Why Big Data Kills Businesses

February 28, 2017, 3:12 PM UTC
Oliver Burston Getty Images/Ikon Images

Big data has been anointed the savior of big business: it divines the future, reveals our path, and breathes new life into our venerable business models. But in reality, data kills. It kills projects, it kills money, and it kills time. Twenty-five years ago, data was growing at a rate of 100GB a day. Now, data grows at a rate of almost 50,000GB a second. And as the volume of data grows, the ability of companies to make sense of it diminishes, confounding rather than illuminating strategic decisions.

Time is our most valuable resource, and data drains it. We are on sensory overload. Every one of the thousands of emails, text messages, notifications, and alerts we receive daily are a distractionand therefore kills productivity. They inherently take us away from what we’re doing and force our attention to issues that may or may not concern us. In the same way, our business data is overwhelming and distracting us—throwing up barriers to productive decision-making.

Imagine a world in which every piece of information you receive would not only be relevant to you, it would find you at the right place and right time. How much more would you be able to get done every day? We expend massive amounts of energy just trying to keep up with all this information, leaving little time or energy for us to actually move the needle for our organizations. It’s overwhelming, and it’s crippling.

What’s more, data kills accuracy. Capturing more data will not automatically generate more value for a company. The more we collect data, the more we convince ourselves that we will be able to glean good insights from it. This modern take on the sunk cost fallacy is corporate quicksand. Data is only good when it results in accurate and relevant insights.

To be useful, information has to pertain to you, it has to be timely, and it has to be true. Unfortunately, when it comes to gleaning insights out of big data, the odds are stacked against you. Take for example the OfficeMax coupon that was addressed to “Mike Seay, Daughter Killed in Car Crash.” It’s not the quality of data that lies at the source of the blunder, but it’s relevance (and appropriateness). It’s virtually impossible to collect only the data you really need—and therefore, you are much more likely to be using data that you shouldn’t. Data that, in the context of what you’re trying to do, is mistaken or even damaging. Big data is good for connecting dots that would otherwise go unconnected. But in order for information to be pertinent, timely, and true, you need to understand its context.

And with 2.5 quintillion bytes of data accumulating every day, the likelihood of achieving a broad purview is low. You will either fall victim to analysis paralysis (and therefore, never unlock insights), or worse, you will glean false insights based on limited or misunderstood data. Without context, you run a high risk of chasing red herrings. Insights that seem game-changing can, in reality, be game-ending.

Data also kills agility. The traditional approach: suck all the data from your transactional systems into a data warehouse (or data lake or data pond), slap a few business intelligence systems on top, throw a few (dozen) analysts at it for a week, and dump everything back into Excel and Powerpoint. Rinse, repeat, and continue to fall behind. This type of data processing is a waste. With so much data to handle, it takes way too long to get any useful or actionable insights. There’s simply too much irrelevant data sitting between you and your decisions. You need to find a path through all that data to receive information that is tailored and customized to your business.

When I get in my car to head to the city, I want to know if there’s traffic on the way and how long it will take to get to my destination. I’d be a lot less inclined to use GPS apps if the recommendations were only as accurate as the last time one of my co-workers drove that route. An app such as Waze is powerful because it pools information from a large cross-section of all drivers. This centralizing of global data allows for contextual insights that benefit all users. Big data requires a similar approach. It’s time to stop accumulating business data within the four walls of your company and to start taking advantage of the true economies of scale of the cloud: not just shared infrastructure and applications, but shared data.

If you want to turn data points into valuable insights, you need to leverage a centralized, global platform that can ingest information from a multitude of internal and external sources. Outsourcing all this data collection, management, and analysis will allow this common platform to focus on the data science, while you focus on applying its tailored insights towards strengthening your core competencies and sharpening your competitive edge.

Two decades ago, there was a “No Software” movement that took the world from on premise to cloud. Today, we all need to embrace the “Data Kills” movement. It’s time to transition from collecting data to making it useful. It will free us to innovate while others are tangled in internal business intelligence projects, drowning in their own data lakes and “big data” water cooler prattle.

Nader Mikhail is founder and CEO of Elementum.

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