Solving Big Data BI Problems In Dysfunctional Company Cultures

Ever hear of the term, “crowd psychology?”

Traders on Wall Street sure have.

It’s a term that describes the emotional drivers that make investors buy and sell, and understanding how crowd psychology works can mean the difference between a financial services firm recording its annual ledgers in black or red ink.

No doubt, the term points to a negative trading environment – even a dysfunctional one where coherence and logic take a back seat to emotion, and even panic, as Wall Street has seen from time to time.

Now, technology has offered a new way for investment firms to monitor crowd psychology, and myriad other investor/client habits that can translate into a healthier bottom line.

It’s all about data analytics, and for money management firms, and by extension any company in any industry that relies on information to thrive, turning that data into dollars is the Holy Grail these days.

But it’s no easy task.

According to Capgemini Consulting, most so-called “big data” initiatives just haven’t panned out yet for U.S. companies, even as they’re hip-deep in data analytics projects and platforms these days.

This from Capgemini:

There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.

That’s not to say company IT directors aren’t bullish on big data – they are. Capgemini reports 60% of executives surveyed say big data will “disrupt their industry” by 2018.

But getting a data analytics campaign off the ground in a dysfunctional company culture can be fraught with peril if IT directors don’t plan things right. That’s no knock on data analytics – there is an abundance of evidence showing that big data roll outs can succeed in myriad ways if the underlying corporate culture is a welcoming one (and a functional one, more importantly) for new data initiatives.

So what examples of dysfunction can blunt your big data roll out? According to the Capgemini study, there’s a “combination of infrastructure, structural and social conditions are holding back successful implementations.”

Here’s a line-item look at what might be holding back a successful big data campaign at your firm:

  • No way to consolidate data – 46% of IT managers say the biggest issue is scattered data across various departments, or company “silos”.
  • No solid business plan – 39% say that lack of a good business leadership platforms, especially a good business plan for using data analytics (especially adequate funding) is holding back big data services.
  • No coordination across the organization – 35% say no plan to seamlessly link different teams and departments across the company can threaten any big data roll out.
  • Reliance on “old systems” – 31% of IT execs interviewed by Capgemini report that aging legacy systems for data processing and their management forms are standing in the way of “full scale” data information programs.
  • No skills for data changes – Lastly, a lack of skills in deploying a big data program can sink morale and add to a dysfunctional corporate culture that is equipped to handle the change needed to successfully leverage big data technology across the enterprise. 25% of IT managers say “lack of big data and analytical skills” are hampering their big data roll outs.

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Big Challenges, Big Money – Even as U.S. companies struggle to maximize big data initiatives, they’re pouring billions into the technology. In 2013, for example $31 billion was expended on taking advantage of Big Data, by 2018 the expenditure is expected to rise to $114 billion.

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Consequently, the challenge for company tech executives in deploying big data programs in chaotic or dysfunctional organizations is to target potentially problem areas that need to be cleared, with some areas a big priority over others.

“There are many factors that go into the making of a successful big data implementation,” says the Capgemini report. “However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support.”

“For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams.”

Back in the financial services sector, companies are overcoming problematic, challenging cultures to successfully leverage big data information campaigns that lead directly to heftier bottom lines.

“Application of advanced analytics is helping financial services firms attain better and faster success rates in achieving their strategic goals of incremental revenues, higher profits and lower costs,” says Durjoy Patranabish, a senior vice president of data analytics, for Blueocean Market Intelligence, a global insights and analytics firm that works with Fortune 1000+ companies. “Some of the applications of advanced analytics for money management firms include better ROI, improved customer engagement, superior risk mitigation, and the enhancement of marketing and branding efforts.”

Patranabish points to a large regional bank that uses marketing and risk analytics across customer life cycle to drive both strategic results and generate high returns. He also cites a leading regional bank using behavioral segmentation model to divide their customer base, resulting in a revenue hike of 50%.

In the end, it’s up to leading company decision makers to identify problem areas and correct any threats to a game-changing move to data analytics. That could mean solving multiple problems, like finding funding, streamlining training efforts, getting company-wide buy-in, hiring a technology analyst to do the job in-house, or going the consulting route for ideas and action plans.

The key takeaway is to get going, because if you’re not getting dollars out of your data now, chances are your competitors have already solved their dysfunctional deployment problems, are using big data now, and have a leg up on you already.

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