What exactly is a data strategy? It’s a good question because there are more than a few definitions floating around. Let’s examine a simple and concise description with three core components that together provide a complete, practical and business-focused approach.

What is a data strategy?

1. An overall vision for the enterprise that clearly articulates the role of data and how insights from analysis will support execution of the business strategy,
2. A set of data-related strategic imperatives that connect directly to and help accomplish specific business goals, and
3. Clear objectives that provide a plan to deliver against each of the imperatives

To illustrate what we mean in practical terms let’s take an example from a company many of us are familiar with – Amazon.  The Amazon recommendation engine is an algorithm that reviews and analyzes customer purchases, comparing browsing and buying patterns to those of other customers in order to identify products they have a high probability of buying next.  These algorithms use data on what customers buy, but also meta-information such as product category (e.g., electronics, furniture), number and distribution of ratings by product, price competitiveness or discounts offered, etc.  These algorithms are sophisticated and immensely effective in boosting sales.  So much so that recent estimates credit 35% of Amazon revenue to this engine. For illustrative purposes let’s frame this incredible success as a component of an overall data strategy for Amazon.

Your organization’s data assets are likely some combination of structured (e.g., product sales) and unstructured (e.g., recorded customer service calls).  The question you need to ask is how can those assets be best leveraged to your advantage? Fundamentally, a good data strategy should provide and be assessed based on its effectiveness in answering this question.

Why do you need a data strategy?

A recent IDG study found that 78% of enterprises believe that over the next 1 to 3 years the collection and analysis of data has the potential to fundamentally change the way their company does business. This increasing focus on its value shouldn’t be surprising when you consider the long list of companies (e.g., Venmo – money transfer, Airbnb – hotels, Uber – transportation) and industries (e.g., Retail – online sales, Insurance – home and car coverage, Energy – grid balancing) that are experiencing significant disruption as a result of how it is being used. Many organizations are unknowingly building valuable data assets through a variety of enterprise systems, but these systems are often disparate and disconnected. The data they contain should be treated like any other asset (e.g., human or financial), and it is through the integration and leveraging of these assets that the potential exists to create meaningful competitive advantage.  When IDG asked participants to identify which business goals are driving investments in data, they listed the following as their top three:

1. Improving customer relationships [58%],
2. Making the business more data-focused [53%], and
3. Changing the way we organize operations [41%]

Unfortunately, few companies succeed in tapping into the potential of these assets to provide information that will help to accomplish these goals. This failure of execution is often the result of not having clearly thought through what they want to accomplish in the context of the overall business strategy. In other words, they don’t have a data strategy, so leaders often proceed by integrating these data assets in the hopes that value will emerge over time. Very rarely does this approach pay dividends, and at best will be only partially effective and have a much lower ROI on investment.

So how do you create a data strategy?

To generate the return you expect and need from these investments, take a structured and disciplined approach that starts with business goals and objectives.

1. Business Aligned: Start by reviewing the initiatives that have been developed to achieve the overall business strategy. What insights will allow you and your team to assess the effectiveness of execution and tweak the strategy over time (e.g., resetting priorities, adding new initiatives, removing initiatives that are not gaining traction)?
2. Initiative Focused: For each initiative, list the information you’ll want or need to be successful. For example, a company looking to grow through increased account penetration might want to invest in developing information that will build understanding of account potential.
3. Audience Centric: Identify the critical roles or stakeholder groups that have a disproportionate influence over success in each of these areas. Review the responsibilities and critical decisions that will be made by each one, and then document the information that will enable fast, effective decision-making.
4. Integrative: Determine how information needs can be met through a combination of internal and external data. Having listed the data elements, document the “single source of truth” (SSOT) for each data resource (e.g., the HRIS system for employee records, an industry source for category growth) to provide clarity on how they can be assembled.  This will serve as your blueprint for the data architecture that supports information delivery.
5. Action Oriented: Develop an information design that addresses each need with visuals that truly impel action. These designs should allow users to “connect the dots” between multiple variables so that together they tell a complete story. Test the design so that it does in fact impel action!
6. Practical: Now develop the overarching vision for your strategy, imperatives and objectives that together support accomplishment of your most important business goals.

A well-constructed data strategy can be the difference-maker that sets your business apart from the competition and, through effective execution, provide the means to achieve sustainable growth for years to come.

By Donncha Carroll, March 21, 2018