Avoiding Failure on the Last Mile of Big Data Analytics

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Just picking a big data platform is not enough. The hard work starts when you customize the algorithms and models to enhance the really essential value of the business.

“The use of big data will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously,” says McKinsey’s Business Technology Office, in a report titled: Big data: The next frontier for innovation, competition, and productivity.

But not every big data initiative is successful; a surprising number actually fail. The following report identifies four primary reasons for failure. 

The very first reason may be the most important. Big data projects fail because they focus on technology rather than business outcomes.

That shouldn't be surprising. Technology clearly sets the foundation for driving value from big data. The pure technology focus, however, breaks down at the last mile of big data analytics, which is exactly where big data should deliver its greatest business value.

The last mile of big data addresses the organization's specific business situation. “This is different in every case and is highly context sensitive—this is the last mile, the final alignment of the big data analytics processes, frameworks and best-practices based solutions with the unique business situations of different companies,” says Phani Nagarjuna, CEO of Nuevora, San Francisco, CA.  Nuevora’s proprietary Big Data Analytics & Apps Platform (nBAAP™) provides for rapid-fire configuration, delivery & ongoing re-calibration of actionable big data insights from a broad set of pre-built big data analytic models and apps to enable the organization to correctly pave the last mile and optimize its targeted business outcomes.

This is particularly important to the CFO, who has some very specific big data analytics concerns. For example, big data is ideal for uncovering problems that impact margin. It also can become a critical tool for combating fraud and mitigating risk.  A focus on big investments into pure big data technologies alone will not necessarily address the CFO's specific interests nor would it produce immediate and tangible outcomes.

A CFO understands that a successful business is more than its component parts. It’s more than equipment, products, services, and sales. It’s even more than the people that run it. Every business has an unseen atomic makeup that is essential to its very being; its own Higgs boson, the so-called God particle.

With big data analytics, “you are taking a journey in the direction of what your business actually is made of, toward its unique Higgs boson,” explains Nagarjuna.  This calls for paving the last mile of big data analytics exactly the way the business is or should be built. “Going a level deeper, you can determine at the most granular, atomic structure what makes your business tick and drive those levers for achieving profitable growth,” he adds. Then you have something very powerful, allowing the business to dive into deeper levels of management and decision making.

There are not that many problems today that organizations are turning to big data analytics to solve. But even if the number of problems is small there are variations within each.  The customer churn problem, for example, might also be applied to employee turnover. Each variation of the basic problem requires tweaks to the underlying models and algorithms that go to the very core of the business itself. A one-size-fits-all big data solution rarely works.  The problem of customer churn at an insurance company, for instance, will be different than at a bank. Although both provide financial services their last mile of big data is not the same.

That’s why paving the last mile of big data analytics is so challenging and why the big platform vendors avoid addressing it. It is costly to configure and customize each big data solution for an organization’s specific big data challenge. Few organizations are prepared to undertake this.

One big data analytics player, Nuevora, starts with the last mile and builds a bridge to address each organization's particular problem through big data. It draws in any type of data and runs it though its big data processing, modeling, and apps engines. For any given problem it selects the most optimum model from its wide range of proven models and then applies the appropriate analytic framework and data heuristics that will deliver the best results.  The results can be deployed to the organization's preferred business intelligence / reporting platforms, whether Cognos, Oracle, Tableau, or whatever else the business uses, even Excel. Nuevora nBAAP platform handles the data, provides the models and heuristics, recalibrates on an ongoing basis for continuous improvement, and can augment the organization’s own data with Nuevora’s proprietary data or outside data it pulls in on its own.

Through such a platform the organization can address its specific business outcomes rapidly rather than deal with significant technology investments and associated implementations. It allows the business to dig down through big data to discover what the business is really made of and unleash its true potential.

 

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