When it comes to big data, bigger is not always better. For a small to midsized company, starting small often makes more sense.
Since the concept of big data became the buzzword du jour, big data has become big business, as specialists in supply chain management work to leverage information for better logistics and supply chain management. Lured by the promise of big payoffs, many companies have sunk millions of dollars into sophisticated data analytics software only to realize they did not have the capabilities to interpret the new insights nor how to turn them into a competitive advantage.
The temptation to jump head first into a big-data project is obvious.
A survey conducted by Supply Chain Insights found that one fourth of the respondents had a big-data initiative in place and 65% planned on launching one in the near future. Seventy-six percent of survey respondents viewed big data as an opportunity.
Giants like Amazon, Google, and Walmart showcase how an entire enterprise can be built around the interpretation of unfathomable masses of data. These companies have perfected the science of gleaning -- and capitalizing on -- detailed insights about customer behavior. (For example, Walmart was able to pinpoint something as specific as what kind of Pop-Tarts customers stock up on before a storm -- strawberry.)
With similar analytics tools now available to companies in all kinds of industries, the opportunity to turn hype into hope may be irresistible.
A supply chain company could on the demand side, for example, determine to use big data to map all the quotes and online searches that never became orders and change its marketing strategy based on a newfound understanding of how the purchase of one product leads to the purchase of another.
On the supply side, big data could be used to measure the impact of a catastrophic event on suppliers abroad, and consequently, allow the company to plan in advance to mitigate the effects on American consumers.
In theory, it sounds easy. But a recent study by Harvard Business School suggests big-data investments fail to deliver because most companies can't handle the information they already have.
"[Companies] don't know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights," the authors write. "Companies don't magically develop those competencies just because they've invested in high-end analytics tools."
Research suggests those companies that do succeed have a culture of evidence-based decision making. Those with advanced Master Data Management (MDM) are also more likely to successfully embark on a big-data project, another study suggests.
In any event, you'd be wise to:
- Clearly articulate what kinds of data you want to collect and start small with a few simple analytics.
- Select your sources (randomly scanning everything between heaven and earth will do you no good).
- Align your goals with your business objectives.
- Turn your analytics professionals loose on the data.
- Determine how the findings should be presented in an understandable manner and how they should be applied to improve your business.
- Gradually allow big-data analytics to permeate the entire supply chain.
What is your take on big data? What kind of approach do you think is key to a successful big-data initiative?