Data and Its Value is Growing Faster than Data Strategies

Having a long-term data strategy in place that is operational and evolving as the business, data, or technology changes is crucial for success.

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What if you could choose to focus on an element of business that was capable of transforming all of the other elements, and it would also keep you up-to-speed on what needs to shift in the business today and in the future? It would certainly help you prioritize projects.

This is data.

Majesco identified data long-term strategy as one of the strategic priorities for insurers in our Future Trends: 8 Challenges Insurers Must Meet Now.  Why?

Data’s “occupations” within the organization are growing. It is critical to nearly every function, business process, workflow, and analytical process to provide insights for decisions. Used effectively, data is the sixth sense that will allow insurers to operate on a different plane. Data may be analytical in nature, but the results of using it are far more touchy-feely than we might admit.

In a data-driven insurance relationship, customers feel more comfortable knowing that they are “known” by their insurer whether in underwriting a policy or in servicing them—in a personalized way leveraging data. Today’s customers expect more. They want an experience that provides them with what they need to manage their lives or businesses and humanize the entire customer lifecycle. Part of the humanizing aspect is offering niche, personalized products, services, and experiences that align with their specific risk need and use their personal data.

With a long-term data strategy and expertly constructed data platform, every aspect of the insurance business from underwriting to marketing, billing, servicing, and claims visions improve. Imagine how good it feels to see trends on the horizon and think, “We know how to deal with that.”  The future gets brighter. Trepidation and pessimism take a hike.

Yet right now, data’s value to the organization is currently growing faster than data strategies. Insurers need to pay attention to data strategy development and the opportunities it can bring to the organization.

What does a long-term data strategy encompass?

Data is at the heart of insurance.  We’ve been using data for a long time now. It isn’t new. But it is growing substantially. It is overwhelming. Many insurers find themselves barely treading water today…unable to keep up with what is happening with data? In many cases, it’s because the use of data is changing and expanding, and long-term data strategies are necessary. Now, because data reaches into every nook and cranny of the organization, insurers need a strategy that includes:

  • An understanding of what, how, and why data will be used along with a plan for collecting, cleaning, standardizing, and storing data, in all of its various types—structured and unstructured.
  • A cross-functional view of data and governance for keeping data and systems secure.
  • Tools that help with all of that, plus those that assist in visualization and decision-making processes as well as a plan to accelerate the use of advanced analytics such as NLP, AI, and ML take on greater responsibilities.
  • A framework that reduces data maintenance costs and is adaptable, based on scalability, plug-and-play data streams, and the need to connect to partner data sources and models.
  • Leadership and teams that grasp data’s ability to fuel creativity and innovation.

When these things come together, the competitive organization is unleashed, and everyone gets excited. But there are challenges within the organization. When we recognize them, it becomes much easier to see why data proponents need more than just a good idea.

Challenge One: Data volumes are unimaginably large. Data varieties are growing exponentially.

The data we create—transactional data from our core business systems and the data we collect through other sources (imagery, sensors, warning systems, telematics) as input to these systems—are both growing in size and importance. Today’s data volumes are too high to use yesterday’s data frameworks. Plus, the types of data that are coming in, such as telematic streams, are not always static points. The structure of our data collection must be streamlined if it is to accept all the relevant data we need. We need our data “nets” to be in collection mode and we need our data collection systems to understand the data that is valuable to us.  Most insurer data systems were built based on schedules, report run times, calendarized checks and balances that operated like clockwork. Today’s data framework is always on to allow refreshed, real-time insights and analytics. Yesterday’s data silos understood only what they had access to. Today’s data framework releases insurers from yesterday’s analytic constraints.

Challenge Two: The value of particular data streams is unknown, so data use is inefficient.

When we are overwhelmed, we lose sight of what is necessary or relevant. Is yesterday’s data type still useful to us or is there a better way to rate, score, underwrite, or market with different data? Is one type of data a better indicator? Will a new type of data reduce our costs in acquiring business? Will another type of data give us a greater picture of a customer’s propensity to buy or leave or upgrade?

For years, Insurance IT has been consumed with business transformations that include operational reporting, but many insurers weren’t yet ready to consider it in a bigger data strategy. Thats because the data itself wasn’t fully able to contribute to the conversation. Now, new data and analytics are unleashing data’s real value to the business–both operationally and strategically. As NLP, AI, and ML are improving, so are their abilities to model, test and suggest. A long-term data strategy keeps the pulse of data value, reducing waste, acquiring new data, when needed, and making adjustments as the world changes.

Challenge Three: Data technologists are overburdened because analytic power is still difficult.

In insurance, today’s analytic systems must translate data’s language into a more business-friendly form of analytics. This is crucial for underwriters, claims teams, distribution, and marketers who didn’t specialize in data. The end user needs a user-friendly technology bridge that makes sense of data in less time. These tools are easy to use and simplify the work of users, allowing data to grow more meaningful. For example, if an insurer wanted to understand the variables involved in retention rates, access to the data and analytics that allow them to create their own reports and analysis would allow self-sufficiency so data scientists and other specialists can focus on other strategic areas.

Challenge Four: Data needs top-down focus.

Without an extraordinarily strong focus on data as a strategic and vital corporate asset, most insurers struggle to keep up with the necessary changes in a rapidly shifting digital insurance era with new products, channels, risks, data, and technologies. It is crucial to have a foundation based on a long-term data strategy.

A long-term data strategy requires an examination of the sources, types, use, and quality of data as well as the analytics needed to drive operational and strategic value.  Part of this strategy is defining an ecosystem that identifies and utilizes internal and external data sources and accompanying technology such as operational and advanced analytics, and AI, ML, and NLP that will empower a customer-first strategy and achieve tactical and strategic outcomes.

Data strategy’s timing imperative

Data will soon be either the tsunami that swallows the organization or the wave that carries it to success.

Data is becoming more readily available and cheaper. It is turning into a commodity that will allow it to spread across the entire value chain. The devices that will fuel a rise in data are also getting cheaper.

Google, for example, is extremely focused on creating data and analytics capabilities as a service at scale for the industry with a heavy focus on customer centricity, risk management, and analytics. In industry presentations, they note the disparity of data across the value chain that creates challenges and why they are looking at the data holistically rather than building point solutions for different problems like the traditional data and analytics providers do.

Sensor data makes a great example of a “point” application that should be considered under a holistic strategy instead. Because sensor use is on the rise, sensors are becoming cheaper to place and use. Data-generating IoT devices are proliferating in businesses, homes, cars, and health-related areas. At the same time that it is becoming easier and cheaper to gather relevant sensor data, customers across all demographics are growing more comfortable with sharing their data for improved pricing. (See Majesco’s latest Consumer and SMB Consumer reports.) Insurers will have an opportunity to inexpensively capitalize on this growth, but they will need a data strategy to make sure they are capturing the data points they need and monetizing them properly.

The holistic aspect comes into play when insurers begin to personalize service and improve products using individual data streams from people and businesses. The same data that is used for underwriting, real-time pricing, preventive monitoring, and claims, can also be used for value-added services and products such as warranty service, automated maintenance service, restoration programs, and parametric insurance.

Data is changing the value proposition for the entire insurance industry.

Data, effectively used, will always have ground-breaking, business-changing, and mind-enlightening value. Analytics capabilities are poised to be a game-changer for insurance. When new and real-time data, advanced analytics, AI, and machine learning are effectively combined, insurers can have a significant impact across the entire insurance value chain.

In the future, the story will be less about the data, but more about the analytics applied to the data, the insights drawn from it, and increasingly the embedded use of analytics across workflows and business functions… creating an “intelligent core” something Majesco has done with our Spring 2023 release. Insurers will be personalizing products, knowing their customers better, and developing new products far easier—all of it as a result of engaging with the data through an effective long-term data strategy.

Having a long-term data strategy in place that is operational and evolving as the business, data, or technology changes is crucial for success. It assures access to what is needed to remain competitive but also empowers entrepreneurial teams to use their creativity and think big. Recent advances in technology, driven by cloud, ChatGPT, analytics, and data warehouse are bringing a long-term strategy to life to meet insurer business needs operationally and strategically today and in the future.

Are you ready to create a data strategy that will carry you into the future? Contact Majesco to learn more about Majesco Data Analytics Solutions for Insurance.

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Denise Garth // Denise Garth is Chief Strategy Officer responsible for leading marketing, industry relations and innovation in support of Majesco’s client centric strategy, working closely with Majesco customers, partners and the industry. She is a recognized Top 50 InsurTech Influencer and industry leader with both P&C and L&A insurance experience as a CIO and business executive with deep international ties in Asia and Europe through her ACORD leadership role. Denise is an acknowledged strategic thinker, innovation leader, international speaker, and author of thought leadership and articles regarding the key issues and opportunities facing the industry today to prepare for the future.

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