The problem is… Businesses are facing big data that is estimated to double in volume every 1.2 years. The consensus is that data visualization is a powerful tool to show trends and represent simple data but how can you explain the results of complex analysis of big data or predictive or prescriptive analytics? What are the limitations of data visualization?
Data visualization tools help represent data results in pictorial/graphical formats. And while data visualization tools are meant to help analysts see trends and understand data, there are significant limitations that can become problematic as data sets grow. Amongst these problems, below are the top 4 limitations of your conventional data visualization tools and solutions to those problems.
1. Data visualization tools show but they don’t explain:
While data visualizations can be generated in real-time, they do not provide any explanations. In fact, the process through which companies draw insight has not changed in the last 30 years. Analysts look at data and then write reports. This process is too slow for the market and too costly for the company. At the same time, data visualization tools expect the user to be an expert in all of the data and all of the corporate best practices. Natural Language Generation (NLG) offers a solution adopted by many business. NLG automatically writes narrative explaining the insights and the drivers behind changes in the data.
2. Different users draw different insights:
Two different users confronted with the same data visualization may not necessarily draw the same conclusion, depending on their previous experiences and particular level of expertise. This presents several problems for companies. On the one hand, certain users could be erroneously drawing conclusions which cost the company money and on the other, in highly regulated industries, users’ incorrect conclusions could actually put the company at risk.
3. No guidance:
What if the user interpreting the data lacks expertise and/or training? He or she could make mistakes that may affect the entire company. At the same time, analysts could provide clients with incorrect or substandard advice. Even systems with Natural Language Query, expect the user to know what they are looking for. This works with simple data but the industry trend is towards big data, data lakes and complex analysis. It’s so complicated you might not even know what you don’t know, to paraphrase an American Defense secretary. The answer is so simple that its easy to miss. We don’t speak data, we speak English, so software that explains the data to us in plain English. This is the value of Natural Language Generation software, the last mile in the data analytics workflow.
4. Data visualization provides a false sense of security
Graphics are great for conveying simple ideas fast – but sometimes, they are just not enough. If words could be replaced by pictograms, they would have been a long time ago. To express a complex situation, sentences and phrases are required along with a system that is smart enough to articulate its reasoning process. Importantly, language also makes sure the end user really understands. Graphics can make users think they are making data driven decisions or think they fully understand the data when in reality they are only seeing a picture but they don’t know the full story.