The hype surrounding the big data movement is enormous. The way some organizations talk about it, big data can solve every single problem a business faces and provide guaranteed success well into the future. While the hype may be a tad overblown, make no mistake that big data carries with it numerous benefits that can be used to boost business. But simply having big data is no guarantee of anything. Unfortunately, too many companies have bought into the mindset that as long as they possess big data and analyze it on a regular basis, that’s all you need to do to find success. The truth is much more complicated than that. Yes, big data can be valuable, but if it’s not being used to help your company actually make more money, it’s really more of a waste.
Big data solutions are plentiful, but the main point of using big data analytics should be to grow your revenue. If there’s no revenue growth, big data serves no purpose. Many companies see big data as an end in and of itself, spending vast sums of money collecting meticulous information on every aspect of their businesses, from their operations to customers. While this strategy is certainly not a bad one, companies often find that their sales have not increased and revenue remains stagnant. These aren’t small businesses we’re talking about, either. Walmart and Best Buy have huge databases filled with tons of data about customers, but there have been no signs of sales improving. All that time, effort, and money that has gone into collecting so much data has produced nothing of true value.
These examples might leave business owners scratching their heads. If big data can’t help these giant corporations, then who can? When it comes to big data, however, it’s not necessarily how much you collect but the type. You can have all the data in the world, and it won’t translate to better sales and more money. Instead, businesses need to focus on relevant data, the kind that actually affects revenues. It also requires knowing how big data can add value to a business in the first place. Determining which data is relevant and which isn’t can be a challenge on its own, which is where talented data scientists come in. With the right expertise and experience, skilled data minds can separate the good data from the bad and know where the most profitable data is contained.
Take the usually cited example of collecting customer information for marketing purposes. Today, companies can find out tremendously detailed information about consumers, from their marital status to what colors they like the most. Pinpointing what information is the most relevant for increasing sales is the important step, one which many organizations seem to ignore. One case of how a gaming company utilized big data and ad hoc analytics can serve as the perfect example of how to use big data right. The game developer collected data on gamer web logs and profiles and figured out the factors that kept them motivated to play games longer while spending more money on games. By maximizing the factors discovered through big data, they eventually increased their revenue from $50 million to an impressive $600 million.
That’s not the only case of companies using big data specifically to increase revenue. A credit card company collected data on high-value customers in order to understand their behavior. From that data analysis, they were able to figure out what they responded most favorably to, eventually crafting more targeted marketing campaigns. The result was a higher conversion rate, a decrease in the amount of customer churn, and annual savings of more than $3 million. This was big data being put to use for the purpose of making money, and the results speak for themselves.
While big data has a lot to offer, companies have to understand that it takes more than data to make money. You can analyze information all you want, but to make your investment in big data analytics really count, you need to put it toward increasing sales. If the results aren’t there, strategies need to be changed. If revenue decreases, you’re not using big data effectively. Don’t buy into the big data hype without realizing that the only way it makes sense is if it is used to get more money out of it. An investment that doesn’t improve the bottom line simply isn’t an investment worth having.