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Early adopters of Big Data analytics have gained a significant lead over the rest of the corporate world.

muddy cross country race

Early adopters of Big Data analytics have gained a significant lead over the rest of the corporate world. Examining more than 400 large companies, we found that those with the most advanced analytics capabilities are outperforming competitors by wide margins. The leaders are:

  • Twice as likely to be in the top quartile of financial performance within their industries
  • Five times as likely to make decisions much faster than market peers
  • Three times as likely to execute decisions as intended
  • Twice as likely to use data very frequently when making decisions

To get in the Big Data game, a company needs three kinds of table stakes. The first is the data itself: large quantities of information in a format allowing for easy access and analysis. Most large companies already have this—in fact, they generally have more than they can use. The second is advanced analytical tools, such as Hadoop and NoSQL. Both proprietary and open-source tools and platforms are widely available these days— all you need are people capable of putting them to work. That brings us to the third, and usually the most challenging, set of table stakes: expertise. Advanced analytics requires staff with state-of-the-art skills in everything from data science to worldwide privacy laws, along with an understanding of the business and the relevant sources of value.

But table stakes alone won’t help you win, because Big Data isn’t just one more technology initiative. In fact, it isn’t a technology initiative at all; it’s a business program that requires technical savvy. So you can’t just add more capacity and expertise, and expect your IT or marketing functions to begin generating data-based insights. Even if they did, the rest of the company would be unlikely to act on those insights.

As the analytics leaders have discovered, succeeding with Big Data requires a different approach: You need to embed Big Data deeply into your organization. It’s the only way to ensure that information and insights are shared across business units and functions. This also guarantees the entire company recognizes the synergies and scale benefits that a well-conceived analytics capability can provide.


Most companies are opportunity-rich when it comes to analytics, and large enterprises can pursue multiple avenues, either simultaneously or sequentially. Still, nearly every company can improve its trajectory by determining priorities and picking the right angle of entry.

Horizontal analytics capability

Big Data leaders work on developing a horizontal analytics capability. They learn how to overcome internal resistance, and create both the will and the skill to use data throughout the organization. They provide incentives for analytics-driven behavior, thereby ensuring that data is incorporated into processes for making key decisions. They create targets for operational or financial improvements. They work hard to trace the causal impact of Big Data on the achievement of these targets.

An organizational home

The Big Data leaders then create an organizational home for their advanced analytics capability, often a Center of Excellence (CoE) overseen by a chief analytics officer. Note that in none of these models does IT own Big Data. While IT often plays a critical role in providing and maintaining the infrastructure and tools required to run Big Data analytics, most companies find that it’s a mistake to have IT own or manage the business adoption capability.

Getting started

Many companies are already dipping their toes into Big Data waters. But given the complexities we have discussed—in particular the need to anchor analytics capabilities in the organization—toe-dipping isn’t likely to produce significant insights. That’s why only a select few, so far, have made substantial progress. Right now, many of these leaders are pulling even farther ahead of competitors, so others are playing the necessary game of catch-up.

Stashed in: AMZN, Data Analytics

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