Contrary to popular belief, big data isnt some crystal ball to the future, and unfortunately, many organizations feel discouraged and deceived by all the hype surrounding big data. Thats disappointing because even if data isnt a guaranteed recipe for success, its incredibly useful in helping to build strategies and shape decisions. Organizations just need to temper their expectations, realize what the limits of big data are, and learn what data analytics can actually offer.
What Big Data Cant Do
Who should you vote for in the next election? How should criminals be punished? Where should I go to school? Big data analytics struggle to provide quantitative answers to qualitative questions. Meaning, data can tell you who has the best chance of winning the next election, but we dont vote for who we think will win, we vote based on values.
With this in mind, organizations shouldnt expect data to help them make value judgements and solve problems to qualitative issues. That could be disastrous. Data analytics will never replace human emotion, which can be incredibly important in many decisions.
What Big Data Can Do
However, just because big data cant make value judgements, doesnt mean its useless. In fact, almost every industry is using data to shape decisions, increase productivity and learn even more about their customers and audiences. But instead of just stating the promises youve heard hundreds of times, why not look into some real examples of big data usage. Lets start with crime prevention. There are a number of police forces and law enforcement agencies across the United States using crime statistics to pinpoint criminal hot spots. By devoting resources to these areas, law officials are able to make the most of their resources, anticipate criminal activity, and stop it before it happens. Already this has proven to reduce the crime rate in these areas.
Big data is also being used within financial institutions to crack down on fraud and money laundering by creating algorithms that detect anomalies within transaction records.
Or what about the world of commerce? E-commerce sites like Amazon have made tremendous usage of big data insights. Every time you sign into your Amazon account, it tracks your searches and purchases. That information allows them to recommend other things you may be interested in, or offer you deals and promotions based off purchasing trends.
Finally, marketing and advertising companies are using big data to further segment, or micro-segment, audiences. These small groups mean companies can create more targeted messaging and strategies. The more specific a message is, the more likely a person is to respond to it. Big data helps take marketing cases even further. Not only does it allow companies to tailor messaging, but it can also help determine the most effective way to deliver messages. Understanding which devices people prefer to use, like smartphones, tablets or laptops, will allow marketers to optimize content for them and increase conversion rates.
Living With Uncertainty and Making Decisions
Obviously no one likes uncertainty. Wed love to know if our new product will sell, or if our digital campaign will lead to more sales. Unfortunately, that doesnt exist with or without datas help. Big data was never intended to work in certainties. Its built on statistical analyses, so by nature there can never really be a 100 percent guarantee. This is even more true when trying to analyze the unpredictable nature of human behavior. However, this uncertainty isnt such a bad thing. In times past, advertisers and executives relied on their gut and experience to make important decisions.
Today, weve become so reliant on computers that we often cant spot mistakes and blindly follow what the numbers say. Thats a dangerous path. As powerful as computers are, and as advanced as analytical platforms have become, they perform at their best when guided by humans. The uncertainty of data forces decision makers to be involved. Experiences and instincts are necessary as data is only as helpful as the inputs and their interpretation. Algorithms help us make predictions, but those predictions are more likely to be true when overseen and analyzed by experienced leadership. The best decisions are made when instincts are paired with data insights.
