At public company Endava, 30 percent of the workforce came on board in the last year, following a series of strategic acquisitions and new hires. This posed a challenge: how to position these employees in commercial teams to generate new business and serve existing customers in a variety of industry sectors.
“When you grow as fast as we’re growing, adding to our global footprint organically and through several recent acquisitions, there’s only so much information you can hold in your head about your projects and the right people and skills for them,” said Helena Nimmo, Chief Information Officer at the London-based provider of software services and automation solutions.
One way to solve this problem would be the manual development of a spreadsheet listing Endava’s more than 8,200 employees and their respective interests, skillsets, current projects, where they work geographically and when their vacations are scheduled. Of course, once this data and likely more of it is collected, a project team leader would need to go through it, analyzing who might best fit the goals of the commercial initiative and where there were skills gaps requiring additional recruitment.
This analysis would take quite a bit of time and be potentially of little value once completed, as people generate new skills and interests, and move from project to project. And given Endava’s growth trajectory, new employees are likely to be coming through the front door who wouldn’t even be included.
“We needed a way to have an internal marketplace where our employees’ skills, locations, desires and recruitment possibilities come together in an ideal way to meet demand from customers in different sectors for our services and solutions,” Nimmo said. “Once we had that, we could tie this data into our workforce forecasting.”
The CIO rose to the occasion, via the development of a data and analytics tool she dubbed EWA (pronounced “Eva”) for Endava Workforce Acceleration. The pronunciation is important as Nimmo refers to the tool using pronouns like “she” and “her” to underline the personal nature of the technology, which involves people’s work and career aspirations. “EWA has offered a way for us to rapidly assemble the best mix of people optimally aligned with our customers’ needs in different industry sectors worldwide,” she said. “She’s helping us build trust and rapport.”
Endava is among the many companies that have seized upon the value of today’s increasingly sophisticated data and analytics tools to carve a sharper competitive edge through more informed and insightful decisions. For businesses still using yesteryear’s data and analytics, however, the edge is no longer as competitively sharp.
“Data warehouses and business intelligence tools have been around for the past 15 years, but these legacy systems have since become ubiquitous, commoditized and less competitively differentiating,” said Faisal Alam, EY Americas Emerging Technology Leader and Data Evangelist. “Once everyone has what you have, you move from a position of competitive advantage to merely staying relevant to becoming competitively disadvantaged.”
It’s the equivalent of using an iPhone 6 instead of an iPhone 12 Plus—the device still works and is worthwhile if you’re on a budget, but users miss out on a lot of added capabilities. Since data and analytics tools enable better business decisions, a competitive disadvantage can quickly set off a precipitous decline in market share.
“Many CIOs talk a great game but they’re not putting their money where it ought to be—investing in data as a competitive asset and building an innovative and responsive culture around data savviness,” said Alam. “First movers always capture more value. The ones that invest first will leapfrog the ones that don’t.”
Seeing is Believing
Among the companies that are using data and analytics tools to catapult their competitors are Dun & Bradstreet, HGS, Chubb and Endava. Each of the companies was asked to provide a single data and analytics success story, although they had more tales to tell. Their stories nonetheless demonstrate the different insights that can be culled through the tools’ use and the competitive traction they provide.
At Chubb, one of the world’s oldest and largest insurance companies, the use of data and analytics is far-reaching, given the data-intensive nature of insurance underwriting. Chubb underwrites a broad array of property and liability insurance policies for businesses in all industry sectors, basing the pricing and coverage terms and conditions for these policies on an analysis of historical claims and prospective loss exposures, among other factors.
Much of this work was manual in the past, making it an exhaustive and time-consuming process. Moreover, some data was inaccessible in real time, requiring the use of a proxy. An example is the risk of property damage in a factory or office building caused by a water leak, such as a burst pipe or an accidental mishap like leaving a faucet running.
“Insurers generally underwrite and price this exposure based on historical water damage losses,” said Sean Ringsted, Chubb Executive Vice President, Chief Digital Officer and Chief Risk Officer. “This information is aggregated with other factors like the size and age of the structure and its location, which serve as a proxy to gauge the potential risk for buildings with similar features.”
Today, with data and analytics instantly available through the Internet of Things (IoT), Chubb can underwrite property damage coverage and pricing for a specific building based on its unique risk characteristics, as opposed to the previous reliance on aggregated historical data as a substitute for the real thing.
Thanks to the use of water detection sensors in plants and buildings, real time data on a possible water leak can travel over the internet to an application where the data is analyzed. If determined to be an immediate hazard, the app remotely shuts off water in the pipes feeding that section of the building. “The existing model is `repair and replace’—a water damage event occurs, resulting in a claim for the repair and/or replacement,” said Ringsted. “The new model is `predict and prevent.’”
What does this have to do with underwriting? Knowing a company has this capability provides assurance to Chubb underwriters that the risks of property damage caused by a water leak are effectively managed, reducing the potential for damage losses to judiciously underwrite and price the exposure. “Our ability to improve our risk selection, offer more refined coverages and price the exposure becomes a defining competitive characteristic,” Ringsted said.
HGS, a global leader in business process outsourcing management, provided a very different success story involving the use of data and analytics to gauge contact center volume and capacity trends. This manually generated process made it difficult to measure each center’s operational metrics to react quickly to adverse trending, said Virgil Wong, Chief Digital Officer at the India-based public company, which tallies more than 35,000 employees worldwide.
“The process entailed manually pulling information from disparate systems, some of them outdated and others rife with inaccuracies,” Wong said. “The data was subsequently cut, pasted and aggregated into a report. By the time the report was produced, it was of little use in ensuring customer service was topnotch and in situations where it was subpar, that remediating actions were taken.”
In 2020, Wong and his team launched a project called HGS Pulse, in which the customer call contact data was digitalized, related processes were automated and an analytics engine was deployed to measure the quality of customer engagement. For example, the analytics engine provides real time information on how long each service call takes compared to optimal average call times, across different products and issues like an invoice query. The engine further analyzes contact center call abandonment rates compared to aggregate rates. “It’s made a huge difference,” said Wong.
Concerns over inaccurate or outdated data have been resolved and by categorizing the typical reasons why a customer is contacting the company, customer service reps can use the analytics engine to rapidly solve most every issue, irrespective of the channel, such as phone calls, emails, chatbots and so on. “Altogether, we’ve experienced a 40 percent efficiency improvement, in addition to lower processing costs and speed of service,” said Wong, adding that HGS Pulse recently was launched as a new product for customers to use in automating their call center volume and capacity trends.
Supply and (Skills) Demand
Like HGS, Dun & Bradstreet has developed a data and analytics tool, in its case to strengthen the quality of the commercial data and insights the 180-year-old company provides businesses. The new tool leverages sophisticated technologies like machine learning and Natural Language Processing (NLP) to analyze wide-ranging data from D&B’s suppliers to ensure the information provided is reliable and accurate, as these suppliers often cull information from other suppliers. The data and analytics tool reaches as far down in the supply chain as the tenth link.
“Our procurement organization uses the tool to monitor tiers of suppliers in real time, with an eye toward enhancing the precision and contemporaneity of the data insights and services we sell to our customers in government and industry,” said Gary Kotovets, D&B Chief Data and Analytics Officer. “The analytics tell us the financial stability, geography and geopolitical risks of each supplier, as well as the same risks of the supplier’s suppliers.”
The NLP software is configured with a proprietary algorithm to do the initial scraping of structured data on each supplier’s financial stability, geography and geopolitical risks. Using machine learning, unstructured data on a supplier is extracted and normalized to reduce redundancy and improve data integrity, giving D&B’s data scientists a wider and more accurate lens to look at these risks.
“We’re able to compare these real time activity signals to our current content (on a company’s creditworthiness) for modeling purposes,” said Kotovets. “Market trends are dependent on rapid-fire political changes, corporate actions like M&A activity, incessant technological innovations, and social changes and stability.”
He noted that D&B just recently began integrating its suppliers’ ESG (environment, social and governance) scores into its models. “The opportunities to acquire deeper insights using the tool are unlimited,” Kotovets said.
Nimmo has a similar perspective about EWA’s impact on workforce management. “The analytics is enriching,” she said. “We’re driving real time decisions on the skills and individuals needed to bring into the business at different stages. For example, if someone has picked up a talent in data science language modeling on a previous team and has interest in developing this skill, EWA will flag the individual to be put in the mix on a new team needing that skillset.”
She credits the analytics for a 70 percent reduction in the manual manipulation of data typically required to assemble a commercial team serving a specific client in a particular industry. “Instead of mucking around in a data swamp when a client project opportunity appears, we interrogate the data to shape the optimum team,” Nimmo said, calling EWA “the engine of our business.”
Ain’t Seen Nothing Yet
Over the next five years, past will become prologue and today’s data and analytics capabilities will lose their competitive edge as more companies implement the tools. Newer capabilities will emerge in the meantime.
“Where this is all moving is for data and analytics to become ever more real time and predictive,” said Debanjan Saha, General Manager and Vice President of Engineering for Data Analytics at Google Cloud. “Every company wants to know what is going on in its sector and the world beyond. To be competitive, however, it’s not enough to look at what is happening. You need to look at what will happen.”
Saha is responsible for the analytics business at Google Cloud and previously led database services at Amazon Web Services, where he developed and launched Amazon Aurora, a massively scalable relational database in the cloud. Aside from becoming more predictive, another trend is the “democratization” in the use of data and analytics tools, Saha said.
“They’ve generally been in the hands of a few, such as the data scientists that know how to use data to drive insights,” he said. “Down the line, people in different functions will be using these tools for their own purposes, asking questions of the data to get answers in real time.”
Failure to invest in tomorrow’s data and analytics will eventually dull a company’s competitive edge, as before. Technology is never static. Amazon invests billions of dollars every year in next-generation technologies knowing that it cannot rest on its laurels; a disruptor with better tools is just around the corner.
“What’s new and shiny today will lose its attractiveness,” said Nimmo. “But as the custodian of IT systems, CIOs need to ensure their companies’ technological capabilities increase in sensible ways to drive the business forward.”
Otherwise, you’re still mucking around in the swamp.