For today’s insurers, data has become a strategic enterprise asset
by Ben MorelandMr. Moreland is VP, Data Practice, for Majesco, industry consultants with over two decades of experience in providing technology solutions, products and services for the insurance industry across all lines of business. Reprinted with permission. Visit here for more information.
May 14, 2020 — Data is vital. Right now, in the middle of a pandemic, no one seems to be arguing that data isn’t absolutely essential. What many people don’t realize however, is that the same attributes that make data essential and vital today, don’t go away when times of crisis aren’t here. Data, effectively used, will always have ground-breaking, business-changing, mind-enlightening value.
Certainly one of the benefits to come from such a negative situation we’re currently facing, is that data’s value is selling itself with a clear voice. While insurers were on a dizzying pace of change before the current pandemic, it has accelerated the level of adaptability needed even more. But there is a hurdle. Without a very strong focus on data as a strategic and vital corporate asset, insurers will struggle to keep up with the necessary changes in the “new normal.” The right philosophy is the foundation needed to design and implement a strong enterprise data strategy.
The Most Vital Data Philosophy — Data as an Enterprise Asset
Every insurance company believes that they know the importance and value of data in their company. We say “believes” they know, because if they truly knew the value of data, there would be an enterprise data governance team that would: (1) at the enterprise level, treat all data as a true enterprise asset – as opposed to a department asset; (2) look at their data strategy and how they plan to use data both internally and externally; and (3) have an organizational structure to fully support that strategy.
It has been well documented that most insurance companies have siloed data, owned by various departments, not the enterprise. The attempted solution has been to create a Huber data storage, Master Data Management (MDM) or data lake solution, and assume once the data is in one of them, they would have full access to it – a traditional “build it and they will come” strategy.
The conclusion assumed that everyone will have full access to any type of analytics or reports that they desire from the data. Some insurers spent tens of millions of dollars in pursuit of this goal, only to fail due to the sheer size and complexity of the effort. Too often it was driven by the IT organization, relegating it to a technology exercise rather than a business-driven strategic project.
Data must be first viewed as a corporate asset, no different from their financial department.
Data Needs an Enterprise Strategy
But most companies do not have an enterprise strategy on how data can provide value to their organization. Many carriers leverage data for predictive analytics by the actuarial department, realizing the value of this data for at least a portion of the enterprise. However, these models can take 4-6 months to develop because with each data set refresh, the data must be cleansed from scratch. Actuaries report that this takes 60 to 90% of the total effort depending on the quality of the data.
As an alternative, AI (Artificial Intelligence) web service applications can provide near real-time model updates. Different departments, such as claims and marketing have done analysis with their department’s data with varied levels of success. Each instance somewhat resembles the actuarial example — the effort takes too long or the results are suspect due to the quality of the data supplied. In each case, we are still dealing with siloed data instead of integrated enterprise data.
It takes a visionary data leadership team to convince the organization that efficiency and accuracy can co-exist. Enterprises need a full enterprise data ecosystem model to establish and define both internal and external data flows and the business value associated with these efforts. When this happens, an insurer’s capabilities change overnight, but the strategy must come ahead of tactical data efforts.
The enterprise data strategy requires a very strong business focus on the use of data and data quality within the enterprise. Data is not an IT asset, it is a business asset. It is the lifeblood of the insurance business, and indeed, the entire insurance industry. It can no longer be an IT initiative to address the quality of the data and how to integrate it together. At the enterprise level, there must be a thoughtful and concerted focus on the business value of data and how both internal and external data can be incorporated into the executive mindset.
This has proved too daunting for most insurers.
Why is Data an Enterprise Asset and not a Departmental Asset?
Most corporate assets are clearly considered an enterprise asset. Budgets always start from the top and are continually broken down into smaller organizations and departments. No company ever tries to start the budget process at the department level and consider it the department’s money that “we’ll share as a good corporate citizen to help the company” as applicable. This would be unthinkable.
For each department or organization to decide what assets are theirs and what is worth sharing would never work. Why then is this approach used with respect to data? You hear statements like, “We need to bring in the Claim division’s data or Product team’s data or Marketing data into a consolidated store.” If you listen carefully, they are not referring to the department from which the data is coming, they are referring to the department’s data as if the department owns it. This kind of thinking adds layers of redundancy and fosters siloed approaches, not to mention losing cross-departmental knowledge and an understanding of common synergies.
While an Insurer does want to integrate their (the company’s) claims data with their (the company’s) policy data, with their (the company’s) marketing data, it is the enterprise’s data. The other part of this misaligned mindset is discovered when only the Claims team “knows” the claims data, and only the Product team “knows” the product data, and so on. The senior leaders all know at a high level how the Claims department should process claims, and what their KPIs are for reporting at the company level. Of course, there are lower level unique data details which may require a department level of understanding, but even in those instances, they are always discussed in business terms.
Carriers must change their mindset about their data. It is the enterprise’s data which must be governed, understood and managed from the very top, no different than any other corporate asset.
Insurance Data Efforts Deserve a Data Management Organization (DMO)
Most insurers have a Program Management Office, or PMO. The PMO’s purpose is to create and maintain a consistent world class project management methodology and process for all project engagements across the company. The PMO establishes policies, processes, and best practices, plus standards, training and governance over each of these. Project managers are expected to execute against these best practices for each project. The PMO doesn’t get involved in individual projects unless they deviate from planned budgets or delivery timeframes, or if they fail to adhere to the established standards.
A similar approach is required for an insurer’s data strategy. Adding a Data Management Organization (DMO) and a governance program can be a game changer for providing valuable, holistic data perspectives. Similar to a PMO, a DMO would:
- Create and maintain a consistent world class data management[i] methodology and process for all data management engagements across the company;
- Train, certify (if possible), coach, and mentor data stewards in not only data management, but also in data delivery, to ensure skill mastery and consistency in planning and execution;
- Manage corporate and data priorities matching business goals with appropriate technology solutions and provide increased resource utilization across the organization — matching skills to data needs;
- Provide centralized control, coordination, and reporting of scope, change, cost, risk, and quality across all data initiatives;
- Increase collaboration across data efforts;
- Provide increased stakeholder satisfaction with data-related work through increased communications, collaboration, training, and awareness;
- Reduce time to market by providing better coordination and the right resources with the right skills for the data projects;
- Reduce data costs because common tasks and redundant data efforts could be eliminated or managed at the central level; and
- Reduce corporate project risk[ii].
Adding A DMO
While a PMO might be more focused on the internal execution of a project, the DMO must address both internal and external data services and projects. The crucial point of the DMO is that it must be governed and understood at the executive level. It sets the corporate objectives for all data initiatives, and the business value of all data initiatives must be clearly understood at the executive level.
The genius of the DMO is in its ability to translate data’s real, enterprise-wide potential, plus its day-to-day value, up to the executive level, where it can promote leadership buy-in. In other words, all of data’s chief users within an insurer gain an internal champion to lobby and lead them in ways they may never have been able to do otherwise. Instead of departments losing control by adding a DMO, they gain an enabler.
Data Needs a Map with Detail
The final step for insurers is to create their data ecosystem strategy and direction. This is more than just documenting the existing data flow. It must take into account where data can be applied to business processes for more effective decisions and business value. For example, one insurer is applying AI to their underwriting process, creating real-time updates to their underwriting models. Another example is bringing an insurer’s own historical data on their customer and product experiences into renewal and underwriting decisions. The focus is now on the value of the data being brought into the decisions to improve them, then to make lower level data corrections at this level.
Data Business Value Must Be Driven by Executives
Many organizations have created CDO (Chief Data Officer) positions or aligned the data group under the CFO. Both of these are great first steps, but they still miss the need for an insurer’s data strategy, direction and projects to all be driven by the executive level and the data asset value understood at the executive level. A CDO should be at the executive table working closely with executives across the organization, eliminating the silos and managing the DMO for the company.
The CDO and DMO should create dashboards to understand the value achieved by data efforts, adherence to the processes and impact. This will ensure that data’s efforts are aligned to business goals and objectives, to help drive better decisions from a business perspective than from a data or IT perspective.
[i] Data implies data and analytics
[ii] Borrowed from: “Goals of an Enterprise Project Management Office (PMO)”, https://project-management.com/goals-of-an-enterprise-project-management-office-pmo/
Majesco Data Solutions
Majesco provides insurers with rich data solutions that enable them in their data journey. Majesco Enterprise Data Warehouse (EDW) consolidates their policy, billing, claims and distribution management transactional data into one consolidated data store. The process requires an insurer’s data governance committee to be integrally involved in the process, bringing business issues to the executives for discussion and decisions at the enterprise level. Majesco Business Analytics (MBA) solution enables business users to dynamically interact with their data to understand how their processes are performing and if they are meeting established KPIs, to perform root cause analysis of anomalies and seasonal trends, as well as to gain insights from a rich set of out-of-the-box dashboards and reports. Customers can use their data from the Majesco EDW to feed advanced analytic solutions because the data is governed and cleansed and updated daily to retrain models on an as-needed basis, and provide AI/ML analysis back to the decision-making steps in their operational transaction system.
Whether you are thinking about starting your journey or you are ready to do so, we would love to have a conversation with you on how we can help you accelerate your path to data mastery.