6 data analytics success stories: An inside look

Leading CIOs are successfully tapping data analytics and machine learning to achieve business goals.

10 data analytics success stories: An inside look
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If data is the new oil, then knowing how to refine it into actionable intelligence is the key to unleashing its potential, elevating IT leaders to rock stars in the eyes of their C-suites and boards of directors. Accordingly, CIOs are leveraging predictive analytics, crafting machine learning algorithms and battle-testing analytics solutions in pursuit of businesses efficiencies and new ways to serve customers.

Thanks to a universal push for digital transformation, worldwide revenues for big data and business analytics software will top $189.1 billion in 2019, an increase of 12 percent over 2018, according to IDC. "Enterprises are rearchitecting to meet these demands and investing in modern technology that will enable them to innovate and remain competitive," says IDC analyst Dan Vesset. "BDA solutions are at the heart of many of these investments."

CIOs who put analytics to work in boosting top- and bottom-line growth recently shared lessons learned and advice for peers undertaking similar efforts.

Accenture analytics facilitates sales, utilization and diversity

Analytics guides many of the decisions made at Accenture, says CIO Andrew Wilson. For example, the professional services company’s Win Probability Tool leverages key metrics defined by business owners to score the likelihood of winning potential business opportunities.

The application churns through Accenture's Salesforce.com CRM data, taking into account several years' worth of deals, as well as geography, price points, margins and other metrics to predict loss potential with 90 percent accuracy.

At a time when enterprises place premiums on resource allocation, Accenture also uses analytics to track technology device and real estate utilization. The device dashboard displays utilization trends by location, with the ability to drill down at the device level. The space dashboard shows the use of seats and meeting rooms in Accenture offices and delivery centers drawing on nine data sources refreshed monthly.

Both apps enable leadership to make critical decisions that improve the experience for Accenture's 500,000 employees, many of whom work remotely and travel a great deal. "Getting the right utilization in terms of space and technology is key," Wilson says. "We have to be very careful of how we allocate fixed real estate and office space."

And in another sign that companies continue to emphasize diversity and inclusion, Accenture is also using predictive analytics to conduct present modeling and forecast modeling to perform what-if scenarios that test out gender-mix hiring impacts and forecasts using different inputs. The tool is critical for helping Accenture reach its 50/50 gender parity goal by 2025, as well as to have 25 percent of the female workforce in leadership positions.

Wilson supports these apps with a data lake running in Microsoft’s Azure cloud, which employees query for information and visualize with Qlik software.

Lesson learned: The bigger the enterprise, the more value that is trapped in the data it has collected. Accenture is constantly revisiting its approach to providing consulting services, which means evolving what data it curates and how. "A digital strategy has analytics at the core," Wilson says.

Belkin charges up its analytics strategy

At Hon Hai–owned Belkin, CIO Lance Ralls is gearing up for analytics around customer and operational information. But before he can execute on that strategy for the maker of charging cables, adapters and cases for smartphones, laptops and other devices, Ralls must lay a strong data foundation.

So Ralls’ team is combing through a variety of Excel spreadsheets for data that will eventually be aggregated into a data lake. They are also exercising copy data management, a practice in which users can rapidly roll backward and forward through snapshots of financial and business reporting data to identify issues. The software, from Delphix, also enables Belkin to virtualize, compress and protect data.

"Users feel much more comfortable with the data and can run a lot more reports, giving the business more real-time data for analytics," Ralls says.

Lesson learned: Prepare now for 5G. If CIOs aren’t already thinking about this ultra-fast cellular network technology, they should, says Ralls, who is already thinking about how the eventual “explosion of data” enabled by 5G will impact the wireless gateways, routers and other Belkin products that provide connectivity for consumers. 

Shell crunches data to anticipate machine failure

Few sectors generate more data than the energy industry. But for years, oil giant Shell didn't know where parts were in its various facilities around the world; it didn’t know when to restock; and it didn't know when maintenance issues were occurring until parts began failing. With machine downtime costing industrial companies millions of dollars a day, Shell decided to harvest data to head off these issues.

Shell built an analytics platform based on software from several vendors to run predictive models to anticipate when more than 3,000 different oil drilling machine parts might fail, according to Daniel Jeavons, general manager of Shell’s data science center of excellence.

One of those tools, Databricks, captures streaming data via Apache Spark. Shell uses this tool to better plan when to purchase machine parts, how long to keep them, and where to place inventory items.

The tool, hosted in Microsoft Azure’s cloud, has helped Shell reduced inventory analysis from over 48 hours to less than 45 minutes, shaving millions of dollars a year off the cost of moving and reallocating inventory.

Lesson learned: It takes a lot of tools to stave off machine failure. Jeavons says Shell’s platform includes software from Databricks, Alteryx, C3, SAP and other vendors to help his data scientists generate business insights. Ultimately, CIOs must evaluate the right tools and see what works before making big-ticket purchases. 

Cargill’s analytics feed data to shrimp farmers

Cargill’s animal nutrition unit developed iQuatic, a mobile data-tracking app that helps shrimp farmers reduce the mortality rate of their yields.

The app predicts biomass in shrimp ponds based on environmental factors, such as temperature, pH and nutrition, and works in concert with Cargill’s iQuatic automated shrimp feeding system, which employs automated feeders using acoustic technology to understand the natural eating patterns of shrimp, says Tiffany Snyder, CIO of Cargill’s Animal Nutrition Enterprise. Snyder presented on the iQuatic system at the CIO 100 Symposium in August.

Farmers save data from the app to the cloud, then access a live operations dashboard that visualizes pond performance, providing key measures and predictive analytics that help them better manage shrimp health and increase yields. Previously, farmers collected this data the old-fashioned way — on pen and paper.

Lesson learned: To build the app, Snyder says Cargill sent engineers and business executives to a shrimp farm in Ecuador to learn how farmers captured data from their ponds. “We made the farmer part of our team,” Snyder says. Snyder says working quickly in agile, two-pizza teams paved the way for a successful pilot in five months and, ultimately, a production launch.

Making data analytics work at Merck

Global healthcare company Merck was looking to use data collected in ERP and core systems for manufacturing execution and inventory control to gain more business insights. But with its engineers spending 60 percent to 80 percent of their effort finding, accessing and ingesting data for each project, the business objective was going unfulfilled. "We were not viewing data as a viable, permanent and valuable asset," says Michelle D'Alessandro, 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 percent. The tangible business outcomes include a 30 percent reduction in average lead time, and a 50 percent reduction in average inventory carrying costs.

Lessons learned: A key to her success, D’Alessandro says, 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. D’Alessandro says 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," she says.

ARC tackles new data management

Data is the lifeblood for Airlines Reporting Corp. (ARC), which each year settles more than $88 billion worth of airfare transactions between airlines, including Delta, American Airlines, British Airways, Alaska Airlines, and travel agencies, such as Expedia. Airlines pay to access data that ARC collects on these transactions to learn more about where travelers are going, when they travel and how much they are paying in the process for more than 2.2 billion flights each year.

ARC captures the data, ingests it into analytics engines, refines it, and builds custom reports for its customers. The company is migrating from a Teradata data warehouse to cloud software from Snowflake, which will help ARC get data products to market faster and offers greater scalability and performance, thanks to its home on AWS, says ARC CIO Dickie Oliver. Oliver says Snowflake, which is architected to separate compute resources from data storage, enables ARC to rapidly build new customized reports for customers. Thanks to this project, ARC will able to create new products tailored for customers that take new forms of data into account, Oliver adds.

Lesson learned: Moving to a new data platform is daunting and not just because of the technology shift; change management is the real bugbear here. Getting peoples’ “minds wrapped around change to begin with and moving them through the change process is the most challenging part of the process,” Oliver says, adding that he’s fully engaged in training staff, including moving them through certifications, and bringing in consultants, such as Slalom to help with the change management.

This story, "6 data analytics success stories: An inside look" was originally published by CIO.

  
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