The vast potential offered by combining user-generated data from social networks and information from sensors and transactional data traditionally housed in databases has prompted the moniker “Big Data Revolution” to be bandied about in the media.
Booz & Co. notes in a recent Forbes blog post that companies should educate themselves about big data through a lens focused on the technological revolutions of the past.
Just as with the advent of the availability of transactional data in the 1980s and the rise of the Internet, executives maneuvering to corral insight into big data should learn from lessons of these past paradigm shifts.
“Our reaction to it should be informed by all we’ve learned from past revolutions, which for me boils down to two main points: Don’t miss the boat, and stay focused on solving core business issues,” according to David Meer, the author of the post.
And data collection should be the core of business decision making, he notes.
While larger companies like Wal-Mart have acquired analytics companies to bring large-scale data analytics capabilities in-house, other companies are being creative and using a combination of internal and external data sources and advanced analytics, the post notes.
After a company has institutionalized data-driven decision making it should start data analysis by focusing on the most critical business issues.
“The core issues businesses face haven’t changed: understanding consumer/customer needs, developing and refining value propositions, building strong brands that consumers care about, and creating win-win relationships with channel partners,” Meer says. “In these and many other areas, data, wisely used, can open up new markets. Identifying and building them should be the primary focus of data-driven capabilities.”
A trio of directors from McKinsey & Co. concur in a Harvard Business Review post that the power of data analysis to alter the business landscape is so great that company C-suites will need to evolve as they have in the past leading to the creation of new roles like the chief financial officer and the chief marketing officer.
“Because the new data analytics horizons typically span a range of functions, including marketing, risk, and operations, the C-suite evolution may take a variety of paths. In some cases, the way forward will be to enhance the mandate of (and provide new forms of support for) the chief information, marketing, strategy, or risk officer,” according to the post. “Other companies may need to add new roles, such as a chief data officer, chief technical officer, or chief analytics officer, to head up centers of analytics excellence.”
Before companies tap the new roles and responsibilities that they will need to become data driven, McKinsey advises they should take six steps first:
1. Ask the big question. Companies need to acquire knowledge of data analytics and change the business culture with regard to analysis, the post suggests.
“Push durable behavioral changes through the organization with the question: ‘Where could data analytics deliver quantum leaps in performance?’ This exercise should take place within each significant business unit or function and be led by a senior executive with the influence and authority to inspire action,” according to McKinsey.
2. Set the strategy. Charge specific team members with developing a clear, well-defined strategy around data analysis initiatives.
3. Tackle the tradeoffs. Determine “buy-versus-build” trade-offs for advanced analytics models and tools.
4. Tap the experts. There is a brewing talent war for data scientists and others with the statistical horsepower and the business acumen to exploit the opportunities that big data offers.
5. Break the barriers. “Companies often are surprised by the arduous management effort involved in mobilizing human and capital resources across many functions and businesses to create new decision-support tools and help front-line managers exploit analytics models,” the post continues. “Success requires getting a diverse group of managers to coalesce around change – breaking down barriers across a wide phalanx of IT, business-lines, analytics, and training experts. The possibility of failure is high when companies don’t commit senior leadership.”
6. Engage the front-line. To be most effective, the analytics that companies put into place should be pushed out to front-line workers and embedded in their daily work processes.
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