Previously, I shared how some executives are skeptical about Big Data analytics and its ability to match their own business intuition.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=i
This made me wonder: How do some leaders find that Big Data analytics actually enlightens their business behavior? To help you find the path, I’ve compiled five expert tips that may illuminate your Big Data analytics projects.
Tip 1: New analytics often requires new behaviors. Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, says in his discussions with companies, those who struggle or achieve only moderate outcomes tend to use Big Data analytics primarily for decision support. By contrast, Big Data achievers leverage Big Data to change their conversations.
In a Harvard Business School post (registration required), Schrage specifically recommends encouraging more sharing and collaborative work, rethinking or adding business processes, and re-evaluating existing incentives.
Tip 2: Ask the business to identify the business processes or behaviors that need illumination. SOA and veteran IT consultant Joe McKendrick says success with Big Data analytics will require creating an “analytical culture.” To do this, find out what business leaders would like to understand or what they think might need to be re-evaluated.
Tip 3. Increase analytical skills across the organization. Analytics will require some level of retraining at all levels, McKendrick says, if it’s going to be transformative instead of merely “a support” for existing practices.
Tip 4. Tell a story or a history lesson with the data. Personally, I think narratives illuminate more than charts — and the experts seem to agree. Instead of querying the data as you would in business intelligence, experts suggest examining the data to see what story it tells. Data scientist Mike Cavaretta calls this taking “your audience on a data journey.” Schrage also quotes a CIO who said people responded better to a history lesson than to a math lesson when it came to analytics.
Tip 5. Integrate and add more data. That can sound crazy when you’re already dealing with large data sets, but one of the game-changing aspects of Big Data analytics is that it supports adding more data to the process. That includes integrating data sets that now exist in silos, of course, but it also means searching outside the organization for relevant external data. The combination of internal and external data is what often leads organizations to new light-bulb moments.
“A game plan for assembling and integrating data is essential,” advises a recent McKinsey Quarterly article. “Critical data may reside in legacy IT systems that have taken hold in areas such as customer service, pricing, and supply chains. Complicating matters is a new twist: Critical information often resides outside companies, in unstructured forms such as social-network conversations.”
One take-away from these tips: Big data analytics is not just a larger version of BI. BI excels at reporting and compliance — but for Big Data, these are low-hanging fruits, one CIO told Schrage.
“The most interesting tensions and arguments consistently revolved around whether the organization would reap the greatest returns from using analytics to better optimize existing process behaviors or get people to behave differently,” writes Schrage. “But the rough consensus was that the most productive conversations centered on how analytics changed behaviors rather than solved problems.”