Analytics vs. Data Visualization
There is no doubt that any legitimate analytics solution should offer various forms of data visualization, displaying your information in a colorful array of pie charts, bar graphs, timelines, and the like. Such visuals make it far easier for users to absorb information and identify trends. However, any tool used to visualize data will fall short unless used in conjunction with proper analytic principles and preparation. In other words, a solution that offers only data visualization (no matter how pretty those visualizations might be) is not truly providing analytics.
Consider the following example:
You have a bar graph that displays the overall batting averages of every player on a baseball team. Sounds simple enough, doesn't it? But there are influential variables (an important and relevant set of data) that these visualized averages are not taking into account—
- Wind speeds/directions – how is each batter's game average affected by wind
- Weather conditions – are certain batters' performances affected by specific weather
- Locations – perhaps specific batters have poorer performance at some stadiums and do better at others
- Innings – perhaps specific batters perform better in early innings and worse in later innings, or the reverse
See? Visualization is nice. It is pretty. But what if that is all it is? If the data being visually presented is not providing actionable insights, is it doing much good? Not really. Even the most stunning data visualization is more or less meaningless if not coupled with thorough, accurate, and exhaustive data analysis. This can be difficult to achieve… unless, of course, your company has its own team of data scientists and/or analysts on the payroll. If you do, you are very lucky. But chances are that you do not. And, honestly, you do not need to… you just need a robust, end-to-end automated analytics solution that can manage and validate data for you - before it is visualized.
An automated analytics solution means that the bar graph in the above example is just a foundation, a central place from which you can drill down into all the outlying variables that would be absent in a simple visualization. Click on one specific player in the graph, and you are navigated to more focused visuals that portray the player's performance averages when confronted by any number of influential variables. These offer actionable insights, allowing for decisions related to training or scheduling. Perhaps a player seems to perform worse in high heat weather, and so more training to acclimatize that player to hot weather may be needed. Perhaps players who do well in early innings should be put at the front of the batting order, while those who perform better in later innings should be placed near the end of the batting order.
So, as you can see… visualization can be useful. What it cannot be, however, is a replacement for true analytics. Data must first be gathered, validated, arranged, and interpreted by a valid analytics tool. Numbers can't just be crunched and shoved into a nice graph without any insight.
If you are using data visualization without analytics… then all you really have is a very pretty (and extremely expensive) calculator.
Contact us if you would like to discuss how we can help make you more successful with data analytics.