- While Big Data enables statistical models to be built and predictions to be made, it offers too much information and provide no explanations about the whys and the wherefores of the data.
- Deep Data explains the facts: why what happens actually happens and what the true drivers of purchases really are.
As that Pirelli tire commercial says, “power without control is good for nothing”. The same holds for Big Data. “Data gathering without control is good for nothing”.
This is the second lesson that companies that want to become Data Driven Businesses learn. Companies have gathered petabytes of data in the hopes that they would indicate to them what products to offer, to whom, when, where, how and why. But data in and of themselves doesn’t say much. You have to get them to tell.
Big Data is in charge of asking the questions and Deep Data is there to answer them.
The goal with which Big Data strategies are developed is not to explain what happened or to make predictions. What’s more, Big Data alone is not able to provide anything other than the numbers. In other words, with Big Data you can see:
- The inhabitants in an area
- Their social and economic level
- Whether they are on Facebook or not…
- What their annual consumption usually is…
But they don’t tell you that you should look for all of these metrics if you want to predict the amount of sales that your business campaign will have, nor do they tell you what is missing. They don’t tailor the data to your business objective or seek peripheral information to enable you to understand not only which specific action will work best, but why. They do not replace human reasoning.
Enter Deep Data
This is exactly why you need Deep Data. Data that provide you with a personal dimension to explain consumers’ purchasing decisions. So:
- Before you had their zip code. Now you have their personal property tax information.
- Before you had their annual consumption. Now you know what they spend each portion of their budgets on.
- Before you had their social-economic status. Now you have access to their level of indebtedness and their bad debts.
With Big Data you knew where there were people who could buy your product and what they wanted to buy. With Deep Data you can make distinctions between who will buy your product and who won’t, what, and why.
From Big Data to Deep Data using an example: videogames
New business models in the gaming industry are a perfect example of the proper use of Deep Data.
When the first videogames came on the market, companies could only know what traditional market studies could tell them.
Then came consoles connected to the Internet and platforms like PlayStation Network and Xbox Live to enable multi-player games… and they sucked up each player’s data like vacuum cleaners.
Data on which customer was playing which games came to define upcoming launches, and that was only the first step. Then came the other services like subscriptions and streaming services that not only became a business line but also provided a further dimension of information about each user: what they consumed when they were not playing videogames and how much they paid for it.
With all of this information, for years franchises like Call of Duty have been adapting their launches to the most recent fads and hedging their bets using elements they know their customers will like. They have done so to the extent that they have redefined the very notion of videogames:
- By creating titles that combine cartoons, sports and tournaments with shooters in successful mixes like OverWatch
- By redefining their content and through pricing strategies with subscription models, expansions, updates and customized packages, all for pay.
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