Telling an Effective Data Story

Written by Paul Eder and Matt Ancona

Your data is a lie. You don’t believe us? Then tell us what its limitations are.

If you can, then you know your data is flawed – but you still trust that it has high enough quality to tell you something meaningful. If you can’t, then you don’t know enough about your data to know what it is hiding from you.

Over the past several years, government has attempted to solve the problem of lying data with increased transparency and effective data management measures like the Digital Accountability and Transparency Act and the Evidence Act. No, transparency does not solve quality issues.

But it opens the kimono enough that an educated public can ask questions and seek rectification of major issues.

Quality, trustworthy data is the number one prerequisite for telling a compelling data story in your organization. Your data knows more than you do. But it does not know how to describe itself. That’s where you come in. You hold the key to unlocking its secrets. But to get to a place where your story makes sense, you must first overcome a number of major barriers.

Barrier #1: Improper data mix

Most data do not reveal enough as standalone metrics. The most compelling data stories integrate quantitative and qualitative data as well as historical trends.   In many organizations, we hear that a particular issue is their number one concern. However, they have no hard data backing up the claim – only anecdotes. For example, one organization described how one process consistency “took too long.” But the organization did not measure the time the process took, nor had they identified a target timeline. How can something “take too long” when you do not know the goal you are striving to achieve? That story is missing some supportive details.

Barrier #2: No alignment with mission

Many organizations want data. They know they need data. But they cannot tell you what they will use it for. They lack a coherent data strategy. This is one of the principles embedded in the Evidence Act. Organizations should have learning agendas – they should know the questions they seek to answer. The amount of data that an organization could collect is literally infinite. Knowing the questions you want to answer grounds your organization and enables it to focus on the data most important to its mission.

Barrier #3: Lack of quality data

Data governance isn’t sexy, but it is essential to telling a coherent data story. Organizations must know what clean data looks like. Leaders must know that the story they are being told is based on timely, valid information. Robust governance processes provide the confidence that decisions made from data will make sense.

Barrier #4: Lack of skilled people

Although the need for data is becoming universal, the understanding of data is not a universal skill. The Federal Data Strategy highlights the need for government to upgrade its data culture and data acumen. Accordingly, the Office of Management and Budget has opened a data science academy to reskill the Federal workforce. Still, data responsibilities fall within many job descriptions. For example, a low-level data entry clerk can be the linchpin connecting advanced analysts to quality data. A clerk who doesn’t understand how they fit in the quality data cycle could inadvertently make mistakes that lead to bad data stories being told throughout the organization – even if the most skilled data resources are tasked with interpreting the data.

In addition, the rise of data visualization tools has had a side effect of making people trust data more when it is presented with a pretty visual than when it is in its raw form. It may be human nature to be drawn to beauty, but pretty pictures do not mean the data story is accurate, and those interpreting graphics need to be wise enough to question inaccurate data that is visually appealing.

Barrier #5: Improper availability and use of tools

Many modern analytical tools require special licenses or software programs that few people can access or understand. Even freeware tools can require a sophisticated knowledge set that makes them useless in the wrong hands. Organizations must focus on getting the right tools for their needs, rather than just flooding their environment with everything. Choice can be wonderful, but bad choices can lead to inaccurate or incomplete data stories. We also caution organizations not to discount data just because it “comes from Excel.” Excel is a tool available on nearly everyone’s desktop, and that level of availability may make it the best tool in a number of situations.

So what can you do to tell a great data story?

Barriers always exist. But you can overcome them with knowledge and skilled people. Data won’t make decisions for you, but good data strengthens confidence which is sometimes what you need to drive a culture forward. The lack of confidence stifles creativity and willingness to decide. If the protocols in place overcome barriers effectively, leaders can have the confidence to move forward with data-driven decisions. Or they can choose to ignore the data – which is also fine, as long as the story they are telling is not a lie.