Whether its to achieve targeted product development, improve risk assessment and forecasting, or to gain insights into how an electorate may vote, analytics and Big Data take center stage across all aspects of life, including business, politics and sports. Research firm IDC predicts revenue from sales of Big Data and business analytics applications, tools, and services will increase more than 50%, from nearly $122 billion in 2015 to more than $187 billion in 2019 and insurers are likely to account for a significant portion of that spending.
But while you may be convinced that analytics could transform everything from service and channel personalization to underwriting to customer retention, you probably have doubts about how to realize that potential. Many insurers continue to struggle to realize meaningful and measurable returns on their investments in data and analytics. Gartner recently reported that while Big Data investment is up, many companies acknowledge that their efforts and investments are stuck at the pilot stage.
There are numerous forces, including regulatory requirements, new competitors and increased frequency of natural catastrophes that make the ability to rapidly and accurately gain insights into customers, markets and risks a must. How can you reconcile that imperative with the reality that many data and analytics initiatives take too long, are expensive, and dont always show a clear return on the investment? This was a recurring theme at insurance industry events I attended this past fall, including the Insurance Analytics Forum. Along with impressive success stories about how insurers are using analytics to get closer to their customers and to improve operations, there was honest discussion about the persistent challenges in achieving these kinds of benefits.
Governance drives analytics success – That was the theme of a panel discussion hosted by TCS, featuring Novarica on optimizing analytics and Big Data investments to achieve customer knowledge and insights. Successful customer analytics initiatives have a clear focus or goal and dont try to do too much at once, the panel agreed, adding that governance is critical to reducing the costs and time it takes to gain the capabilities to obtain actionable knowledge.
In an interview at the conference, Mitch Wein, VP, research and consulting at Novarica, said data governance represents 40% or more of the challenges of actually executing a project. This boils down to who is going to make a decision about the data, who is going to own the data and who is ultimately going to arbitrate how the data can be used and where it can be used. Insurers should establish a data governance committee that includes both senior executives as well as operational mid-level managers. They should also develop a data strategy and roadmap that clearly points out the current-state situation, the pain points and the strategic objectives as well as defining an enterprise strategic blueprint for data and a roadmap to get from the current spot to where the organization wants to go, Wein said.
Another obstacle can be corporate culture, since commitment to becoming a data-driven enterprise inevitably changes roles and relationships throughout the organization. Related to this is the necessity of integrating your analytics team within the organization rather than setting it apart as a special, exclusive unit. At Zurich North America, for example, Im acutely aware of making sure my team is integrated and accepted, said speaker Peter Hahn, head of predictive analytics.
A related, and growing, challenge is finding (and keeping) skilled professionals to fill those new or changed data and analytics roles. Many carriers are looking outside of insurance for this talent, and will need to educate and motivate these people. According to speaker Jay Rajendra, chief analytics officer, Arch Capital, its essential to convey that the work these teams do is helping to transform the insurance industry. The work we do has an impact on the bottom line and makes a difference, he said.
Offering opportunities for professional development and growth also are critical, said Zurichs Hahn. Development is a critical expectation. Analytics professionals need to be challenged and learning and feel they are progressing, he said. Data and analytics professionals dont join your teams fully formed or accomplished. They are looking to expand their skill sets.
If youre dissatisfied with the results so far of your analytics efforts, it may be time to reassess how youre structuring and staffing these activities. Do you think your corporate culture is up to the challenge?