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6 data analytics success stories: An inside look

6 data analytics success stories: An inside lookIf data is the new oil, then knowing how to refine it into actionable intelligence is the key to leveraging its potential. To this end, CIOs are playing with predictive analytics tools, crafting machine learning algorithms and battle-testing other solutions in pursuit of businesses efficiencies and new ways to serve customers.

Hyperaware that reducing costs or boosting revenues can help them shine in the eyes of the C-suite and board of directors, CIOs are spending more than ever on technologies that support data science. Worldwide revenues for big data and business analytics will reach $150.8 billion in 2017, an increase of 12.4% over 2016, according to IDC. Commercial purchases of hardware, software and services intended to support big data and analytics are expected to exceed $210 billion. IDC analyst Dan Vesset noted that big data analytics solutions have become key pillars of enabling digital transformation efforts across industries and business processes worldwide.

But there is a dark side to this delirious spending: Most data analytics projects fail to yield measurable value. Legacy systems and business-line bureaucracies have spawned data siloes and perpetuated poor data quality. And CIOs are still struggling to fill the gaps in talent required to manipulate data for insights. The war for talent is fierce and the rise of university analytics programs isn’t producing qualified candidates fast enough.

Yet data analytics success stories abounded at the CIO100 Symposium earlier this month, where several IT leaders revealed and were awarded for their efforts. CIOs also shared lessons learned and advice for peers undertaking similar efforts.

Making data analytics work at Merck

Merck, which had grown to become a $40 billion global healthcare company operating in 140 markets worldwide, sought to use data collected in ERP and core systems for manufacturing execution and inventory control to gain more business insights. But with Merck engineers spending 60% to 80%  of their effort finding, accessing and ingesting data for each project the business objective was long gone. "We were not viewing data as a viable, permanent and valuable asset," said Michelle A'lessandro, CIO of manufacturing IT at Merck. "We wanted to establish a culture where we spent far less time moving and reporting the data and far more time using the data for meaningful business outcomes."

Merck created MANTIS (Manufacturing and Analytics Intelligence), an über data warehousing system comprising in-memory databases and open source tools that can crunch data housed in structured and unstructured systems, including text, video and social media. Importantly, the system was designed to allow non-technical business analysts to easily see data in visualization software. Conversely, data scientists could access information through sophisticated simulation and modeling tools. MANTIS has helped decrease the time and cost of the company's overall portfolio of IT analytics projects by 45%. The tangible business outcomes include a 30%  reduction in average lead time, and a 50%  reduction in average inventory carrying costs.

Lessons learned: A key to her success, A'lessandro said, was identifying a "lighthouse" analytics project in an Asia-Pacific plant where Merck would see the biggest payback. Upon demonstrating success with MANTIS there, it became a call

to action to other sites. She also learned not to bite off more than she can chew. A'lessandro said she "overreached" in an early experiment to use artificial intelligence and machine learning to analyzes costs of Merck's manufacturing processes. "It wasn't for lack of sponsorship or lack of visions, we just couldn't get it to work," A'lessandro said.



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