Big Data Bias Syndrome: Don’t Be a Victim!

So how do you remove the bias? Whether retailers hire their own data scientists or purchase applications that allow merchandisers and other business personnel to interact directly with the data, bias prevention techniques need to be built in from the outset. Follow these steps:

1. Data selection: Carefully select relevant data and take the time to explore which combinations of inputs produce the most accurate results. This may include the use of heuristics, or educated guesswork, to fill the sparsity gaps. Combining analytics expertise with business and targeting expertise — two attributes that merchandisers have in spades — greatly improves the chances for optimal results. This puts the data scientist and the operational expert on the same team, with the same goal, with each bringing different, complementary skills.
2. Data improvement: Aim to improve the data sets. Don't settle for what you have; instead, look for ways to fill any data sparsity gaps with real information. Adding an intuitive user interface to a software service that combines targeting expertise, data aggregation and the ability to use heuristics intelligently can also help to counteract data sparsity by providing the optimum "fuel" for machine learning.
3. Analytics feedback loop: Build a continuous feedback loop into the analytics process so that output is always optimized for maximum performance. This will help modify and adapt algorithms to changing business requirements, or seasons in retail's case, and keep machine learning fresh.

Exploration is the key to finding the right balance of hard data and heuristics in order to drive machine learning to optimal results, so don't be afraid to boldly go where no retailer has gone before! But whatever you do, don't ignore the bias factor in your big data analytics projects. Build in safeguards against bias from the outset, and smart machine learning will be the closest thing to a silver bullet you're likely to find this side of nirvana.