Big Data: The organizational challenge

Filed under: Analysis |

Samsung uses it to power the content recommendation engine on its newest smart TVs. Progressive Insurance relies on it to capture driving behavior and determine customer risk profiles. LexisNexis Risk Solutions uses it to identify individuals andfamily relationships, thus helping financial institutions and other clients reduce fraud.

It, of course, is Big Data—the mining and processing of petabytes’ worth of information to gain insights into customer behavior, supply chain efficiency and many other aspects of business performance. Industry analysts and media observers are hyping Big Data as the next big thing for every enterprise, and many companies have been rushing to climb on board. But is building an advanced analytics capability really worth the investment?

A recent Bain & Company study should put that question to rest. 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

This may explain why so many companies are now asking where they stand on Big Data vis-à-vis their rivals— and whether they’re missing out on a new and essential competitive tool.

To get in the Big Data game, a company needs three kinds of table stakes: large quantities of information in a format allowing for easy access and analysis; advanced analytical tools, such as Hadoop and NoSQL; and people capable of putting those tools to work. But table stakes alone won’t help you win, because Big Data isn’t just one more technology initiative. Rather, the analytics leaders embed Big Data deeply in their organizations. It’s the only way to ensure that information and insights are shared across business units and functions, and that the entire company recognizes the synergies and scale benefits that a well-conceived analytics capability can provide.

Let’s look at what’s involved.

Ambition. Leading companies begin the embedding process by spelling out their ambition—for instance, We will incorporate advanced analytics and insights as key elements of all critical decisions.They also answer the question: To what end? How is Big Data going to improve our performance as a business? There are four areas where analytics can be relevant: improving existing products and services, improving internal processes, building new product or service offerings, and transforming business models. For example, Humana, the insurance provider, is using Big Data to transform its business. Using claims data, the company can determine who is likely to end up in a hospital for preventable reasons and then intervene early.

Horizontal analytics capability. With ambition defined, Big Data leaders work on developing a horizontal analytics capability. They learn how to overcome internal resistance, and to create both the will and the skill to use data throughout the organization. It’s a big job. Leading companies typically define clear owners and sponsors for analytics initiatives. 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, and they work hard to trace the causal impact of Big Data on the achievement of these targets.

An organizational home. Big Data leaders then create an organizational home for their advanced analytics capability. Companies with deep analytics skills and an emphasis on experimentation and innovation, such as Google and Progressive, can rely on a generally decentralized approach. Many others have found that a Center of Excellence offers the most advantages and the fewest limitations. A well-functioning CoE enables cross-business-unit access and sharing of data. It takes responsibility for supporting and coordinating every initiative from a business unit, thus providing synergies and scale benefits. On the corporate level, the CoE serves as the go-to organization for analytics strategy and insight support. It sets the road map, and it establishes and maintains privacy policies.

How to get started in Big Data? A good first step is to benchmark your industry and determine your company’s current position in analytics compared with that of your chief rivals. This exercise will help you determine the investment necessary to improve your relative position. If you are significantly behind the competition, you will have the kind of burning platform that is often required to create and sustain change.

You can then begin experimenting, testing hypotheses to learn where and how advanced analytics is most likely to help your business. This type of review will help you determine your Big Data ambition, embed a culture of analytics and decide where Big Data’s organizational home should be.

Oliver Merkel and Rasmus Wegener

2 Responses to Big Data: The organizational challenge

  1. 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

    The question is was “BIG Data” the cause of these behaviors our was the use of big data the result of these behaviors?

    What happens if these behaviors are not present, is Big Data worth the investment?

    Does Big Data investment result in behavior changes such as “execute decisions as intended”?

    Richard Ordowich
    March 11, 2014 at 3:28 pm

  2. Very interesting article on Big Data. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. Their built-in Machine Learning Library and Matrix processing algorithms can assist with business intelligence and predictive analytics. More at

    Big Data Queen
    March 11, 2014 at 11:50 pm

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