Key Success Factors in Any Data and Analytics Strategy

A framework like the Gartner Data and Analytics Strategy and Operating Model (DASOM) helps data analytics leaders create a strategy for the organization to build a data-driven culture — and ultimately to drive business outcomes from data assets.

The strategy defines the what and the why of the organization’s data analytics program. It is the overall approach for achieving a stated business vision of success. Critical components to articulate in any strategy are:

• A data-driven vision

• Drivers for the data and analytics strategy

• Desired outcomes from the program

Develop the D&A strategy’s vision, drivers and outcomes in parallel, not sequentially, since each element informs the other two.

The most effective strategies evolve through a set of conversations among stakeholders aimed at defining a common direction and shared goals between the business strategy and the data and analytics strategy. Avoid the mistake of crafting a D&A strategy in isolation with a small team and then “selling it” to the rest of the business. Use the DASOM framework as a structure to help guide conversations to infuse the business strategy with data and analytics.

A vision for the data and analytics strategy serves to explain succinctly what it means to be a data-driven enterprise and what the organization will get out of it. The vision should focus on the customer value that the program expects to realize. 

Articulating a vision in terms of business value helps position the data analytics program as a business function and its leaders as business peers. This helps to transition data analytics efforts away from decision support and toward decision making. This is critical given growing expectations that data and analytics enable digital transformation and agile applications through digital business platforms.

Be forewarned: Vision statements have developed a bad reputation in some organizations. This often happens when they consist of hollow management-speak that lacks any relevance to the reality of the business. That doesn’t mean you shouldn’t bother to write one. On the contrary, an effective vision gives the data and analytics team and the broader organization a common purpose, helps prevent strategic drift, and helps attract talent. A D&A vision that achieves those ends must contain certain details, however, while also fulfilling key requirements.

A compelling data analytics vision must address what it means to be data-driven from three different perspectives:

• Vision and leadership. What is the role of data and analytics in the organization? How does it contribute to mission-critical business goals?

• Business transformation. Which new business model opportunities does it enable?

• Culture and change. What is the role of data and analytics in digital transformation, and what will the data-driven culture and change look like, especially for data literacy?

A good data strategy is company-specific yet inspired by the drivers and trends that affect your organization. These include:

• External societal, business and industry drivers. What is happening in your industry or in industries you interface with? What role could data and analytics play in adapting to these trends?

• Internal organizational drivers. What changes are underway within your organization? Is there, for instance, a trend toward centralization or decentralization? To a more rules- or principles-based style of governance? Toward agile working?

• Technology drivers. What new technologies are transforming your industry or organization? For example, what advances are underway with artificial intelligence (AI), or with the emergence of the data fabric.

A given organization likely has many different drivers influencing the data and analytics strategy. How important each is to your strategy will depend on a number of factors.

Ensure your data and analytics strategy clarifies “what’s in it for me?” for each of your stakeholders by answering four questions:

1. Who are your stakeholders? List all internal and external stakeholders.

2. What are each stakeholders’ required business outcomes? Express these in terms of revenue growth, cost savings, risk management, customer value and so forth.

3. How will the data and analytics strategy help achieve those goals? Specify concrete use cases, initiatives and D&A products that will improve a process or activity for a stakeholder.

4. What key performance indicators (KPIs) will show your strategy is successful? Measure the business outcomes you expect to produce. Define progress metrics, like “percent of staff trained in data literacy,” and outcome metrics like “contributed to 5% revenue growth.”

Through this process, the data analytics strategy will begin to coalesce around a clear value proposition that defines the form of value data analytics will deliver to the organization. Value propositions fall into three categories:

D&A as a utility

This D&A value proposition positions data analytics as a generic capability available to all stakeholders for all requirements at all times. The primary output is an always-on platform. Measures of success for data as a utility come in the form of a service-level agreement, as in:

• What is the availability of the platform?

• Is the data quick and easy to access for a variety of purposes?

• How long does it take to add a new data source or data access API?

D&A as an enabler

This value proposition focuses on a specific business goal. The primary outcomes are specific fit-for-purpose solutions. Measures of success for data as an enabler relate to business KPIs, such as:

• How much did the conversion rate improve after implementing a new marketing campaign management analytics tool?

• How much money did we save because of predictive asset maintenance through Internet of Things (IoT) data and analytics?

• How much money did we save because of improved fraud detection algorithms?

• A D&A strategy is only as good as its execution. Only once you have clarity about the “why” and the “what” should you shift to consider “how” to execute on the data and analytics strategy through an appropriate operating model.

• Capabilities serve as the linchpin that connects the strategy to the operating model. Organizations should therefore round out the strategy development process by assessing existing capabilities today and those you will need to develop for tomorrow. You can self-assess using the Gartner Data and Analytics IT Score to ask whether you have the capabilities to manage the data operation or develop data analytics talent.

Capabilities assessments reveal key gaps the organization will need to fill. Data literacy skills are often one of them. Respondents to the 2022 Gartner CDAO Survey listed poor data literacy as a top roadblock to success in their organizations, a key area of investment to build a data-driven customer with a corps of data literate employees that leverage data assets and increase overall return on investment. Capability assessments can also serve as a key input for chief data officers to identify the data scientist and other talent roles they need on their teams.