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Sep 29

Four Steps To A Data Management Strategy In Light Of Big Data

Why read this report

Data is the lifeblood of your entire organization. It should enlighten every function of the business, including customer experience (CX), 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 enterprise architects to formulate a next-generation DM strategy that’s both visionary and pragmatic. This is an update of a previously published report; Forrester reviews and updates it periodically for continued relevance and accuracy.

Key Takeaways

Data Management Powers Apps And Analytics
DM must give your business fast access to all of the data and analytics 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, CX, marketing, sales execution, customer service, and finance.
Your Data Management Strategy Must Mirror Your Business Strategy
Your next-generation DM strategy must be informed by the current and future requirements of your entire business, or it will be doomed to fail. But your DM strategy also must be pragmatic in order for you to implement it successfully.
Your Data Management Platform Must Be Built For Speed And Self-Service Delivery
A real-time DM platform is not a nice-to-have. It’s a necessity in the age of the customer. To satisfy customers at their greatest moments of need, you need a strategy that enables people to self-serve the data and insights they need to make informed decisions. Your platform must help you be agile enough to keep pace with the market.


Without rich data and analytics, everyone in your business is flying blind. Enterprise architecture (EA) pros need a comprehensive data management strategy that sheds light — lots and lots of light — on every function of the business, including operations, CX, marketing, sales, service, and finance. Your DM strategy should:
  • Continuously source new data. Your internal applications, public sources (such as social media), mobile platforms, and data services are generating an onslaught of new data. 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 has 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. (see endnote 1)
  • Analyze data scientifically to enrich it and find non-obvious insights. The goal is not to just report on what happened. Descriptive, predictive, or prescriptive analytics will help you understand the why, what, and how. 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. (see endnote 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 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 CRM system or marketing database.


The bottom line is that your DM strategy must provide your business with quick and complete access to 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-generation strategy that is both visionary and pragmatic?
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 CX — have many requirements in common, they all have unique requirements as well. Further, 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 you 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 who have led 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. This leader is 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 who 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. This steward continuously communicates 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 managing and supplying trusted data and that continuously look to deliver data that meets operational and analytic needs.
  • Enterprise/information architect. This role maintains the data artifacts, rules, processes, and technology needed to automate the management of data. In addition, architects ensure that 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 of trusted data. Business data analysts will fix data issues, prepare data sets, and provide ongoing support to data consumers when there are questions or concerns about the data. Look for individuals who can easily interpret data and understand its context for use in order to decide how to address data problems.
Figure 1: Organizations Expect Data Management To Own Things That Require Business Decisions
Step 2: Perform A Needs Assessment
With your strategy team in place, it’s time to harvest the team’s wisdom. The current and future requirements of your entire business must inform your next-generation strategy formulation — or it is doomed to fail. Your working group must help you perform a needs assessment, which 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.
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. (see endnote 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 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 are those 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 processes in some way. The second type are those that use advanced analytics 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 enters your store so you can send 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.
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 must make to streamline data access and sharing. 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 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, 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 and bring some new perspectives.
  • 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, as some entrenched processes and applications are often at the root of the problem, you must work around them 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 and choose technology options. (see endnote 4)
  • 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 it 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 it 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 seem to magically solve major requirements ( see Figure 3 ).
  • Blow it up or evolve it. There will be a point in the ideation process when 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’s-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, its expected impact, and its strengths, weaknesses, and risks.
Figure 2: The Key Elements Of A Data Management Strategy

Figure 3: Use Business Requirements And Technology Solutions To Formulate A Strategy

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 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.
  • 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. Remember that your strategy is the blueprint; now you have to build the rocket ship. Refer to the other reports in Forrester’s data management playbook for additional resources to implement your strategy.


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 your firm can realize this capability, it must reach a 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, NoSQL querying, and data virtualization — will empower your workforce with tools beyond their training. The four 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 your business’ future.


Survey Methodology
The Forrester Data: Global Business Technographics® Data And Analytics Survey, 2016 was fielded in March 2016. This online survey included 3,343 respondents in Australia, Brazil, Canada, China, France, Germany, India, New Zealand, the UK, and the US from companies with 100 or more employees.
Forrester Data’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. Research Now fielded this survey on behalf of Forrester. Survey respondent incentives include points redeemable for gift certificates.
Please note that the brand questions included in this survey should not be used to measure market share. The purpose of Forrester Data’s Business Technographics brand questions is to show usage of a brand by a specific target audience at one point in time.


  1. 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 helps EA professionals understand and navigate the process of developing a future-proof DM strategy, which can be done more cheaply than ever. See the Forrester report ” Design Tomorrow’s Data Management For Agility In Context. ” “Back to text”
  2. Predictive analytics enables firms to reduce risks, make intelligent decisions, and create differentiated, more personal customer experiences. See the Forrester report ” The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015. “ “Back to text”
  3. Business architects see articulating operating models as a key component of delivering value from both a business and technology management viewpoint. Though much has been written about operating models, a lack of
  4. , 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 Forrester report ” The Anatomy Of An Operating Model. ” “Back to text”
  5. Forrester’s customer analytics playbook lays out the best practices, strategies, technologies, and approaches that make analytics a core customer intelligence capability. See the Forrester report ” Turn Data Into Insights With Customer Analytics. ” Forrester’s business architecture playbook provides a robust and integrated framework that, regardless of industry, provides organizations with a sound basis for managing business change. See the Forrester report ” Business Architecture 2020 — Evolving To Influence Business Strategy. ” 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. See the Forrester report ” Protect Your Intellectual Property And Customer Data From Theft And Abuse. ” 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. See the Forrester report ” Transform Customer Processes And Systems To Improve Experiences. ” In Forrester’s 45-criteria evaluation, we identified 13 big data predictive analytics solutions providers and researched, analyzed, and scored their current market offerings. See the Forrester report ” The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015. ” In Forrester’s 37-criteria evaluation of enterprise file sync and share vendors, we identified, researched, analyzed, and scored products from the 10 most significant solution providers in this market. See the Forrester report ” The Forrester Wave™: Enterprise File Sync And Share Platforms, Hybrid Solutions, Q2 2016. “ “Back to text”
© 2016, Forrester Research, Inc. and/or its subsidiaries. All rights reserved.

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