The Future Belongs to the Informed

Anthony J. Scriffignano, SVP-Worldwide Data & Insight, Dun & Bradstreet
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Anthony J. Scriffignano, SVP-Worldwide Data & Insight, Dun & Bradstreet

Much like the cortex of the brain and frontal lobes associated with self-control, planning, reasoning and abstract thought, Big Data synthesis is central to the strategic thinking of today’s innovators and business executives.

However, as is this case with our brain’s capacity to gather, process and act upon information, more doesn’t necessarily result in better decisions. How else do we explain the bizarre actions of so many well-educated, successful adults gracing our tabloids’ front pages? Clearly, abundant knowledge doesn’t always translate into intelligent decision-making.

Perhaps, there remains great mystery locked within our brains. Of course, I’ll leave that to the better qualified to determine. Instead, I’ll stick to my area of expertise which involves business intelligence, data and analytics. This domain is an increasingly fascinating, complex and challenging one, especially for business leaders, including chief information officers and their organizations.

If you are reading this article, chances are you may be scrambling to discover the secret of transforming Big Data into Big Insight, and that Insight into winning Action for your organization.

Consider Big Data being a lot like a Big Brain; you have to apply it properly, based on derived insights, to help ensure success. 

As many companies struggle with emerging technologies to discover and curate massive quantities of highly dynamic data, the key often involves asking the right questions. We encourage customers to factor in the “Vs” of Big Data—volume, variety, velocity and veracity—to help address five fundamental challenges.

Question #1  Who owns, or is most responsible for, Big Data?

We find that, in most organizations, this ownership remains a shared responsibility among business and service lines, including sales, marketing, risk management, compliance, business development, information services and technology. Increasingly, chief information officers and their teams are gaining more influence because IT hardware, enterprise software and workflow are critically important to easily deliver Big Data capabilities across an enterprise.

Streamlining and consolidating ownership of market research and intelligence, customer analytics and other Big Data assets, is never a bad idea in terms of overall responsibility.

Question #2   Do your people understand Big Data?

Regardless of how your company applies Big Data, be it to drive sales and marketing efforts through improved prospecting and lead scoring, or to help mitigate risk in supply chain management and credit, a talent gap looms on the near horizon, both in terms of existing expertise and new skills brought into the enterprise.

According to a recent McKinsey & Global Institute Report, by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions. So, recruiting and investing in talent with in-depth expertise in data, market intelligence research, CRM tools and associated areas will be key to success.

Question #3   Are you gathering and interpreting the right Big Data?

The nature of Big Data itself is rapidly changing through human and digital learning. Business challenges such as credit decisioning, supply chain management and sales prospecting are much more complex as the volume of publicly-available information and data continues to mount. The digital footprints of individuals and organizations are vast and readily traceable. Yet, those trails have never been more complex and are potentially full of false and/or outdated information.

Accessing “free data” is straightforward. The temptation to pursue a quick fix, however, should be balanced against the strong possibility of an equally fast and potentially far-reaching failure.

A short generation ago, a lack of transactional data resulted in surveys, surveys and more surveys. Human and digital latency was an accepted part of the analytic landscape. The limitations of storage capacity and computing power were severe (remember the days of megabytes or sharing a PC with other team members?). Analytic methods were descriptive at best, offering fragmented views of the truth that often lacked foresight.

Today, these issues have been addressed, giving rise to others.

Transactional data has resulted in overwhelming problem formulation and an increased reliance on tools versus methods. We normally don’t suffer from lack of storage and processing power. Analytic capabilities can allow us to filter, determine correlation, and be both prescriptive and proscriptive. We still, however, struggle for a comprehensive view to assess causality and to transform our understanding into foresight and action.

In many ways, our collective head is figuratively in the clouds.

Massive volume and changing variety make it all the more important to ensure you focus on gathering, interpreting and acting on the right combination of Big Data. Is it accurate in a way that can be evaluated scientifically? Is it appropriately refreshed? Are the analytics and insights actionable? Does it provide a combination of the necessary detail and breadth of perspective? Does it align with your business strategies and objectives? Is it easy discoverable, accessible and shared across your enterprise?

Question #4  Are you transforming Big Data into Big Insights that ultimately lead to Winning Actions?

This is where the rubber meets the road. If Big Data doesn’t ultimately provide a path to business success, everything involved is a huge waste of time and resources. Worse yet, chances are one of your key or emerging competitors figures it out to their advantage and your company’s detriment.

If there’s one single piece of advice I can share that will make the biggest difference to achieving Big Data success, it’s this—the future belongs to the informed! And by “the informed” I’m referring to those organizations putting “relationship insight” at the center of their Big Data strategy.

Simply put, there are three levels of insight required along a journey to informed perspective and winning actions:

  1. I See You, based on foundational business insight;
  2. I Know You, requires integration of assets and transactional data; and
  3. I Can Predict Your Behavior, dependent on predictive, on-demand insights.

Leveraging new Big Data sources provides a complete transparency of a business relationship which enables actionable insight. This approach involves:

  • Linking relationships among trading partners to see a complete supply chain;
  • Identifying the “heartbeat” of a business, predicting its future health, and rapidly seeing changes;
  • Providing deeper insight based on signal patterns to anticipate the future behavior of a business; and
  • Understanding the true size of a business in multiple dimensions, including social influence beyond the balance sheet.

Question #5   Are you properly integrating new sources of Big Data?

Just because it’s on the Internet doesn’t make it true. There’s a significant effort to discover, manipulate and exploit what we “find” on the Web. Newer information isn’t always more truthful. The veracity of social data should always be of concern.

Integrating social intelligence presents a big opportunity and challenge for most organizations. It’s important to consider approaches that aggregate appropriately sourced public web and social data. By linking unstructured social data with structured data, our customers can gain a more comprehensive view of people and businesses, prioritize leads and targets, and identify opportunities to expand relationships.

Big Data, at its best, is among your organization’s most valuable assets. Operating much like your company’s business brain, Big Data can drive informed decision-making based on a complete perspective and Big Insight. And, chief information officers and their organizations play a pivotal role, especially when it comes to ensuring Big Data capabilities are effectively delivered when and where they are most needed across an enterprise.

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