The Disadvantages of Big Data
In the world of business we tend to be hesitant when it comes to anything “big.” A big idea in business could produce a great success, but it could also lead to great failure, which could be detrimental to a company’s health in numerous ways. For this reason, “big” often gives us a pause. Remember Enron? The idea held significant potential for the traffic of every digital bit of information – an estimated $400 billion. Instead the company went bankrupt.
As the Enron story proves, the real disadvantage of ‘big’ is the inherent risk of failure. The other real disadvantage of ‘big’ is failing to take the risk and being marginalized by those who did, as it can lead to structural shifts in your market. Damned if you do, damned if you don’t!
Big Data has the same ambiguity. Is it really for us? Does my company have the need? Do we really have Big Data? Do we have the resources to do Big Data projects? What will they cost? One can endlessly question the benefits vs. the risks. The more questions you have, the riskier the project will seem.
Rationalizing the negatives brings to mind another disadvantage of ‘big.’ It frequently allows decision makers to slip into a wait-and-see mind set. For example, we will wait until our competitors do it, learn from their mistakes and implement our own project when the time is right. The problem with this approach is the assumption that you can quickly assemble a team of A players to close the gap opened by competitors who embraced the risks of ‘big.’ Every soccer coach knows that this is not the case– it takes a lot of searching and player development to build an A team. And this is even more important for newly emerging disciplines where talent is scarce and much of the skills and knowledge are acquired on the project.
So, the issue is not whether to do or not to do, but how to start. Most companies struggle to find a use case, as often the hype and great use cases are related to the analysis of machine-generated data, and few companies have access to such data. However, there is another source of Big Data that every successful company has: customers.
Customer data contains a wealth of information about the brand, products, customer service perceptions, consumer sentiment and satisfaction and their impact on sales. It is stored though silo applications, and therefore it appears to be small data. But to gain insight, companies must analyze all customer data in its entirety. Put customer data from all sources together and what do you get? Big Data! It is both structured and unstructured, as most of the posts in social media, blogs and emails are unstructured. This Big Data is most relevant for companies, as it helps them glean insights into behaviors, trends, product issues, and even new product ideas. The systems and tools to analyze and mine this type of Big Data are available. Changing your approach to it is the risk you need to take.
UPDATE:I will be speaking on this topic in select cities across Europe along with BARC Research and MapR. To register and attend, please click below. All attendees will eceive a complimentary copy of BARC's report, "Big Data Use Cases: Getting Real on Data Monetization," and an exclusive invitation to a half-day brainstorming session focusing on using big data and smart analytics to monetize data analysis.
September 29 | 09:00 - 11:30 CEST | Paris
October 1 | 08:30 - 13:30 BST | London
October 6 | 09:00 - 13:30 CEST | Amsterdam
October 7 | 09:00 - 13:00 CEST | Frankfurt
Hope to see you there.