What Will the Tools We Use to Understand Our Data Look Like in the Future?

One particular tool that has started to become old news is the dashboard.

Since time immemorial, we’ve been using these tools, but a recent report by Gartner stated that they would become obsolete in the next few years. How can this be possible when so many people depend on dashboards for data analytics? 

Gartner argues that a new kind of tool is better than dashboards—dynamics data stories. 

And frankly, it’s actually better than a dashboard in so many ways. Read on to find out why. 

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What Are Dashboards?

A data dashboard is a tool that many companies and organizations use to visually track, analyze, and display indicators, metrics, ratios, or other key data needed to measure current standing and health. 

A typical example would be a data dashboard containing all the financial ratios of a certain company. It’s linked to different files, attachments, or sheets that contain the necessary data needed to make the calculations. 

Whenever the data changes or something new is added, values also change simultaneously because they’re all linked. 

Depending on the company’s format, it can either look formal and bland or creative and colorful. It could have charts, graphs, gauges, or tables to summarize the data for ease of understanding. 

All the pre-calculated values are there, making it a one-stop-shop for data analysis. This dashboard is basically the place where top management sees a summary of how a company is doing financially. 

Dashboards are often customized according to the context of the organization so that results are presented better. 

Why Are Dashboards Declining?

The COVID-19 pandemic provided insight into why the use of dashboards is starting to decline. 

While these summaries of data proved useful for companies with data analysts, they introduced many hindrances to front-liners—our healthcare workers. 

These front-liners needed insights that were not readily available on dashboards. Since these dashboards only presented the results of calculations, there was not much front-liners could do with the values they saw. 

But it wasn’t just the healthcare workers who found dashboards quite useless. Only about 30% of employees actually use dashboards which was surprising, given that it was considered to be status quo. 

These workforces have the same reason why dashboards don’t work well for them. During crucial moments, these people needed easy-to-understand and actionable data that just weren’t being provided by complex dashboards. 

This pointed out that maybe dashboards aren’t so useful after all, especially when there are no experts you can easily tap for help. 

You could train people to understand how to interpret data but with so many numbers to look at, how can you expect anyone to come up with analysis as quickly as the data was shown? There is additional work to be done in analyzing; there also needs to be a prior knowledge requirement. 

Introducing Dynamic Data Stories 

This raises the question if dashboards aren’t useful to anybody, what could be a good replacement for it? 

A collective response said dynamic data stories. Gone are the days where numbers only had to be numbers. Today, these data stories can tell stories that anybody can easily understand and absorb. 

Using historical data, visuals, and a lot of creativity, data and calculation results can be relayed as if you’re watching some interactive story. Only it’s a bit formal, and making the right decision is essential. 

This is made possible by three main components: use, context, and time-frame. 

First of all, data stories aim to give enough information to give actionable data to whoever sees them. Users are presented with questions, decisions, or options to help them choose an action based on the data they have, which is using the data.

The second application is context. Users are provided with information that lets them understand why a decision has to be made, whether it’s additional data, decisions made in the past, or historical buildup. 

With enough information about the past, a user can firmly make a rational decision. 

Lastly, time-frame is necessary to understand just what kind of data stories should be shown to make the next step. For example, if it’s presented to mid-level managers who need to make decisions every month or quarter, stories are presented quarterly to gather data. 

Dynamic data storytelling is not an easy job. It’s a mix of talent and skill to interpret data in a way that you would tell a story. Only so much can be automated to ensure that the decision still falls on the user’s shoulders. 

But it won’t be long before geniuses figure out just how to do it right.