Data Visualization & AI in Business Intelligence:

The Lines are Beginning to Blur – Here’s What You Need to Know

It’s a simple question: 

“What’s the difference between a data visualization tool and an artificial intelligence (AI) tool?” 

Unfortunately, it’s not a simple answer, as the dividing lines between the two are starting to blur – particularly on the AI side of things. 

So, let’s start by defining the easier of the two: data visualization 

A data visualization tool is one that takes data – from applications like ERP and CRM, spreadsheets, or other sources of data – and presents them in visual formats that are easy (or at least easier) for people to understand. Typical formats include text-based reports, on-line dashboards, as well as charts and graphs. 

Another way of thinking about data visualization tools is that they provide the “presentation layer” for the analysis of metrics and other key performance indicators. And it’s on the subject of analysis that AI begins to enter the picture. 

How AI Works with Data 

AI tools analyze data, retrieve information, identify trends, spot anomalies, generate forecasts, and uncover potential business insights. AI is rapidly becoming the ultimate data retrieval and analysis tool, capable of identifying trends, spotting anomalies, and turning data into potential business insights. However, AI tools are generally not optimized for presenting information in the polished, governed, and highly interactive formats that business users often expect. 

However, if you look closely at representative samplings of both AI and data visualization tools, you’ll notice that each is creeping (at an ever-increasing rate) closer to the other. For example, popular data visualization tools (such as Power BI and Tableau) now contain AI functionality that enhances user experience and improves query performance. 

Likewise, AI solutions (such as Claude and OpenAI) are beginning to include more data visualization capabilities. Claude, for example, can now generate high-quality charts, dashboards, and visualizations. While these capabilities are improving rapidly, they still do not match the depth, governance, scalability, and presentation flexibility offered by dedicated platforms such as Power BI and Tableau. So – what should someone who is wanting to improve their analytics take from this potential – and probably eventual — merging of data visualization and AI functionality? 

Three things: 

• AI tools are moving fast into the data visualization space and data visualization tools are also incorporating BI-focused AI features. 

• As of today, if you want top-shelf AI married to top-shelf data visualization, your best option is often to adopt both approaches and leverage the strengths of each. 

• Although AI and data visualization address many presentation use cases, neither of these technologies is primarily focused on providing curated, governed, high-performance, and high-quality data. The last of these is the proverbial ‘elephant in the room’. 

If the data being analyzed: 

• Contains errors or inconsistencies 

• Hasn’t been cleansed or validated 

• Is incorrectly defined and standardized (“modeled”) 

• Or lacks defined security and accessibility 

… even the best AI and data visualization technologies will struggle to produce KPIs and insights you can trust. 

We encourage you to do your research on AI and data visualization technologies and choose the solution (or solutions) that best fit your organization. But don’t stop there – because as important as data retrieval and data presentation is, the bottom line is your data. If you don’t take care of it, everything that follows will fail to deliver.