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TDWI Upside - Where Data Means Business

How Analytics Teams Can Exploit Data Stories

As information becomes the primary power source for the digital economy, analytics teams need to evolve to leverage data stories.

As more businesses undergo digital transformations, the role of the analytics professional is seeing a fundamental shift. In the past, analytics professionals were a back-office function. Experts in mathematics and statistics were revered for their operational ability to take data and turn it into information.

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As this information becomes the energy source powering the digital economy, this role is evolving. It is increasingly critical that this information gets into the hands of decision makers so they can use it to drive the business forward. This changing role of the analytics team is driving a change in the necessary skills on the team.

Analytics teams are being asked to transition from the role of information production to information sales and marketing. This new role requires the team to either develop new skills among current resources or bring in new resources who can bridge the gap and possess the fluidity to move between the technical nature of information production and the marketing nature of information delivery.

In the sales world, there's a saying: "Facts tell but stories sell." We will see data stories become more prevalent and start to enhance (and even replace) the traditional dashboard. Gartner predicts that in the coming years "dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration."

So the question is how do you transform data points, some of which represent complex and computationally expensive mathematical and statistical processing, into data stories that can be easily consumed by decision makers? When looking to create an effective data story, you need to focus on three components: use, context, and audience.


The goal of a data story is to drive decisions and action. Dashboards and reports often are informative but represent only a snapshot of the business. A data story, on the other hand, provides background information and prompts the recipient to action, most often to make a decision or choose between alternatives. Data stories need to be crafted so driving action is their primary function.


With data stories, context represents the historical buildup to that point in time where a decision must be made. It includes what has happened, what decisions were made, and where that puts the business now. This is where the data story provides a much more robust platform for a decision maker to know how to proceed than dashboards of the past.

The data story often will augment the data points with human experience, external observation, and emotional insight to create a complete picture of what is happening and what the decision maker needs to know. The data story accurately frames the decision to be made.

The data story incorporates multiple types of analytics to paint the picture for the information recipient.

  • Descriptive analytics provides context for what is happening
  • Diagnostic analytics gives deeper meaning to why it is happening and what it means to the business
  • Predictive analytics provides a likely future state, either positive or negative
  • Prescriptive analytics provides alternative paths forward that will take the business down different routes and an assessment of how these alternative paths could play out in the business

When woven together with narrative and visualization, a data story prompts the recipient to take action and decide how and where to proceed.


Different types of decisions require different levels of data granularity and timeliness to support decision making.

Frontline managers are often working within operational time frames and need in-process data for making decisions. The data stories that garner their interest relate to things that are happening here and now and will help them to guide near-term decisions. These data stories are delivered more informally -- often as in-person conversations with operational managers.

Mid-level managers are often making tactical decisions that will have an impact on the business in the next few months, few quarters, or possibly few years. These decisions don't require real-time data because the impact of these decisions is on operations that will occur in the future when the real-time data will have come and gone. The level of formality in the delivery of tactical data stories is often higher than that of the operational data stories -- often as defined presentations to tactical leaders.

Senior-level managers make strategic decisions. These decisions usually have a time frame of multiple quarters to multiple years. These decisions have a potentially significant impact on the business and the data stories need to be carefully crafted and reviewed before they are delivered. These types of data stories are usually more formal and are delivered on a scheduled, periodic basis.

As the practice of data storytelling evolves, the analytics team will identify new ways to support it with automated tools. They will learn what parts of the process are inherently about information creation and analysis and what parts require the finesse of a skilled storyteller. As the team goes through this process, it is important that they do not try to automate too much too quickly. Too much automation tends to miss the impact of the data story and overshoot to the point that it becomes decision-making noise. In data storytelling, there is a delicate balance between the art and the science of it, and it will require talented resources with high levels of business acumen to maintain this equilibrium.

When performed correctly, the skill of data storytelling will become a highly sought-after competency for all analytics teams. Whether existing team members will evolve into this role or new resources with existing storytelling competencies will be educated with skills in information-production knowledge will depend on your team. Teams with strong storytelling skills can take the results from very complicated and resource-intensive analytics processes and increase their power to drive the business forward.


About the Author

Troy Hiltbrand is the senior vice president of digital product management and analytics at where he is responsible for its enterprise analytics and digital product strategy. You can reach the author via email.

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