Skip to main content

Weekly AI Digest: Automating Data-Driven Decision Making

A number doesn’t always tell the full story. Very few departments, even people, are numbers-oriented and even then, a single number can be interpreted a wide variety of ways. This is a fundamental problem we see with data-driven decision making. Businesses are realizing the importance of incorporating real-time information, but are unsure of how to achieve this effectively.

Companies should “consider data as more than numbers” as presented in an article from the Forbes Council, “The Future Of Data-Driven Corporate Cultures.” Numbers alone can provide a picture, one that can be open to interpretation. Data needs context in order to become useful.

 

Are You Still Manually Drawing Insights? 

For many companies, the data-to-decision workflow looks like the figure below: data collection, data analytics, and the process of drawing conclusions or insights from data. At the moment, multiple tools are required to facilitate this workflow from start to finish.

Figure 1: The Conventional Data-to-Decision Workflow

Figure 1: The Conventional Data-to-Decision Workflow

 

Many businesses today, despite investing in real-time data streams and state-of-the-art data aggregation tools, still manually draw insights from data. Often, this task is left to the end users or data analysts. This creates a bottleneck when trying to draw insights – causing delays and decisions to be made with outdated information.

With the potential shortage of data experts and as companies collect more data, relying on a few individuals to interpret and analyze information is expensive, slow, and lacks consistency. The truth of it is that the combination of too much data and too little time is a recipe for mistakes.

 

The Future of Data-Driven Decision Making

With the ability to generate data-driven text automatically (otherwise known as Natural Language Generation), companies can bring automation to the last mile in data analytics. Today, NLG can analyze data, extract insights, and explain what these insights mean— in plain language—so that anyone can understand. Some NLG software can go even deeper and explain the drivers behind a dataset as well as next-step actions. Read more about the last mile of analytics and how you can bring automation to data-driven decision making.