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Separating Signal from Noise: How Meaning Makers Conquer Big Data


Smart application of advanced business analytics are not only driving growth but separating winning companies from the pack, our research shows.

Big data is a big deal. You can't walk through an airport or train station without seeing magazine racks crammed with publications touting the latest and greatest ways to create value from the ever-growing pools of information coursing through commerce.

What's more, the sheer volume of data generated by devices can be overwhelming. Every day, for example, we create 2.5 quintillion bytes of data — so much that 90% of the bits and bytes that exist today were created in only the past two years.

But is there any real value in mining and applying that data? Is big data just a fad—more noise than signal?

The Hype Is Warranted If Data Is Collected With Purpose

In an effort to quantify the impact of big data, we partnered recently with Oxford Economics and IT leadership guru Thornton May to survey 300 businesses in a dozen industries throughout the U.S. and Europe.

1. Over the last year, surveyed companies combined for more than $750 billion in economic benefits through business analytics (roughly $400 billion in increased revenue and more than $350 billion in cost reductions)

2. If the surveyed companies used all available analytics, respondents estimate they could gain an additional 11.9% (or $91 billion) in value

While these projected impacts appear enormous—a healthy dose of skepticism is never a bad thing—they are well in line with other estimates, including McKinsey & Company.

The key to cracking the code of business analytics is to find the story in the bits and bytes and derive tangible, actionable business insight. Finding meaning instead of "just doing analytics." The data, for instance, must tell a meaningful story and that narrative should matter to a real-life decision-maker. The story is what making meaning is all about.

Our view is that understanding and applying data and insights from customers, partners and employees—what we call Code Halos™—is already becoming a powerful competitive advantage. Companies such as Google, Amazon, Netflix and Pandora are winning their markets because of their refined ability to mine insight, decipher patterns and then make informed decisions on the data they ingest.

Code Halos: The Future of Data Science & Business Analytics

Meaning-Makers, Data Explorers and Data Collectors

In our survey, we identified three specific types of companies.

  • "Meaning Makers": 26% of those surveyed) : These companies integrate business analytics more effectively into their daily work, are more effective at using analytics tools, create well-defined teams that focus on wringing value from data and derive value in at least five of 10 key areas (ranging from basic financial reporting to sophisticated predictive modeling). These businesses also believe they are ahead of their industry peers in using data analysis.

  • "Data Collections": (24%) These companies do little to nothing with the data they capture, largely because they don't know what to do or are overwhelmed by it.

  • "Data Explorers": These companies dabble in business analytics—sometimes to their advantage, sometimes without knowing why. Many are overwhelmed by the quantity of data, unlike Meaning Makers that revel in it.

By studying the three groups we learned:

1. Meaning Makers anticipate outsized revenue gains. In fact, over the next two years, 15% of Meaning Makers expect to see revenue growth of more than 10% (about three times more than average expectations).

2. Data Explorers expect solid growth but miss the potential upside. Nearly half (49%) of these companies and 42% of Data Collectors anticipate a revenue growth rate of up to 5% over the next 24 months. But 15% of Meaning Makers anticipate sustained growth of greater than 10%, three times the rate of the other groups.

3. Just 18% of Data Collectors anticipate revenue growth of 5% or more over the next two years (compared with 39% of Meaning Makers).

4. Meaning Makers said business analytics impacted about 22% of profit last year (including both cost reduction and revenue growth) across all markets. For data Collectors, only about 12.3% of profit was impacted. That's a full 40.1% (or 9.7 percentage points) lower impact than Meaning Makers.

5. More than 25% of respondents said that meaning making impacted revenue growth most when it was embedded into the processes at the customer interface — sales, marketing and customer service. More than 26% pointed out that their companies are growing because they use business analytics to shape new product and service development.

6. Across all companies surveyed, more than 20% connected meaning making to reducing core manufacturing, supply chain and service delivery costs in the enterprise. A similar number indicated they are using business analytics to control costs for financial planning, tracking, analysis and reporting.

7. More than two-thirds of respondents already rely on technology experts to help them apply analytics; meaning-makers are already bringing behavioral scientists into the analytics fold.

8. A recent survey of workforce skills conducted by Oxford Economics indicates that companies will face severe shortages in finding the needed big data talent. Nevertheless, some 26% of respondents intend to reduce their reliance on external business analytics and 19% have no plans at all to engage in external partnerships.

9. Uncertain ROI, lack of talent and a gap between IT and business operations were cited as the number-one obstacle to business analytics.

Obviously, Meaning Makers are a lot more optimistic, profitable, efficient and confident about the future. But they're also in more control. Regardless of how your organization applies big data today, here's how you can get a leg up on the competition.

5 Ways to Conquer Big Data

Studies like ours reveal not only a huge potential impact of big data, but how companies are unlocking value from Code Halos. The roadmap may not be completely clear-even Meaning Makers are merely forging ahead with the understanding that more data doesn't necessarily translate into competitive advantage unless it is properly harnessed and applied.

For example, the San Francisco Giants are matching prices to demand. Toyota is becoming more predictive to keep potential problems out of its cars. UPS has become a technology company with trucks. Clorox is deriving meaning from advertising spend to impact business.

These are all different companies in different industries, but each is organized to win its market based on increased insight, understanding and even wisdom. Every company is different, so there is no "right" answer and it may seem daunting given all that must be done, but there are several very clear mandates for companies to succeed at this shift point.

Become a Meaning Maker (or pay the price).

Companies that learn to manage their information and use data to find meaning with specific business processes can thrive, as our study showed. Conversely, Data Collectors only will undoubtedly face a tough journey, or worse, extinction.

Leverage SMAC Stack™ technologies to fuel insights.

To get the most from big data, organizations need to track social, mobile, analytics and cloud apps. As such, process owners and technology leaders should be proactive about finding ways to leverage all four.

Reimagine big data at the process level.

The imperatives to "do analytics" or "use big data" are just too broad to be meaningful. Instead, focus on a specific business process. Whether it's your underwriting process, clinical drug trials, wealth management service, supply chain, or customer relationship management process, focus on specific functions that shape at least 10% of your costs or revenues. This is exactly what Meaning Makers such as Zappos, Netflix, UPS, Toyota, Pandora and others do around key processes.

Don't forget the "small data" you already have.

For all the talk of tools, algorithms and seemingly magical devices, the reality is that most companies have tremendous wisdom locked up in spreadsheets, call centers and employees' heads. Organizations don't always need a billion records to derive business meaning. Start by exploring the data already on hand before buying new tools or hire 10 data scientists. This will help break inertia and start people thinking about how to make business meaning.

Staff your team before the talent shortage hits.

Most of our survey respondents recognize that winning based on knowledge will require new human capital and are planning to hire or train accordingly. But our research also showed there may be a shortage of data scientists in the future. Hence, Meaning-Makers and forward thinking Data Explorers are hiring and partnering with available talent sooner rather than later.

Analytics sometimes get a bad rap. The word has taken on a mysterious quality and often creates more business questions (and spreadsheets) than answers. But as companies transform their operations to be more nimble and SMAC driven, they are finding that the next frontier of business competition entails extracting meaning from data. And Meaning Makers already know that separating signal from noise will be the next decade's killer business skill.

To learn more about How Meaning Makers Conquer Big Data – read our white paper The Value of Signal (and the Cost of Noise): The New Economics of Meaning-Making, available on our SMAC and Future of Work Web sites. Also visit Cognizant's analytics practice for more insights.

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