Entries filed under “Analytics”

For business executives that want to make the most of Big Data but find the whole process somewhat daunting, the research house International Data Corporation (IDC) has published a Big Data and analytics (BDA) maturity model designed to bring order out of chaos.

The BDA model provides an extensive framework of stages, critical measures, outcomes, and actions. The idea is that organizations can use this framework to move through the successive stages of BDA competency and maturity in an orderly fashion.

For example, they can:

• Assess their BDA competency and maturity
• Define short- and long-term goals and plan for improvements
• Prioritize BDA technology, staffing, etc.
• Uncover maturity gaps among business units and between business and IT groups

In a related story in Computerworld (IDC and Computerworld are both part of the International Data Group), IDC analyst Dan Vesset comments, “We’re at this period now where there’s certainly a lot of hype, a lot of promise. The question is what’s reality, and what can and should companies do in the near- and short-term?”

Big Data and analytics have become top agenda items for many executives, but with the opportunity to unlock the value of Big Data to accelerate innovation, drive optimization, and improve compliance comes the need to demonstrate value, navigate expanding technology alternatives, re-create business processes, and ensure the availability of appropriately skilled staff,” said Vesset in a press release announcing the BDA model. “The ability to manage and analyze Big Data and derive value from these activities will increasingly define an organization’s ability to compete or service its constituents.

The BDA model is described in the IDC report: IDC Maturity Model: Big Data and Analytics – A Guide to Unlocking Information Assets.

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Over at the Growth Science Blog, Thomas Thurston describes how the question of whether data science is better than human intuition is really false choice.

Relying on your wits is part of doing business. However if there are big problems that keep resurfacing, it’s a lot slower to go on guessing. If you don’t bring data science or some other form of rigor to the table you may never get a grip on what the underlying problem is. In the long term it’s a lot slower to treat the symptoms (keep guessing) than to cure the heart of the disease (ex. use data science). In contrast, data science is often slower at first, but faster later on. It can take hours, months or even generations for data science to model, predict or solve some of business’s toughest riddles. However once you’ve built a robust tool, it’s relatively fast and easy to handle or even prevent those problems if they threaten to pop up again.

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If you Google “benefits, Big Data” you’re rewarded with a landslide of links pointing to various companies, consultants and vendors extolling the virtues of this new technology trend.

One of the more restrained assessments comes from McKinsey & Company research, which indicates that we are now at an inflection point where the ability to capture, store and analyze the massive amounts of data available to business, government and consumers has become an economic necessity. Referencing the McKinsey research, an article in the Ivey Business Journal states that, like other past trends in IT innovation, Big Data has the power to “transform our lives.”

But before throwing caution to the winds and proclaiming a new era of enlightened decision making, it might be wise to heed the words of Accenture Managing Director Jeanne Harris as reported in a recent Wall Street Journal blog authored by Deborah Gage.

Using the latest storage, databases and analytical tools, corporate managers can now, at least in theory, make better, faster and more informed decisions, she (Harris) says, but she doesn’t see that happening,” Gage writes. “A quarter of managers still say they make decisions not with data, but with their “gut”–and of the ones that do claim to use data, 40% used their gut to make their last big decision. Older managers grew up in a time when data, if you could find it, wasn’t reliable, she says. ‘They grew to trust their gut, because they had no other option.’ The problem now, Harris has decided, is no longer technology—it’s a shortage of people who understand enough about math and data science to evaluate their companies’ options and speak to their managers in a language that managers understand.

In the parlance of Daniel Kahneman, the Nobel Prize winning author of Thinking, Fast and Slow, this is a problem involving System 1 and System 2 thinking. System 1 is fast, intuitive, emotional – the gut feeling mentioned above. System 2 is slower, deliberate, logical. System 1, which functions very well most of the time, can lead to spectacular mistakes.

Kahneman is recommended reading for anyone involved in the decision-making process, whether Big Data is involved or not. And that, of course, means all of us.

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