- Insurers often tend to treat and quantify data as a short-term expense managed for individual functions or projects, rather than as a strategic asset that should be continually nurtured for long-term, enterprise-wide gains.
- Siloed systems, talent gaps, and risk management challenges, are among a multitude of possible obstacles that could hinder insurers from advancing their data management maturity levels and realizing the full value of all the new information and analytical tools at their disposal.
- While not all data and analytics spending should be expected to deliver quick or even easily measurable results, chief data officers often emphasize the importance of regularly spotlighting tangible “wins” to maintain leadership support and funding.
- Upgrading to more holistic data management systems, empowering teams to collaborate across functions, and moving beyond basic risk and cost reduction goals could help insurers capitalize on their data and analytics initiatives to accelerate innovation, bolster competitive differentiation, and ultimately sustain profitable growth.
Why Haven’t Many Insurers Been Able To Fully Capitalize On Their Treasure Trove Of Data?
Insurers are drawing upon a cornucopia of new data sources for everything from customer acquisition to claims thanks to the proliferation of sensors, digitization of physical records, and the growing inventory available from external information brokers. Yet many are still struggling to maximize the full value of data, despite the advanced technologies and analytical tools being put in place.
Interviews with 10 chief data and analytics officers or their equivalent at a wide variety of carriers (including two InsurTechs), as well as a survey of 50 insurance analytics leaders around the world, revealed nearly a dozen significant challenges for companies seeking to fully capitalize on their data to expand capabilities, spur innovation, and enhance growth. Our research found data often siloed by function, system, and platform, and its utilization usually relegated to basic efficiency and cost control initiatives. As a result, many are stuck in the early stages of data management maturity – still striving to make data more accessible, shareable, and actionable.
One particular finding stood out as a possible game-changer. Although the majority of survey respondents indicated they treat data more as an asset than an expense, the attitude and approaches among those interviewed in depth were often far more nuanced and aspirational.
This conundrum is not unique to insurance. As the Deloitte AI Institute noted in a 2021 report, while “data is often viewed as a costly necessity and a byproduct by many companies … more innovative organizations see things differently. They understand that data is a strategic asset,” which was defined as “a holding that appreciates and generates a return.” The difference between these two attitudes in terms of outcomes can be dramatic. “When organizations approach data as a strategic asset, it can open the door to new efficiencies, insights, and capabilities while also enabling emerging technologies,” the Institute noted.
Yet even though data has been the industry’s lifeblood since the first actuary was hired, the ability of many insurers to refine their most basic raw material and produce more impactful insights has likely been hindered by a lack of long-term strategic frameworks and the integration of systems to support them. One insurer interviewed said, “we still treat data more like the plumbing,” while another described data as “more of a commodity.”
Newer players appear to have a head start in rethinking the value of data. One InsurTech interviewed – without ties to legacy systems or commitment to traditional operating models – emphasized the importance of putting data front and center as an asset, “because as data grows and our ability to leverage it expands, that contributes to our success. And as it grows, even more data is generated.”
Such a phenomenon – which this InsurTech referred to as the “data flywheel effect” – means once an accessible infrastructure is in place, information can usually be leveraged faster and more effectively. This can provide fodder for proprietary insights and enable enhanced decision-making across the value chain.
CDOs and senior leadership should also be aware that efforts to improve data management and maximize the use of analytics are not taking place in a vacuum. There are already independent entities tracking and seeking to judge how carriers compare against peers in monetizing data.
One example is YourDataConnect LLC, which in February 2022 released a Data Monetization Index for the property-casualty industry as a whole as well as a number of individual carriers. Such external reviews may soon be as routine as those of longstanding rating agencies evaluating financial strength and sustainability assessment firms monitoring how carriers address climate risk, diversity, and financial equity challenges. Third-party reports on how well insurers capitalize on their data assets are likely to be increasingly sought by distributors, corporate customers, stock analysts, and institutional investors, as well as internal stakeholders such as boards, management teams, current employees, and potential hires.
Where Do Insurers Stand On The Data/Analytics Maturity Curve?
When it comes to data management and analytics maturity, insurers in general appear to be all over the map. “With certain areas we’re on our way, but overall, it’s still early,” said one CDO interviewed – something that resonates with most carriers taking part in Deloitte’s research.
For our online survey, we asked respondents to characterize their maturity in one of three stages – either as Explorers, Adopters, or Pioneers (figure 1).
Only a few respondents characterized themselves as stage 3 Pioneers in their overall operations, with the majority in the middle stage and more than one-third still relatively at the beginning of their data management journey. Size of the responding company did seem to matter, as half of those at smaller insurers (with less than US$5 billion in annual revenue) said they are still Explorers. However, the fact that one in four respondents generating over US$5 billion in revenue remain at the initial maturity level indicates the industry as a whole likely has much work to do to get full value from all the data they collect and purchase.
In addition, while many insurers surveyed may not have yet reached intermediate levels of maturity overall, let alone the most advanced stage, there was a wide disparity in maturity by functions. For example, many insurers surveyed have prioritized core systems such as underwriting/pricing and claims in upgrading their data and analytics capabilities, and therefore tend to be at relatively higher levels of maturity than they are in customer segmentation, marketing, and distribution (figure 2). However, more attention should be paid to those externally facing areas going forward, as they ultimately could differentiate insurers from a strategic (innovation) rather than tactical (cost and efficiency) perspective.
Based on Deloitte’s discussions with insurer data/analytics leaders and the survey results, we elaborated on our three-stage maturity model to suggest specific qualitative benchmarks for progress (figure 3).
Outcomes are what fundamentally distinguish the three maturity stages. At first, Explorers usually are looking to leverage data to better assess incoming risks being underwritten and priced, as well as cut loss costs through improved claims management and fraud detection. These are important goals but should be considered minimum outcomes of data/analytics initiatives.
As they advance to stage 2, Adopters are already transitioning from defense to offense, seeking revenue growth and expanded market share. They routinely base strategic business decisions on more advanced data analytics to determine what products to sell, which segments to target, and what channels and individual distributors to tap.
At the peak of this three-stage maturity model, the convergence of operational and analytic data fuels maximum returns from AI and other capabilities to enhance most business processes. To maintain momentum and spur greater innovation, Pioneers should be continually investing in data and analytics as strategic assets – as much a part of their organization’s culture as are the people leveraging them to innovate products, platforms, and business models, with the bigger-picture goal of sustaining competitive differentiation. “As data kept rolling in and data science kept rolling out models to make the data actionable, we saw monster gains model over model,” noted one interviewee.
What’s Holding Many Insurers Back When It Comes To Turning Data Into A Strategic Asset?
About half of our 50 survey respondents were less than satisfied with the progress their companies have made on data and analytics initiatives. That’s perhaps not surprising given all the challenges raised during our CDO interviews affecting an insurer’s ability to upgrade their maturity level and maximize the value of data and analytics (figure 4).
Consider The View From The Top
Managing and meeting the expectations of C-suite leadership and a host of other stakeholders involved in specific projects is one of the biggest challenges in keeping data initiatives on track and fully funded, according to most CDOs interviewed. They often lamented a leadership mindset that tends to think of data spending as short-term expenditures for isolated initiatives, rather than as long-term commitments to bolster and maintain value creation.
Indeed, leadership may have unrealistic expectations for immediate returns on data investments, according to many interviewees. “These initiatives often take multiple years to bear fruit, and senior leadership doesn’t always have a lot of patience for the long-term nature of data and tech investment,” said one CDO, while another noted that “a lot of time is spent in reminding peers and executives we are playing the long game.”
Do The Math: Hard Metrics May Be Hard To Come By
Those interviewed often suggested that not all data and analytics spending can or should be expected to deliver quick or even easily measurable returns. This may be particularly true when they are part of broader transformation initiatives or are designed to help establish basic data management infrastructure – often necessary to overcome many of the challenges cited in figure 4. Sometimes it can be difficult to even provide a realistic estimate of the costs involved, let alone tie them quantitatively to outcomes, CDOs noted.
At the same time, interviewees conceded that expecting open-ended funding commitments from leadership may also be unrealistic without being able to provide some tangible proof of progress and improved outcomes. They often emphasized the importance of spotlighting periodic “wins” to maintain momentum and funding.
Use-case approaches were cited as a compelling way to retain leadership support, especially if they are tailored to a particular function and backed by a key performance indicator – such as cuts in processing time, improvements in closing rates, and reduction in fraudulent claims. These should suffice, at least in the Explorer stage of maturity, when insurers are focusing on improving the precision of pricing and effectiveness of claims handling – activities that can provide quicker, more quantifiable results.
However, it is also likely important for CDOs to keep leadership focused on a bigger-picture vision, or else the use case approach could lead to a continuation of fragmented architectures and siloed thinking.
Resolve Rank And File Issues
The “talent crunch” ranked high on the list of concerns among survey respondents – trailing only integration issues and data quality challenges. CDOs uniformly lamented their constant and worsening struggles to acquire the right talent, upskill existing employees, and retain those with the necessary capabilities to realize the true value of data and supporting analytical technologies.
Meanwhile, gaps in data literacy across the value chain were often cited for accentuating the disconnect between leadership expectations and execution of goals. Fragmented data management architectures, with information and responsibilities spread throughout the enterprise, can also present challenges in maintaining consistent standards and possibly hindering speed to market.
Reconcile Incompatible Systems/Processes
One in five insurers surveyed characterized their data management governance process as “ineffective,” while one in four were only neutral on the question. A lot of that likely had to do with shortcomings cited in our one-on-one interviews, from the absence of holistic data governance to a lack of alignment between how data is generated versus how it might be used for advanced analytics. For example, the rapid influx of data from both internal and external sources is unlikely to deliver full value as long as information is stored in siloed and often incompatible legacy systems – making it tough to share, let alone scale for optimum value.
One carrier said that “if you want reusability and the efficiencies of scale across the organization, the walls between functions and business lines must be broken.” This is not likely to be an easy task for many insurers, noted one CDO, who observed that “in some cases, we’re erasing decades of significant legacy system obstacles.”
Address Risk Management Challenges
As more personal data is collected and purchased by insurers, concerns over cybersecurity and privacy have multiplied. Yet only 32% of data leaders surveyed said they collaborated very closely with those in compliance. Meanwhile, the reliability, verifiability, timeliness, and relevance of data are among the many quality concerns that could undermine utility and value, while raising additional risk concerns.4
Next Steps: Bending The Maturity Curve To Bolster The Value Of Data And Analytics
An insurer’s ability to overcome obstacles and reach higher maturity stages so they may harness greater value from data and analytics investments will likely differentiate the leaders from laggards in an increasingly data-driven economy.
While “making a purposeful shift away from managing a mesh of siloed, inconsistent data,” as well as treating data as a strategic asset rather than an expense, are both important steps, according to the Deloitte AI Institute, “reaching the point of maturity where enterprise strategy and operations are data-driven can require a medley of phased actions.”5
Insurers might consider laying out a road map to establish a more holistic data management and analytics strategy linking the two sides of the organizational “brain” – technical systems and processes versus attitudes and approaches of the people executing the plan (figure 5).
For example, making data more accessible and sharable among those in underwriting, claims, product development, and marketing could help overcome a major challenge many survey respondents cited by freeing information currently trapped in siloed legacy systems. Many insurers have already laid the groundwork for more holistic data management by migrating to cloud platforms as well as by bolstering cybersecurity and privacy protocols – but often have yet to enable interoperability among various functions, departments, and lines of business.
Data quality was also identified in the survey as a top challenge, which is why many of the CDOs interviewed already conduct regular audits for data accuracy, timeliness, and relevance. This can be a particularly important consideration when buying information from the growing number of third-party providers, given that insurers don’t have direct control over data collection and validation.
Usher In Attitude, Cultural Shifts
To reach the highest maturity stage as Pioneers, attitudes and approaches toward data management also should be altered, emphasizing widespread collaboration as the norm. Our survey found that while data and analytics leaders understandably work very closely with IT (78%) and cybersecurity (54%), only 18% do so with those in product development, marketing, or distribution.
A good start would be to make sure those in line of business and external engagement functions are working hand in hand with CDOs and IT to embed analytics into core processes and functions. The overriding goal should be to make data more actionable in creating new coverages, targeting niche segments, and optimizing distribution productivity.
For instance, one insurer interviewed said their data science team was working with heads of distribution to pinpoint for wholesalers which retail agents might be more likely to market their products based on selling behavior and territorial demographics. The carrier believes this data initiative could be especially helpful for intermediaries unfamiliar with their company’s inventory or those prospecting in new markets.
Meanwhile, insurers should be emphasizing data awareness and analytical skills as core competencies, taking steps to raise literacy levels and bolster capabilities in training and recruitment. At the highest levels, CDOs should be active contributors, leveraging advanced analytics when senior leadership determines an insurer’s strategic goals and sets investment and budget priorities.
Last but not least, to reach the highest maturity level, data-driven decision-making should become part of an insurer’s organizational culture and standard operating philosophy. As one CDO noted, data initiatives usually “feel more like basic blocking and tackling. Too often there has been a lack of opportunistic, forward thinking aimed at taking data and analytics to the next level.”
Tapping Data And Analytics As The Insurance Industry’s Sustainable Power Source
Most insurers have been focusing on delivering the basics when it comes to leveraging new types of data and technology to get a better handle on risks, save time, and cut costs. Such foundational goals, however, have become table stakes, as the industry continues to lay the infrastructure to power their operations in an increasingly data-driven, digital economy.
Insurers should be thinking bigger picture, taking steps to raise the data literacy and capabilities of their personnel as well as advance their operational maturity. They should be elevating data and analytics from an enabler of efficiency and risk reduction to a differentiator generating innovation and growth.
Insurers should also be aware that they may not be able to address the data challenges cited here at a pace and timeline of their choosing. As noted earlier, third-party firms are already assessing how well carriers capitalize on their data and the emerging technologies that make sense of it all. In addition, competition is being exacerbated by InsurTechs unburdened by many of the legacy systems issues hampering more established carriers. Meanwhile, more noninsurance players are entering the market with their own proprietary data streams to tap – such as auto makers and online retailers.
One InsurTech interviewed cited the advantages of being unencumbered by “legacy systems that are not compatible and don’t make it easy to cross-reference or even access data.” Describing the ease with which they can tie claims information at the policy type and agency level back to underwriting, pricing, and marketing decisions, this CDO said insurers should be routinely leveraging their wealth of data to “blow up the traditional system and start from scratch the way business could and should be done.”
Whether insurers treat data as just another infrastructure expense to be managed or as a differentiating strategic asset to be continuously nurtured is therefore likely to determine not just profitability but competitive viability. Indeed, where once data was commonly referred to as “the new oil,” going forward insurers should be transitioning to a state where data is their new sustainable energy source and perpetual growth engine.
originally posted on deloitte.com by Cindy MacFarlane Sam Friedman Namrata Sharma
Cindy MacFarlane | Managing Director | Deloitte Consulting LLP
Cindy MacFarlane is a managing director at Deloitte Consulting LLP, leading Deloitte’s Insurance AI, Data Engineering, and Data Operations practice. She has over 24 years of experience across property-casualty and group life and health insurance, advising insurers in how to establish and execute data modernization programs involving cloud, AI, governance, master data management, analytics, and data DevOps implementations.
Sam Friedman | Research Leader | Insurance
Sam is the insurance research leader at the Deloitte Center for Financial Services, putting his journalistic skills and three decades of industry experience to good use analyzing the latest trends and identifying the major challenges confronting the property-casualty and life insurance industries. Sam joined Deloitte in October 2010 after 29 years at National Underwriter P&C, where he served as editor-in-chief.
Namrata Sharma | Research and Insights Manager | Insurance
Namrata Sharma is a research and insights manager for insurance at the Deloitte Center for Financial Services. Prior to joining Deloitte in November 2021, she had 15 years of research and due diligence experience in investment banking, credit analysis, and asset management with major financial services firms, and launched her own B2C startup.