Build it and they will come: The wrong big data adoption strategy
In a recent Teradata–McKinsey survey, 67% of respondents said that big data and analytics were having a significant and positive impact on their revenues. Of these, 21% believed that big data/analytics was the most important path to competitive advantage, and 38% said that big data/analytics was a top five issue in their companies. Yet internally, many of these organizations acknowledged that a data-driven culture was not universally accepted, and that there were a number of internal stumbling blocks to big data adoption.
In this respect, big data and analytics are no different than cloud, virtualization, and other historical IT trends. New technologies are seldom universally adopted throughout an organization when they first are introduced. This makes the path to adoption gradual and at times, even slow.
“There are always key questions to ask,” said Chris Twogood, Vice President of Product and Services Marketing for Teradata. “For instance, are you relying on individuals within your organization to just spontaneously say that big data is a great idea, or are you defining big data as a core component of your corporate decision-making? Are you developing a corporate strategy around your big data, or do you only have pockets of big data innovation within the company?”
Twogood said that, if an organization lacks direct support for big data and analytics that comes from its C-level executives, adopting a big data/analytics strategy can become a daunting challenge. “You will experience immediate impediments to your big data projects because you’re just not going high enough internally to get the support that is needed,” he said.
When big data and analytics are adopted as a corporate strategy, they are used as intelligence from the outside world that can tell a company what its next line of products or product enhancements should be. In other cases, the data are further monetized by the company, because companies find that they can sell this data to others and create an entirely new revenue stream from it.
“What you don’t want to do is to look at big data in just one dimension,” said Twogood. “Organizations that adopt a ‘build it and they will come’ approach to big data have difficulty succeeding with their big data initiatives.”
Instead, companies should look at their big data/analytics projects holistically, because they are not only delivering analytics — they are potentially changing how people and processes work within the organization. Without an effective change management process that assists managers and employees in using the new analytics, the analytics can be doomed to failure.
“Another key to this cultural adoption is making the analytics easy to use and access,” said Twogood. In other words, your data science and analytics algorithms could be great — but if there aren’t easy ways for people to use and succeed with the analytics, they won’t be used.
What if an organization simply decides that big data/analytics is not worth the trouble or cost of adopting?
“So many organizations are now using big data and analytics for revenue generation and other value-enhancing applications that companies that choose to avoid it altogether risk falling behind,” said Twogood. “In the past, it was ok to start small with a big data trailblazer, and to experiment with big data proof points and failures. Today, there is a rallying call in the marketplace, so if you want to compete, you need to get big data working for you.”
No time to wait
Twogood encourages organizations that lack internal expertise with big data to find consultants who can help them craft a big data roadmap and corporate direction. For organizations beginning with big data, the approach should be a holistic one that encompasses the entire company, but it is still ok to start small with one or two projects, especially if people, processes, and the technology are all tied together.
He said that the major areas of big data applications that he sees in companies are in customer/revenue generation data, operational efficiency, risk management, and data monetization. There’s no doubt that other areas of strategic big data applications will open up going forward; this is why time is of the essence for big data. Even smaller companies with looming cultural and budgetary constraints can no longer afford to wait.
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