{"id":656152,"date":"2013-05-04T13:30:25","date_gmt":"2013-05-04T17:30:25","guid":{"rendered":"http:\/\/gigaom.com\/?p=642005"},"modified":"2013-05-04T13:30:25","modified_gmt":"2013-05-04T17:30:25","slug":"careful-your-big-data-analytics-may-be-polluted-by-data-scientist-bias","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/656152","title":{"rendered":"Careful: Your big data analytics may be polluted by data scientist bias"},"content":{"rendered":"

Expectations surrounding the future of \u00a0big data<\/a> range from the just huge to absolutely enormous \u2013 a reflection perhaps of both its real inherent potential and all the massive hype. Certainly though there is no dispute that companies can reap big benefits from exploring patterns found in the data they already generate and collect. Further, depending on the algorithms used, machine learning can even serve as a real world crystal ball: There are countless examples, but the story about Target\u2019s ability to predict pregnancies<\/a> by analyzing customer consumption patterns, or how well known mathematician Nate Silver predicted the winner in all 50 states during last November\u2019s presidential election<\/a> are two poignant examples.<\/p>\n

But the fact remains that big data can only ever be as good as the machine learning that is used to provide insight, and even the most sophisticated machine learning techniques aren\u2019t omniscient \u2013 the old adage “garbage in, garbage out” sums up this dilemma perfectly. Businesses planning to invest in big data science, with the hopes of reaping the potential wealth of insights available,\u00a0must at all costs avoid introducing bias into the process \u2013 or risk jeopardizing everything.<\/p>\n

Data bias syndrome<\/h2>\n

Data bias comes in many forms. It can come from poorly defined business domain objectives. Or, it can come from opting to gather data that are easy to collect rather than data that are most informative. Data scientists can also receive data that have been biased by incorrect assumptions by the domain experts. (And as a footnote, the recent example of the austerity economics Excel scandal<\/a> shows how a minute data error can have cascading and devastating effects.)<\/p>\n

Likewise, data scientists themselves are not immune to bias. Some can run afoul of their own preconceived notions about business domain \u2013 too much knowledge can cause one to filter out data that may actually be helpful.\u00a0 Scientists with deep experience in a particular data set may develop too much reliance on pre-existing algorithms without re-examining validity for a particular use case.<\/p>\n

Finally, data quantity is a common problem. Intelligent learning requires abundant data, and often the data available are not sufficient to draw accurate conclusions \u2013 a problem known as data sparsity. This may sound unbelievable considering that data volume is doubling every two years according to an EMC study<\/a>,\u00a0\u00a0but there\u2019s a difference between a dense data set populated by similar data points, and the far more diverse sets of user data points we find in the real world. In these cases, the gaps in the data are filled by machine learning algorithms that may inherently be biased, based on assumptions made by the data scientist when designing the algorithm. The trick is to find the right balance between unbiased data exploration and data exploitation.<\/p>\n

Removing bias<\/h2>\n

As companies bring data science in-house or purchase tools that act as a data abstraction layer, the need to address data bias becomes more immediate. The smart move is to build bias-quelling tactics into the data science process itself. Here\u2019s how:<\/p>\n