What does a bad decision look like at your company?

For a financial firm, it could mean overlooking the risk of loan default for a particular organization. For an insurance company, it could mean incorrectly predicting instances of fraud within your client base. For a manufacturing outfit, it could mean failing to identify the correct bottleneck that decreases throughput time.

Bad decisions come in all shapes and sizes, but if you drill into each problem you’ll be able to identify a common thread: the inability to capitalize on data.

The Same Old, Same Old Approach is Broken

You might be thinking to yourself, ‘but we live in the golden age of data analysis. There’s never been a better time to be data.’ And you’d be right—data’s got it good.

Data is being collected at an unprecedented rate. Companies are increasingly looking towards data for reliable answers. And there are a stockpile of business intelligence platforms that store and visualize data in new ways. Nevertheless, organizations still find themselves making poor business decisions, misinterpreting data, relying on tedious manual efforts, and expanding the data literacy divide at every turn.

The “same old, same old” approach to data analysis is broken because it only allows companies employing this approach to reach a certain plateau with their data, business intelligence (BI) platforms, and the people interacting with those platforms on a daily basis. When organizations quickly assess how they analyze their data, it may appear to be an effective process. There are a few small wins here and there, but if they zoom out and take another look, they will find they’re on a data interpretation roller coaster.

same old, same old

We often hear a common story from our customers when they first approach us to discuss the rollout of a new BI dashboard. The account goes something like this:

  • Analysts create a new dashboard for internal team.
  • Analysts distribute and present how to use dashboard to internal team.
  • Internal team and analysts cheer and celebrate.
  • Internal team confidently uses dashboard to make decisions.
  • Analysts move on and start to work on new projects.
  • Internal team starts to lose confidence in decision-making as data changes over time.
  • Internal team calls analysts to confirm a trend in data.
  • Analysts confirm trend.
  • Internal team resumes using dashboard to make decisions.
  • Analysts move on and resume work on new projects.
  • Internal team starts to lose confidence in decision-making and makes a bad decision.
  • Chaos ensues.
  • Internal team asks analysts to make a presentation and explain data.
  • Analysts stop work on new projects, take screenshots of dashboard, and make a presentation.
  • Internal team and analysts (but mostly, only internal team) cheer and celebrate.

Rinse and repeat.

This process might be good enough for some companies, but organizations looking to get the absolute most out of their data, BI tools, and employees must realize this approach will always hit a ceiling. If poor decision-making from bad data interpretation festers, it will become worse—and inevitably more costly—over time if it’s ignored.

How to Fix It

From the moment a new dashboard is distributed form the hands of the creator, there is a risk for misinterpretation. Analysts simply don’t have the bandwidth to be a 24/7 watchdog and manually explain dashboards.

By integrating dashboards with natural language generation (NLG) technology, companies get the best of both worlds: accurate data interpretation and the elimination of manual reporting.

new approach

Our NLG platform, Wordsmith, automatically transforms data in dashboards into clear, natural language. As users interact with and explore a dashboard, Wordsmith serves as their “one source of truth” from day one. It’s like having an expert analyst guide you through your data explaining complex metrics, identifying common trends, and revealing new opportunities.

This approach allows analysts and data teams to stop extinguishing fires around data misinterpretation and to focus more on higher-value projects. It also speeds up the data understanding process and closes the data literacy gap, empowering non-data experts to evolve into self-serving analysts or citizen data scientists.

Companies invest heavily in making the shift towards becoming a data-driven organization. There’s just too much at stake to get this wrong. We want to provide data analysis that helps your company make strategic decisions, not fall into chaos.

  • Internal team and analysts cheer and celebrate.

Rinse and repeat.