Key Success Factors in Any Data and Analytics Strategy

Collaborate and execute your way to a modern, actionable D&A strategy that drives measurable business value.

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Using data as an asset to drive market differentiation and growth

Today, business success and digital initiatives are fueled by data and analytics (D&A) strategies that scale with business ambitions.

Use this roadmap to:

  • Position D&A initiatives to drive measurable business outcomes

  • Design your D&A program around five key stages, from vision to continuous improvement

  • Identify your key stakeholders across IT and other functions

Infuse data and analytics into the business strategy

To achieve outcomes such as improved decision making or increased revenue, data and analytics leaders must focus from the outset on business value. A strategy framework is key.

A framework helps D&A leaders lay strategy foundations

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.

Craft the strategy — the what and the why — before moving to the operating model — where you define how to execute the strategy. Use this data and analytics strategy template to help.

When it comes to defining the operating model, make sure to include the integrated set of competencies and capabilities (resources, processes and structures) required to successfully deliver on the strategy. Conduct an assessment to identify the gaps and deficits your organization will need to address to realize its data and analytics goals. Common gaps include data and analytics talent, data literacy and data governance policies.

Position D&A as a business function focused on value

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?

An effective data strategy vision statement also fulfills four specific requirements, such that it is:

  1. Inspirational

  2. Company-specific

  3. Positioning data analytics as a business discipline

  4. Providing strategic focus

A well-developed vision statement that explains what data-driven means from the three perspectives and fulfills the four requirements adheres to the following structure:

We contribute to (strategic goal), for (stakeholder X, Y, Z), by doing (value propositions).

Consider this example from a pharmaceutical company:

“We strive for a world where information provides actionable insight to prevent, protect and predict diseases of all kinds, and improve people’s lives.”

Or this one from a financial services provider:

“We aim to help clients lead financial sound lives, being there when needed, through data-driven insights, processes and products.”


Identify relevant business, industry and technology drivers or trends

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.

Stakeholder priorities and concerns, for example, will influence how much you emphasize a given driver in the data and analytics strategy. How established a given driver is today in relation to its likely future impact offers another source of consideration. Trends may be:

  • Established — for which you can adopt best practices to address and prioritize them.

  • Evolving — which requires planning, even though it is usually clear which direction they are likely to go.

  • Emerging — bringing more uncertainty and requiring more experimentation and learning.

Map drivers and trends in a radar grid according to the type of driver/trend (internal, external or technological) and how established it is.


Define a clear value proposition for the data strategy

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?

D&A as a driver

This value proposition focuses on achieving new business goals with tools or new forms of data that result in new business ideas and revenue sources. Measures of success for this value proposition relate to innovation. For instance:

  • What is the relative split between data efforts that produce no new insights, that produce optimization insights and that produce transformative insights (e.g., 50%:40%:10%)?

  • How much new revenue has been generated by data and analytics initiatives?

All three of these value propositions deliver business value, and they often exist simultaneously within the same organization.

Adopt a portfolio management approach to optimize the business value produced by the data operation. Considerations include the business impact for a given use case in relation to success contributors and inhibitors. These include urgency, time to value, stakeholder commitment, organizational readiness, data literacy, data driven culture and so on. Note that the value of some use cases may be difficult to quantify in financial terms. For those, use a scoring system to assess, rank and prioritize.

Capabilities connect strategy to operations

  • 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.

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