, our data shows that business users believe the “IT department” has primary ownership
of key areas that also require input from business users (see Figure 1). The first step in formulating
your strategy is to assemble a working group of data subject-matter experts (SMEs) across all of the
functions and divisions within your business. This working group will play a key role in helping you
understand the requirements and identify ownership across the organization. This will help avoid
creating yet another rogue DM silo. Define the key stakeholders for your working group:
■ Chief data officer: This executive leader works directly with senior executives and line-of-
business peers to identify and craft the direction and investment in data and data governance for
business outcomes and objectives. Look for individuals that have lead business transformation
projects that relied on data and intelligence, represented business interests, and collaborated
closely with the CIO.
■ Data governance program leader: This role leads the data governance program that
defines, executes, and tracks the compliance of data with business and data policies. They are
accountable for putting into place the policies, procedures, and processes that data governance
teams support and meeting the strategic objectives and programs of the business. Look for
individuals that have managed data programs within the lines of business, such as customer
intelligence, marketing and sales operations, or financial operations.
■ Business data steward: This role brings the subject matter expertise and requirements for
data to the data governance organization and technology management. They continuously
communicate how data performs for the business and where more support or capabilities are
needed. Look for this individual within the lines of business that have day-to-day experience
of managing and supplying trusted data and continuously look to deliver data that meets
operational and analytic needs.
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Four Steps To A Data Management Strategy In Light Of Big Data
■ Enterprise/information architect. This role takes on the responsibility maintaining the data
artifacts, rules, processes, and technology needed to automate the management of data. In
addition, they ensure the right processes and procedures are in place to consistently address
data needs across all initiatives, projects, and ad hoc requests. Enterprise architects should have
experience designing and developing data management systems.
■ Business data analysts. Remediation of data is a key facet to trusted data. These individuals will
fix data issues, prepare data sets, and provide the ongoing support to data consumers when there
are questions or concerns about the data. Look for individuals that can easily interpret data and
understand its context for use in order to make decisions on how to address data problems.
Figure 1 Organizations Expect Data Management To “Own Things That Require Business Decisions”
“Which organizational unit currently has primary ownership
of the following tasks or responsibility areas?”
(Respondents who answered “IT department”)
72% Data security/privacy
Issue: IT doesn’t know the right
risk classification
48% Data quality
Issue: IT doesn’t know the
impact on business outcomes
40% Data strategy
Issue: IT may underestimate business
disruption and where to prioritize
39% Business intelligence
Issue: IT doesn’t know what the
business needs to know
Base: 249 North American business decision-makers
(multiple responses accepted)
Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2014
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Step 2: Perform A Needs Assessment
With your strategy team in place, it’s time to harvest their wisdom. The current and future
requirements of your entire business must inform your next-gen strategy formulation — or it’s
doomed to fail. Your working group needs to help you perform a needs assessment. This assessment
shares all of the characteristics of a traditional requirements-gathering process, but with a careful
eye toward likely future requirements as well. To begin understanding surface requirements, enlist
your working group through surveys, individual interviews, and group meetings.
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What business capabilities and processes create, transform, and consume data? The best way to get at
these requirements is to ask SMEs to describe the current and future business processes that directly
involve customers or prospects — this will reveal customer data needs and applications.3 It’s valuable
to have a technical SME in sessions with business SMEs to fill in the technical details of where the
data lives and how it flows through the technical architecture. Ultimately, your needs assessment
should include:
■ The current business use cases for data. To be pragmatic, your strategy must take into account
the current uses of data throughout your organization all the way up to consumption. A strategy
concerned only with maintaining systems of record will be a disaster. Your business use cases
should identify business processes, applications, and data for each functional area and division.
For most organizations, this will be the most time-consuming part of the strategy process, because
most organizations don’t have a complete view of how they use data. This assessment should
include enough detail about customer profile, transactions, and analytics so that your technical
SMEs can later trace the origin and flow of the data throughout the technical architecture.
■ The future business vision and use cases for data. There are two types of future requirements.
The first type are those requirements that will allow key business processes that currently have a
limited link to data to better leverage that data. For example, is your customer segmentation tightly
integrated with your R&D processes? Do you collect real-time client feedback for marketing
campaign tracking? Accurate and timely customer data can enrich and improve pretty much all of
your core business process in some way. The second type of requirements is those that use
advanced analytic capabilities to create more business insights from your customer data. You
might have a good grip on your customer demand, but can you predict how it will change over the
next few weeks? You collect customer feedback, but do you know when a customer actually enters
your store so you can send him or her a timely promotion? Leveraging data is not only about
tightly integrating that data into your business processes, it also depends a lot on the insights and
information that you can extract from the data and leverage in those processes.
■ A technical map of data. The final product of your needs assessment will include a technical map
of your current DM environment. Use the information gathered from current and future business
use cases to create a map that details the origination and flow of data throughout your technical
architecture. The map should show the applications, databases, and other systems in which each
data element makes an appearance. Keep in mind that data can originate from external sources
like customer apps and internal sources like CRM, transactional systems, and analytics.
■ Alignment with corporate and other strategies. The business use cases that your team gathers
and the data map it creates during this needs assessment must also align with corporate,
divisional, and other relevant strategies. Make sure that your team is aware of other active or
planned strategies as a final checkpoint for the requirements.
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Four Steps To A Data Management Strategy In Light Of Big Data
Step 3: Investigate Your Strategic Options
You now have the essential business and technical understanding of the data within your business to
formulate a strategy. This is the fun part if you’re creative and like to solve puzzles. You have to make
sense of all of the requirements gathered during the needs assessment to generate strategy options.
Remember the end game:
Your data management strategy must provide your business and customers with complete access to
all of the data and analytics that it needs, with agility and context; both now and in the near future.
The key elements of your strategy will include business imperatives, technology architecture, and the
implementation road map (see Figure 2). The business imperatives will include those changes that
the various functional areas and divisions need to make to streamline access to and sharing of data.
The technology architecture will make it possible. The implementation road map will capture what it
will take to make it happen. Follow these practices to facilitate the ideation process:
■ Be very selective about who is on your strategy team. To avoid getting bogged down, your
strategy formulation team should have about six members — half from your technology
management team and half from the business. Make sure that requirements from all relevant
business lines are represented and considered. Don’t let sales, marketing, or any other function
dominate your team. Formulating strategy options requires a command of the requirements,
technology expertise, creativity, and lots of iterations. Even though your core team is small, your
members can reach out to other resources as needed to clarify requirements, brainstorm the
technical architecture, and do some initial vetting of ideas. Consider inviting outside experts,
such as consultants and analysts, to augment your strategy team. They can bring some new
perspectives to the group.
■ Descope to make your work less daunting. You may be sitting on a mound of requirements
resulting from the needs assessment. Pragmatism demands that you try to take some business
requirements and/or technologies off the table. Try to identify business use cases and/or
technologies that are unlikely to change, either because they will have no impact on your DM
goals or because they are so entrenched that you should consider them constraints rather than
opportunities. However, some entrenched processes and applications are often at the root of
the problem and must be worked around to avoid creating an idealistic strategy that you can
never implement.
■ Stay informed. Your knowledge of your business is only one of the raw materials you should
use to formulate your strategy; it’s just as important to leverage technology research and
industry best practices. Be sure to use external research to fuel your creative process. Forrester
offers a plethora of playbook research on relevant areas such as: analytics, business architecture,
data security and privacy, and CRM. We have also published Forrester Wave™ evaluations on
enterprise data warehouse solutions, big data predictive analytics solutions, file sync and share
platforms, and many others to help you find technology options.4
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■ Oscillate between bottom-up and top-down ideas. It can be hard to know where to start
the ideation process. Looking from the top down can give you the best view and ideas, but
overlooks the mess of processes and technologies that a pragmatic strategy must address.
Looking from the bottom up categorically acknowledges your existing processes and
technologies, but can overly constrain your ideas. The best solution is to oscillate between the
two. Devise a big-picture idea unconstrained by existing processes and technology, and then
dive into the depths to see how practical the solution looks. Conversely, starting in the depths
can result in technical architecture ideas that magically solve major requirements (see Figure 3).
■ Blow it up or evolve it. There will be a point in the ideation process where the existing
constraints seem so overwhelming that you wish you could start from scratch. This is often the
case for firms that have amassed Frankenstein monster-like architectures over the course of
years of evolving business processes and architectures. Options often emerge at either extreme:
gradually evolving the existing architecture or implementing a completely new one at massive
cost. You’ll probably want to consider options that fall somewhere in the middle. This is OK;
final decision-makers will appreciate having choices.
■ Finalize your strategies in a presentation document. Create a presentation of 15 to 30 slides
for each strategy option to communicate what it is, what its expected impact is, and what its
strengths, weaknesses, and risks are.
Figure 2 The Key Elements Of A Data Management Strategy
Strategy element Key question
Issues to address
Business What changes should the functional areas and
imperatives divisions make to streamline access to and
sharing of data?
• Impact on customers and business
• Business processes
• Organization
• Risks
• Measure success
Technical What is the target architecture needed to provide • Impact on business and IT
architecture quick and complete access to all of the customer • Existing architecture
data needed to meet the business requirements? • Technology components
• Processes
• Organization
• Cost
• Risks
Implementation What is the plan to implement the business
road map imperatives and technical architecture?
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• Resources
• High-level project plan
• Impact on customers, business,
and IT
• Risks
• Business value milestone
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Figure 3 Use Business Requirements And Technology Solutions To Formulate A Strategy
Source and Manage
• External/public
sources
• Interactions
• Machine-and sensorgenerated data
• Profiles
• Transactions
Data
Relevant Tech:
• Data encryption and
masking
• Data integration
• Data warehouse
• Hadoop
• In-memory cache
• NoSQL database
• Operating system file
system
• Relational database
management system
(RDBMS) database
Analyze
• Analytics data marts
• Enrichment
• Intelligence (descriptive)
• Operational data stores
• Predictive/prescriptive
models
Learn + Adapt
Relevant Tech:
• Advanced analytics
• Business Intelligence
• Predictive analytics
• Streaming analytics
Deliver
• Analytical applications
• Customer applications
• Dashboards/alerts
• Operational applications
• Reports
Insight
Relevant Tech:
• Advanced visualization
• Applications
• Data application
programming interfaces
(APIs)
• Data virtualization
• Distributed in-memory
data fabric
• Search
Master data management (MDM)
(model and govern)
Data quality
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Step 4: Prioritize Your Strategic Options
Now you’re ready for the final step in this strategic exercise: a road show to get stakeholders to
choose from among the two or three strategy options you came up with during the strategy ideation
step. This includes getting buy-in from all stakeholders, including the executive stakeholders who
will ultimately have to approve and fund the effort to implement the strategy. Before you make the
final presentation:
■ Vet the options with stakeholders. You have doubtlessly already included various stakeholders
during the strategy ideation process. However, it will be enormously valuable to vet your strategy
options with other stakeholders who were not part of the process. This will provide additional
unbiased feedback that you can use to refine your strategies to make them more understandable.
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■ Score the impact of each strategy. Revisit each of your strategy options after vetting them with
stakeholders. You’re likely to have received feedback that that changes your view of the strengths
and weakness of each of your strategy options. Revise your strategies as necessary and score
each strategy based on its impact on the business, the cost to implement it, and the difficulty of
implementing it.
■ Make a strong case for each strategy. The purpose of formulating two or three competing
strategies is to provide significant, differentiated options for the final decision-makers. Assign
advocates to each strategy — people who can make the case that it’s a viable option that merits
consideration. The ultimate decision-makers will appreciate that they have strong strategies to
choose from.
Your strategy is the blueprint; now you have to build the rocket ship. Refer to Forrester’s data
management playbook for additional resources to implement your strategy.
W h at I t M e a n s
The DM Strategy Of The Future Delivers On Speed, Agility, And Context
Your data management strategy should lead your firm to a real-time, self-service data platform to
meet the needs of the agile digital business. In the future, enterprise architects will be measured
not on how much data they store but instead on if the business can act on the data quickly and
confidently. But before this capability is realized, your firm must reach consensus on a data code of
conduct regarding thorny issues of privacy, security, and the ethical use of private and public data.
Without this shared culture, the new data management technologies, such as distributed in-memory
computing, Hadoop, No-SQL querying, and data virtualization, will empower your workforce with
tools that may be beyond their training. The steps discussed above stress the interconnectedness
between how well you manage your data and how well you serve your customers — which means
your DM strategy will set the table for the future of your business.
Supplemental Material
Forester’s Business Technographics® Global Data And Analytics Survey, 2014, was fielded to 1,658
business and technology decision-makers located in Australia, Brazil, Canada, China, France,
Germany, India, New Zealand, the UK, and the US from SMB and enterprise companies with
10 or more employees. This survey is part of Forester’s Business Technographics and was fielded
from January 2014 to March 2014. Research Now fielded this survey on behalf of Forester. Survey
respondent incentives include points redeemable for gift certificates. We have provided exact sample
sizes in this report on a question-by- question basis.
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Four Steps To A Data Management Strategy In Light Of Big Data
Each calendar year, Forester’s Business Technographics fields business-to-business technology
studies in 10 countries spanning North America, Latin America, Europe, and Asia Pacific. For
quality control, we carefully screen respondents according to job tile and function. Forester’s
Business Technographics ensures that the final survey population contains only those with
significant involvement in the planning, funding, and purchasing of business and technology
products and services. Additionally, we set quotas for company size (number of employees) and
industry as a means of controlling the data distribution and establishing alignment with IT spend
calculated by Forrester analysts. Business Technographics uses only superior data sources and
advanced data-cleaning techniques to ensure the highest data quality.
Endnotes
The future of DM is also big — meaning that firms must capture, store, analyze, and use a plethora of
data from new sources to create a multidimensional view of customers. Forrester’s future look for data
management and helps enterprise architecture professionals understand and navigate the process of
developing a future-proof DM strategy, which can be done more cheaply than ever. See the May 7, 2014,
“Design Tomorrow’s Data Management For Agility In Context” report.
1
2
Predictive analytics enables firms to reduce risks, make intelligent decisions, and create differentiated,
more personal customer experiences. See the January 3, 2013, “The Forrester Wave™: Big Data Predictive
Analytics Solutions, Q1 2013” report.
Business architects see articulating operating models as a key component of delivering value from
both a business and IT viewpoint. Though much has been written about operating models, a lack of
methodologies, common terminology, and replicable results still hinders development. Business architects
have an opportunity to improve model development and articulation methods by providing more structure
to the process. See the July 16, 2013, “The Anatomy Of An Operating Model” report.
3
Forrester’s customer analytics playbook lays out the best practices, strategies, technologies, and approaches
that make analytics a core customer intelligence capability. For an overview of the playbook, see the June 19,
2014, “Turn Data Into Intelligence With Customer Analytics” repot.
4
Forrester’s business architecture playbook provides a robust and integrated framework methodology that,
regardless of industry, provides organizations with a sound basis for managing business change — from
broad organizational transformation to cost reduction, customer experience improvement, and mergers and
acquisitions (M&A) and IT strategy alignment. For a perspective on the future outlook in this space, see the
December 4, 2014, “Business Architecture 2020-Extending Beyond Organizational Boundaries” report.
Forrester’s data security and privacy playbook shows you how to avoid the hype and take a holistic and
long-lasting approach to data security. For an overview of the playbook, see the May 27, 2014, “Protect Your
Intellectual Property And Customer Data From Theft And Abuse” report.
Forrester’s CRM playbook outlines four steps in order to transform customer-facing business processes to
deliver differentiated customer experiences: 1) discover the value of CRM; 2) plan the right strategy; 3) act
to execute the strategy with precision; and 4) optimize your results. For an overview of the playbook, see
the January 13, 2014 “Transform Customer Processes And Systems To Improve Experiences” report.
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This report details our findings about how well each solution fulfills the criteria and where they stand in
relation to each other, which will help you select the right big data predictive analytics solution. See the
January 3, 2013, “The Forrester Wave™: Big Data Predictive Analytics Solutions, Q1 2013” report.
In Forrester’s 26-criteria evaluation of file sync and share vendors, we identified, researched, analyzed,
and scored products from the 16 most significant solution providers in this market. We scored factors
like mobile support, security, links to systems of record, organizational commitment, market experience,
and deployment architecture to give you the decision tools to create the right shortlist for your particular
environment and scenarios. To see how vendors in this market fared, see the July 10, 2013, “The Forrester
Wave™: File Sync And Share Platforms, Q3 2013” report.
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