In God we trust, all others must bring data.
– W. Edwards Deming
Everyone is talking about data all the time. “Data is the new oil,” is one of the most famous clichés among data-oriented people who don’t actually understand data science. 1)Listen to this podcast to understand the point. This data talk is a form of mass illusion as collecting data is very different from actually deriving meaningful and valuable insights from the data available. Many people have emphasized the importance of data for the future of insurance business, and there probably is n.
It also looks like that insurance companies and their senior executives are constantly talking about business intelligence (big) data, data analytics, data mining, and data science. 2)Sure, consultants are talking about this a lot too. Every insurer recognized the importance of data management and data analytics. According to SMA’s research, over 90% of insurers have data and analytics initiatives under way in 2017.
These data topics, whether specifically big data hype or data and analytics hype in general, are vital for various industries (including insurers, banks, and other financial services companies), and as it has been stated over and over again, data should prima facie improve the quality of decision making drastically. With data, we should be adequately equipped to identify, select and prioritize most important issues and come up with the best solutions to address these matters. All organizations that want to be successful and high-performing should encourage the establishment of data-driven organizational culture to make and take data-based behaviors, actions, and decisions. 3)Dunlea, E. (2015). “The Key to Establishing a Data-Driven Culture“. Gartner, 30.11.2015. As Vapor IO’s CMO Matthew Trifiro has argued:
Time and money are your scarcest resources. You want to make sure you’re allocating them in highest-impact areas. Data reveals impact, and with data, you can bring more science to your decisions. 4)This quote echoes Peter Drucker’s important insights on the fundamental nature of time.
So, in the end, analytics is all about saving time to make better decisions. Easy, right?
Talk is cheap, actions are expensive
Take Finnish LocalTapiola’s (LähiTapiola) as an example of the new data-driven mindset. LocalTapiola’s CEO Erkki Moisander has stated that the new-age insurance is based knowledge that is derived from vast amounts of data. 5)It’s not only LocalTapiola that is striving to be an “intelligent enterprise” as for example OP Financial Group’s CEO Reijo Karhinen has been talking about “financial intelligence” (Finanssiäly). This kind of thinking exemplifies a very different kind of mindset when compared to more traditional insurance executives. 6)LocalTapiola is currently undergoing a massive transformation journey to be the “lifelong security company”.
Intelligent life insurance will be a product for a new era. People will be able to provide mass information on how they are doing and what’s going on as dictated by relevant data privacy laws. When [LocalTapiola] has enough data, we are able to shape even better products. Customers are also motivated with discounts and similar benefits.
– Erkki Moisander, CEO, LocalTapiola (Qlik, 20.9.2016 [Translation: TB])
As another example, let’s take a look at a Nordic insurer If P&C Insurance. In 2016 it was made public that If P&C Insurance had implemented Cortana Analytics Suite to tackle with big data analytics, and If P&C Insurance has also been previously highlighted as a general reference for similar kind of data analytics implementations. So it seems like that quite a few Nordic – both life and non-life – insurers are taking steps towards actually learning what all the hype around analytics and business intelligence buzzwords is really about. 7)Chang, Y. C. & Nelson, H. (2017). “Data Opportunities in Insurance“. Silicon Valley Data Science, 2.2.2017.
So it seems like that at least some insurers want to move beyond the hype, and it’s true that there are a lot of things that can be realized in terms of data and analytics. All this buzz and fuss around these issues is not just hot talk but rather it exemplified the fact that execution and realizing the (economical) benefits can be very hard. On one hand, senior insurance executives hear and read all things great and beautiful, and on the contrary, they face their everyday issues in their particular organization. Data analytics, alongside big data, predictive analytics and machine learning, have the real potential to create valuable and actionable insights that can be rendered into decisions and actions. As it has been noted by several companies, analytics is not a one-off investment but rather a journey as shown in Figure 1.
Insurers, like any other financial services companies, love data, and they have a long history in collecting, organizing, interpreting and using data, but they have are facing their own problems in utilizing all the data they have. For example, the basic business model of property and casualty insurance is under considerable pressure. There are considerable challenges ahead. It’s noteworthy that according to Accenture’s 2015 consumer research, eight out of ten insurance customers are looking for personalization, and it seems like that the clients are really willing to switch providers if they don’t get the service they want. 8)Accenture (2017). “The Voice of the Customer: Identifying Disruptive Opportunities in Insurance Distribution“. Accenture Financial Services 2017 Global Distribution & Marketing Consumer Study: Insurance Report.
Data is about the culture, not the technology
I went to an insurance branch office last in 2007 or 2008. After this, I have purchased, changed, and upgraded all of my insurances online – and this has been a trend for some time in Finland. If something were to happen, I just access my 24/7 online account, check out my current coverage, and file a claim if I need to. More and more insurance consumers demand digital channels of their choice, compare insurance products, coverages and various options available online, purchase insurances through digital channels, and share their positive and negative experiences and feelings via multiple channels. 9)See, for example, this very interesting claim case from Finland. As the volume, variety, velocity, and veracity of data continues to increase, new creative solutions are becoming available for insurers and other financial institutions to turn all this data into insights and actions – to make data useful and valuable. So why are insurers still struggling with all of this? 10)West Monroe Partners (2017). “Two-Thirds of Insurers Find Data Quality Lacking, Hampering Analytics“. 27.1.2017.
The difficulty is not so much about technology. To adapt a quote from an American-Canadian writer William F. Gibson, the technology is already here, albeit unevenly distributed. The main problem is actually outdated business models and organizational challenges related to this. Accenture’s Technology Vision for Insurance 2017 revealed that “87 percent of insurance respondents agreed that we have entered an era of technology advancement that is no longer marked by linear progression, but by an exponential rate of change” and “86 percent of insurers say their organization must innovate at an increasingly rapid pace just to keep a competitive edge.” In addition, according to Accenture’s research, “94 percent of insurance executives agree that adopting a platform-based business model and engaging in ecosystems with digital partners are critical to their business.”
Sure, insurers, as well as other companies, differ in the way they actually invest in data and analytics capabilities, and how they develop their analytics operating model (see Figure 2). As noted by PwC’s Paul Blase and Anand Rao in a very good paper on analytics operating models, “Many large organizations have a mix of several data and analytics operating models without clarity on their purpose, how they work together and go-forward plans to build new capabilities to support a DIAO [Discovery, Insights, Actions, Outcomes] approach that will continue to progress in speed and sophistication”.
There are various ways to realize analytics organization as noted by Accenture’s Julio Hernandez, Bob Berkey, and Rahul Bhattacharya. Each model has its own strengths and weaknesses that primarily depend on sponsorship, leadership, funding, and governance. For a very small traditional niche insurer, it probably doesn’t make sense to prop up a huge analytics function while for some others it might be very important. It’s notable that the insurance ecosystem is currently changing incredibly fast, and there are numerous catalysts contributing to the ongoing disruption. 11)Niddam, M., Gard, J.-C. & Koopmans, J. (2015). “Insurance and Technology: The Disruptive Force of Insurance Ecosystems“. BCG Perspectives, 1.5.2015.
It’s true that most insurtechs differ a lot from traditional insurers just because they invest heavily in their digital and analytics capabilities, and while incumbents are still trying to scramble together a big business case, these agile companies are already rotating to different technologies and systems. Tieto’s Christian Segersven has noted that the Nordic insurers have not been able to capitalize on the current trends. “The [Nordic insurance] sector has undertaken only modest development and fine-tuning. – – At the same time, the insurance marketplace has been flooded with globally competitive start-ups, and traditional business models are under assault from these nimble startups that are rewriting the rules”, Segersven says.
But one thing is clear: those insurers that do not invest in analytics will face tough times, or as one McKinsey article frames it, “a significant competitive disadvantage.” Why is this? As Tieto’s Segersven argues, operating model can be changed only with the help of right kind of data. “The true combination of data, cognitive analytics and customer orientation can be used to develop customer service and open up new business opportunities”, Segersven argues.
Insurers that acknowledge this are already taking steps to complement their internal data sources with (open) external data and collecting, managing sharing data with their partners. 12)Manral, J. (2015). “IoT enabled Insurance Ecosystem – Possibilities Challenges and Risks“. arXiv:1510.03146 [cs.CY] Sure, the GDPR will affect insurers as well as any other industries for that matter, and just keep in mind that the GDPR will affect data brokerage business, so selling (or buying) data is not so easy anymore.
Data available to them is expanding exponentially from a broad range of sources—not only traditional data providers, but also public entities and enterprises generating data “exhaust” from business activities.
– Data-rich, Profit Poor (Source: Accenture)
So, instead of coming up with placebos, insurers need to become data-driven organizations, and as Ari Libarikian, Kia Javanmardian, Doug McElhaney, and Ani Majumder from McKinsey point out, this means that the insurers need to “rethink their approach to building and managing data and analytics assets and develop distinctive go-to-market capabilities that allow them to offer clients data-centric solutions.” So we are not actually talking about getting rid of the legacy systems and improving random processes here and there but rather insurers need to strategize first, analyze later. Insurers, as well as other companies across different industries, face a daunting challenge with their data management efforts; there are so many so many data sources ranging from various internal sources to multiple external sources that it’s tough to make sense all of the data available. 13)Birckhead, D. (2014). “Customer 360: Understanding Your Customers in the Digital Era“. 13.12.2014.
You’ve got data? Just monetize it
Data and analytics have always been important for insurers, banks, and other financial services companies alike. Actuarial science – a mixture of mathematics and statistics to assess risk, predict losses, and predict the price of risk (risk premium) – has always been very data-driven and model-intensive. As I pointed out earlier, insurance companies of today are living in the midst of (big) data overload, and every insurer out there faces serious issues with managing, mapping and understanding their data, which, if done right from the strategic point of view, might be a distinctive capability that all competition can’t copy and offer real opportunities for data monetization.
As both Accenture’s report and McKinsey’s article argue, insurers are still laggards in the adoption and effective use of data analytics, as well as investing in this area. As Kinzie states, “while [insurers] do see the need to invest in analytics, they are typically focused on using it to understand the past rather than shape the future.” This trend is also confirmed by a recent research carried out by SMA as “80% of survey responders indicate they have no plans for investing in cognitive computing, and 37% have no plans for investing in data and text mining”. As Accenture’s John Cusano argues in a recent blog post, “insurers are turning to insurtech, whose digital products and platforms can help them in their quest to innovate quickly and at scale.” Furthermore, as another Accenture report on insurtech demonstrates, insurers are currently investing heavily in analytics/big data, artificial intelligence/intelligent automation, and the Internet of Things/connected insurance insurtechs (see Figure 3). The focus of incumbent insurers is clearly shifting towards a certain set of promising technologies, and every single area of these insurtechs presents various possibilities for incumbent insurers to grow in terms of creating new revenue streams and alleviating cost pressures.
So what should insurers do? McKinsey has put forward a five-step model for insurers to launch data and analytics initiative (see Figure 4). This model offers clear guidance in conceptualizing, initiating, designing and managing data analytics initiatives. 14)Insurers must, of course, make sure that they have a good assessment of the overall market and business potential before conducting Phase 1.
Accenture Strategy’s John Mulhall, Berend de Jong, and Ivo Weterings have recently published an interesting point of view highlighting how insurance companies could monetize their data to generate new revenue streams and competitive advantage in the midst of the ongoing disruption. In a paper titled “Data Rich, Profit Poor: Tapping the Revenue Potential of Insurers’ Data” the authors estimate that data monetization could be worth as much as $6-8 billion of new annual profit for the global insurance industry. The authors argue that insurers might have an opportunity to turn their data and insights into new revenue streams and competitive advantage. As previous research from Deloitte points out, every insurer needs to come up with a data management blueprint assess their current maturity level of existing data management capabilities, ensure executive engagement, sponsorship, and recognition of the (economic) value of data management programs, and make sure that there is an enterprise-level buy-in.
Data monetization, as it has been pointed out by many, is not to be taken lightly and it’s not very easy. As Barbara H. Wixom and Jeanne W. Ross argue in their article, “How to Monetize Your Data,” published in MIT Sloan Management Review, “Impressive results from data monetization do not transpire from single ‘aha’ moments. Instead, they stem from a clear data-monetization strategy, combined with investment and commitment.” Every data monetization effort has to be based on clear understanding of the current capabilities and skills. The importance of strategic planning, and having a data monetization roadmap, should not be underestimated. As Ravi Kalakota says, “Successful data monetization requires the ability to fully exploit data across organizational and application silos.”
It’s a fact that the only way forward is true customer-centricity but no business executive is really interested to read academic service-dominant logic articles. In the end, very few clients care about what happens inside the black box, namely the firms, but the importance of co-creation shouldn’t be over or overestimated. What I want my job to be done, not a relationship or demands for loyalty. I don’t care how my insurers get things done as long as they are able to redeem their value proposition when I really need their help – personally, I don’t care about anything else.
So, in the end, it’s up to the insurance companies and their preferred ecosystem partners to offer me the personalized services I really need; and it’s a shame, for example, my current insurance provider has not never ever contacted me about my current coverage, nor have they provided me any exclusive benefits or rewards. As I have pointed out in my earlier article, insurers and banks have different innovation patterns according to Gartner. 15)If you work at an insurance company operating in Finland, give me a call. I am more than ready to switch my insurance company. Instead of offering me a new insurance product now and then, it would be awesome if insurers would be able to use their valuable, underutilized data assets such as historical transaction data, customer service requests, demographic insights, etc. to create new revenue streams as suggested by Accenture and Deloitte reports.
As a 2015 survey conducted by SAS Institute and Intel points out, telecom, media and insurance place a lot of focus on big data, new data, and analytics. Now it’s time for insurance companies to redeem this focus in real life.
Photo credit: Foter.com
|↲1||Listen to this podcast to understand the point.|
|↲2||Sure, consultants are talking about this a lot too.|
|↲3||Dunlea, E. (2015). “The Key to Establishing a Data-Driven Culture“. Gartner, 30.11.2015.|
|↲4||This quote echoes Peter Drucker’s important insights on the fundamental nature of time.|
|↲5||It’s not only LocalTapiola that is striving to be an “intelligent enterprise” as for example OP Financial Group’s CEO Reijo Karhinen has been talking about “financial intelligence” (Finanssiäly).|
|↲6||LocalTapiola is currently undergoing a massive transformation journey to be the “lifelong security company”.|
|↲7||Chang, Y. C. & Nelson, H. (2017). “Data Opportunities in Insurance“. Silicon Valley Data Science, 2.2.2017.|
|↲8||Accenture (2017). “The Voice of the Customer: Identifying Disruptive Opportunities in Insurance Distribution“. Accenture Financial Services 2017 Global Distribution & Marketing Consumer Study: Insurance Report.|
|↲9||See, for example, this very interesting claim case from Finland.|
|↲10||West Monroe Partners (2017). “Two-Thirds of Insurers Find Data Quality Lacking, Hampering Analytics“. 27.1.2017.|
|↲11||Niddam, M., Gard, J.-C. & Koopmans, J. (2015). “Insurance and Technology: The Disruptive Force of Insurance Ecosystems“. BCG Perspectives, 1.5.2015.|
|↲12||Manral, J. (2015). “IoT enabled Insurance Ecosystem – Possibilities Challenges and Risks“. arXiv:1510.03146 [cs.CY]|
|↲13||Birckhead, D. (2014). “Customer 360: Understanding Your Customers in the Digital Era“. 13.12.2014.|
|↲14||Insurers must, of course, make sure that they have a good assessment of the overall market and business potential before conducting Phase 1.|
|↲15||If you work at an insurance company operating in Finland, give me a call. I am more than ready to switch my insurance company.|