Apps to Profile Fraudsters Based on Social Behavior

A leading analyst firm predicts a solid future for big data driven social computing-news that probably evokes mixed feelings in CIOs and CTOs.
Let us back up a bit to set the context. For well over a year now, enterprises have woken up to the opportunity that big data spells. An opportunity to uncover insights about themselves and the world of business around them-from new and emerging types of data and content. The tantalizing promise of infusing the business with more agility, and the prospect of finding answers to all those queries that previously was considered unanswerable. All of this is slowly reaching a tipping point. Because, with information exploding, so has the business organization’s expectations of insight, which everyone wants delivered quicker, with greater accuracy, and in a form that is ready to consume, deploy, and act upon-a tall order to unlock the value of all this data, which they believe technology can do for them. Now, with the world going social, and large amounts of operational data organizations collected over the last decade, data is getting BIG-petabyte big-and this tall order has assumed somewhat insurmountable proportions.

For instance, bankers are looking at big data to tell them which customers are at the greatest risk for account take-over fraud. Automotive insurers are seeking to identify, with big data analysis, those exact customers who are considering a new car purchase with their next insurance renewal. And this has set off the trend to procure big data point solutions, including those on social computing networks-for each business function, and each process requirement.

The Challenge

Historically, the need for insight has been addressed by creating puddles of traditional data warehouses. These seem to be multiplying by the day, and often don’t integrate with one another. And, what’s more, these structured data sources represent just 25% of any organization’s data. Really, it’s no surprise then to see things getting increasingly tough for technology organizations seeking to establish a single version of truth, drive data governance and, most importantly, reduce time to insight. And, they have been mostly unable to derive insight from much of the unstructured data that still lies untapped, unanalyzed and almost completely overlooked.

Most CIOs or CTOs of information-heavy enterprises, already know all of this, with internal teams perhaps cleaning up the mess these structured data puddles have made. But, at the same time, this is a challenge that is yet to be fully understood, much less dealt with.

What is needed is a platform, which gives both business and technology users the ability to quickly develop and adopt big data applications. A platform that straddles the entire data and analytics value chain-from the discovery of big data, integration with structured data sources, to the building of insights and the operationalization of decisions. And because it cuts across internal and external data sources, structured and unstructured data, from enterprise systems to customer touch points to social networks, the platform is an asset for all types of analyses-from rigorous, inward-looking insights built on structured and unstructured internal data to freewheeling analytics based on qualitative opinion gathered from the social universe.

Let’s Focus on the Latter

It is an accepted fact that today’s enterprises are swamped by the volume of available data and the multiplicity of its sources. A mid-2012 study of C suite executives reveals that their organizations are dealing with around 86% more data than just 2 years before. This doesn’t come cheap or easy. It is estimated that even best in class organizations take around 12 days to integrate new sources of data; the rest take 2 months!

Now, it’s possible for platforms to address these problems by providing out of the box connectivity to most popular data sources, including social networks like Twitter and Facebook. It can also automate most analytical functions, like schemas, multiple data format plug-ins and so on, to dramatically improve an enterprise’s ability to extract exactly the information that it needs from different sources.

But data by itself is of little use. Because social data or historical operational data is so voluminous, so unstructured, so subjective, and so unqualified-in other words, everything that traditional data is not-it takes a Herculean effort to make sense of it. By the time technical staff is through with creating the algorithms, business has probably missed its deadline for formulating the insights. And the data has very likely gone stale.

No longer. An extensive repository of algorithms, enabled for your platform, can help business users arrive at insights in a fraction of the time that they took earlier. What’s more, should they not find a ready algorithm for their needs, solutions are available that can easily create others by dragging and dropping them onto an easy to use interface, and test their performance, even without real data.

Then again, insight is of little use until it is put into action. However most senior leaders know how difficult it is to get the various decision makers in an organization to assemble, let alone agree. This task too has become a lot easier, because now, representatives of different functions from across the enterprise can gather together virtually, and collaborate to understand the insights and put them into operation. To assist in the latter, platforms today can offer users the option of integrated workflows to prescribe the best way forward.

So, the next time your business users ask you for an application which profiles potential fraudsters based on social behavior, or targets a promotion campaign at a very specific demographic, or recommends the ideal inventory for spare parts based on the service issues reported by customers within your industry, don’t bother negotiating timelines…tell them you have it ready. Because, now, you really can.

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