Business Intelligence Gets Amped: The Data Viz Evolution
Business Intelligence Gets Amped: The Data Viz Evolution

By on in Analytics

Business Intelligence Gets Amped: The Data Viz Evolution

Data visualization has advanced enormously over the past few years, with tools processing vast amounts of information at faster speeds to turn complex data into graphically displayed, comprehensible insights.

 

The Rise (and limitations) of Dashboards

As we advanced from the age of Excel to that of Google Analytics, dashboards quickly gained popularity. We got used to being able to scan a selection of key performance indicators (KPIs). However, while this was a big improvement over spreadsheets, understanding why or how we arrived at a certain point is way more complicated than what a dashboard could show us.

Making marketing or business decisions based solely on KPIs can be risky—especially if you have a diversified plan and you’ve taken a wide variety of actions over time. The greater the number of variables, the riskier the correlations.

 

Even the Best Dashboards Are No Substitute for Analysts

Dashboards can provide great information at a glance, but they do not provide actionable insights. A skilled analyst is necessary to see data in a holistic way, help draw conclusions and guide decisions. The problem? That can be time-consuming and, therefore, expensive.

Any mathematician, accountant or economist will tell you that numbers are powerful storytellers. And while trained eyes may not need to have digits plotted graphically to see patterns, cycles, correlations or trends, as data quantity increases, even they can benefit from a little technology assist.

For those of us not trained to see the meaning in data, understanding what the numbers are trying to say is likely to be even harder. Thankfully, the advancement of artificial intelligence and algorithms have helped data viz tools evolve to better support storytelling, contextualized data and decision making.

While business intelligence has traditionally used rule-based programs to deliver static reports, today’s augmented analytics use AI techniques such as machine learning and natural language generation to automate data analysis, visualization, querying and insight generation. (If you want to go deeper, Dataconomy has a great post explaining how this works.)

 

The Bottom Line

There’s no doubt that the ability to analyze, contextualize and provide deeper meaning around KPIs can increase business leaders’ speed and confidence in decision making. However, not everyone has the budget to bring on professional analysts. That’s where data visualization tools play an important role.

Implementing these tools, however, can be a complex task—especially if the plan is to merge data from multiple sources, including third-party data. The process involves significant planning and security diligence to ensure that you have selected and implemented the right tools properly, so expect it to take time. If you want to stay ahead of the game, get familiar with the top trends in data and analytics and start moving now.