7 Elements of a Data Strategy

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Even as companies make larger investments in data and analytics initiatives than ever before, age-old obstacles like siloed and untrustworthy data, inefficient data management practices, and a lack of meaningful insights continue to get in the way of unlocking your data’s potential.

A good data strategy framework is proven to help companies overcome those obstacles and define the path to become more data driven.

In this blog, we discuss the key components of a data strategy, including:

A data strategy is the foundation to all your data practices. It’s not a patch job for your data problems, and it addresses more than just data—it’s a long-term, guiding plan that defines the people, processes, and technology necessary to solve your data challenges and support your business goals.

Creating a successful data strategy requires business leaders to take a deliberate—and objective—look at the business through the lens of data and anticipate what needs to happen to bring about specific objectives the company has defined. Business leaders should consider:

It’s not enough to just have data—you need a strategy in place to realize your data’s value and to bring to bear meaningful outcomes aligned with your business goals. A data strategy enables your organization to be innovative, business users to be effective, and the business to be competitive. Without a data strategy in place, you can encounter common data challenges including:

We’ve helped hundreds of organizationswith varying levels of analytical maturity and technical needs craft their data strategy and make better use of data. Our extensive experience has resulted in identifying the following key components of a data strategy.

Data initiatives must address specific business needs to generate real value—otherwise, you risk prioritizing the wrong projects, missed insights, wasted time and resources, and even loss of interest and faith in data initiatives throughout the organization.

Tying your data strategy to your business strategy sets you up for success. When your data initiatives support company goals, you get business buy-in—which means more prioritization of data activities—and the whole organization wins.

Here are ways to align your data strategy with your business strategy:

With this information documented, you can begin to build a log of use cases that will be included in your data strategy roadmap (see Tip #6!).

You need to know your starting point—your current analytics maturity level—before outlining your desired future state. This helps you set attainable goals and take realistic, incremental steps to become more data driven.

According to Gartner, modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive. Here’s how, when, and why you should use each.

To get a full picture of your analytics and data maturity, you need:

With an understanding of your current state, you can identify where you have gaps, where there are known issues, and what you need to optimize—whether it be technology, processes, people, or all—to meet business objectives across the organization.

Your data and analytics maturity level is a tool to prioritize your projects and serves as a benchmark to measure progress as you increase capabilities and perform tasks from your data strategy.

It’s easy to get caught up in the hype and latest technologies and have the inclination to want to choose the “newest” tool in the market. It’s also easy to get overwhelmed by the ever-growing number of choices and decide to stick with what you have or take a single-vendor approach.

There are effective ways to cut through the noise of the market and choose technology that works best for your situation:

When choosing your tools and technology, remember that they are not standalone components, but rather integrated parts of your data architecture.

Becoming a data-driven organization requires more than just technology—you need the right people in the right roles to ensure technology and processes are adopted and that business objectives are being met.

The first step of building an effective data analytics team is to choose or identify your operating model. Your operating model dictates the team structure and roles necessary for you to meet your goals.

There are three types of operating models an organization can subscribe to: decentralized, centralized, and hybrid. One isn’t any better than the other; the decision comes down to the size and resources of your organization and its current and future data needs.

Then, you should assess the skillsets of your team. Start with understanding your staff’s strengths and where they’ll need support.

This assessment should also be tied to your operating model—should data analysts be aligned to a business unit or to IT? And how will IT support the business in their analytics needs? Even topics like employee reviews and incentive plans should be evaluated. After all, these levers can be used to encourage employees to use data in the way the organization is intending.

Data governance is what ultimately leads to high quality data and allows enterprise-level sharing of data across the business.

While data governance is very important to your data strategy, it’s important to understand that just like your level of data and analytics maturity is unique to your organization, so is your need for data governance.

Although there are some great tools on the market to support the effective application of governance, data governance itself isn’t a tool or platform you can purchase, and there isn’t one way to approach it. Implementing data governance carries a high risk of low adoption if not done correctly and can get costly in a hurry. To avoid this, the data governance program you outline should account for your company’s needs, size, urgency, maturity, and capabilities.

User adoption of your data and analytics happens when data governance is realistic and something that blends into your everyday operations.

Data governance takes leadership and sometimes navigating through difficult conversations. Developing a business glossary is a good place to start. A business glossary is a living document in which all available end-user measures and dimensions are formally defined. During these conversations, misunderstandings about terms are identified and corrected.

The data strategy roadmap is the culmination of all the work you’ve done to this point and what makes all your previous work actionable. You’ve identified all that needs to happen to bring you from where you are to where you’d like to go, but before getting started with any design, build, training, or re-engineering of a business process, it’s critical to prioritize the activities.

For each recommendation that will help bridge the gap from current state to the future state, define the feasibility and expected business value it will provide. The plan should prioritize activities that are easiest to implement but also provide quick wins to the business.

Other factors to include in the data strategy roadmap are:

Having a timeline in your roadmap that allows for celebration of incremental wins that are earned along the way will help keep your team motivated and morale high.

You’ve successfully created your data strategy. Equipped with a roadmap, you are ready to proceed with data initiatives.

Last, but not least, is addressing change management, because your teams will be dealing with a lot of change and maybe new responsibilities and expectations. Without a culture change, your data strategy efforts will not see their fullest potential.

Consider training and enablement, budget support, and communication in order to promote a data-driven culture, increased adoption, and improved decision-making.

A data strategy is the foundation for all your data and analytics needs—especially as your organization looks to become more analytically mature. It is not focused on a short-term project, but rather a long-term plan that takes a holistic look at people, processes, and technology.

As you develop your data strategy framework, remember the seven key elements defined in this blog—alignment with business strategy, analytics and data maturity evaluation, data architecture and technology, data analytics team, data governance, data strategy roadmap, and culture change and management—are all critical pieces to the puzzle as you look to overcome data challenges, improve decision-making, and support your business needs.

Watch our webinar, “The (updated!) 7 Elements of a Successful Data Strategy“, on-demand to learn more about the framework you need to build an effective data strategy.