Does Big Data Still Need the Human Touch?
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Visualization technologies offer individuals an analytical window into rapidly expanding – and increasingly opaque – Big Data stores. Yet, given that analytics and other automated data management tools can analyze more data efficiently, is human analysis of data still worth the effort?
Data volumes have become so large that oftentimes only advanced analytics and other data analysis technologies can make sense of them. However, some argue that humans – armed with powerful data visualization tools – can glean valuable insights from Big Data that escape even sophisticated analytics software. While automation delivers essential processing speed and efficiency, can it ever fully replace human judgment and insight?
Explore all sides below by clicking on each button:
|Automation lightens everyone’s work load.
We have the technology to track, cleanse, transform and summarize data to make decision-making easier. Why overwhelm people with minutiae if computers can do the work for them?
|Human analysis is still important.
Humans should be able to see the raw data and to control data analysis. Why? Because raw data often contain outliers and summarizing it might obscure patterns that a computer may not recognize as important.
|Interactive, colorful 3-D models are pretty and stylish, but they can be a dangerous distraction.
The wrong choice of variables, ranges and scales might create an overly simplified and misleading picture.
|Interacting with and visualizing Big Data is integral to understanding – and trusting – analysis results.
Excessive dependence on computer models can create blind spots. Humans should check the assumptions of a model and verify that the right questions are being asked in the first place.
|Big Data’s scale often overwhelms visualization capabilities.
Big Data can often involve billions of records, yet even effective visualization tools can’t plot more than a few million data points on a single display.
|Visualizations can highlight what’s important.
Just as a database with a multitude of rows can be queried and summarized, good visualizations make it possible for users to apply filters to data and drill down on items of interest, and then communicate their findings more efficiently at the appropriate level of abstraction.
|Humans only introduce the potential for error.
Among other cognitive biases, people may often be overconfident in their initial impressions and only see what they want to see. Better to let a computer make the decisions objectively and consistently.
|Humans may catch what a computer can’t see.
Human eyes, with their ability to detect symmetry and adjacency, can see patterns in data that even sophisticated automated recognizers can overlook.
David Steier, Director, Deloitte Analytics, Deloitte Consulting LLP
Some people say to automate it all. Data volumes are too big and the data changes too rapid for manual analysis to add value. After all, they posit – how many Wall Street traders still base buy-sell decisions on data they crunch in spreadsheets?
Yes, it’s true that the sheer volume of data being generated makes most modes of traditional manual analysis unrealistic at best. However, that doesn’t mean that we should simply automate the entire process and walk away. When analysis emerges scrubbed and packaged from a black box, many naturally curious individuals will likely disengage or question its validity. There will likely be people who want to get into the data and dig deeper. They want to understand how it is being processed and to look for patterns and insights. By deploying visualization tools, some of these inveterate data crunchers are able to recognize valuable symmetries, relationships and adjacencies in data – particularly raw, unstructured data – that analytics cannot recognize.
Of course, there are some circumstances in which automation alone could be sufficient to the analytical task at hand. For example, algorithms quickly and effectively determine if a credit card should be accepted or declined in a retail transaction. Human intervention in such a routine time-sensitive process would likely bring the retail industry to a halt. In other circumstances – medical diagnoses, for example – the consequences of making incorrect decisions can be severe. In such cases, it may well be beneficial to “get under the hood” and question how and why data analysis technologies arrived at their conclusions.
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