Data Insurance

5 Steps to a Winning Data Strategy Roadmap

In our swiftly evolving digital world, the ability to leverage insights from data is a key factor in determining the leaders and laggards in every industry. Recognizing the limitations of their legacy systems and processes when it comes to their ability to use data, insurers are responding by making data modernization a major priority for their organization. For most insurers, the journey to a modernized data infrastructure requires first understanding the challenges inherent to legacy data systems, and then designing a robust data strategy roadmap that incorporates the organization’s particular strengths, limitations, goals, and technology stack.

But how can insurers select and incorporate the right mix of technologies to advance their business goals? Based on our experience leading data modernization projects with our insurer partners, implementing these five steps will help you craft a roadmap to a modernized data infrastructure that can evolve rapidly and continuously to overcome any threat or meet any competitive opportunity.

Key Steps of a Data Strategy Roadmap for Insurers

Step 1: Map Your Current Environment, Define Terms & Determine Business Goals

This first step will focus on building this mindset by helping you establish a clear sense of what data you have, where it’s stored, how it moves through your organization, who uses it, and why.

To begin, insurers should map their current environment. In this particular case, the mapping exercise should incorporate data visualization, including a map of the pathways data takes as it enters, circulates, resides in and, in some cases, exits your enterprise. For this phase, it doesn’t matter whether the technology within your environment is a legacy solution or a new one; what’s important is the data visualization itself.

When undergoing the mapping exercise, assess and establish measures ensuring consistent understanding across your organization of the various types of data, the repositories where it’s stored, and the systems that use it. In other words, insurers must clearly define terms so that their internal teams know if there are separate underwriting, claims, actuarial, and financial data marts; if the marts have different names for different business lines; etc.

Equally foundational to your data strategy roadmap are key business outcomes. By identifying measurable goals in the early stages, you will not only supply business users with concrete progress metrics, but also establish the signposts that will guide your overall data modernization strategy.

Step 2: Identify the Primary Components Your Platform Requires

The next step is to identify the major components required for your ideal data platform. These components fall into four general classifications: (1) data sources, (2) data hub/data lake, (3) analytics/BI, and (4) business access. The basic definitions and considerations have been outlined below:

Data Sources.  The sources you choose should support the business goals defined in the first step. Every modern data strategy in insurance includes some mix of the following four basic types of data sources:

  • Internal structured data such as a policyholder’s name, address, and policy number.
  • Internal unstructured data such as narrative details contained within a claim.
  • External structured data such as information in an ISO bureau circular, or pressure readings from a policyholder’s plumbing sensors.
  • External unstructured data such as insights collected from studies, news items, weather pattern reports, social media postings, or even photographs taken by a drone, etc.

Data Hub/Data Lake. Known as either a data hub or data lake, these terms refer to the modern repository where your data resides, and are frequently based on technologies such as Hadoop or MongoDB. These repositories require tools to extract, reconcile, and transfer data from legacy systems into the data hub as well as other tools for integrating data with business systems, such as your core suite. When using a data hub/data lake, you will also need to determine where the hub will reside (e.g. on the Amazon Web Services (AWS) cloud).

Analytics/BI. Layered on top of your data hub will be various tools and technologies for analyzing data and deriving data-driven insights. With data hubs typically containing billions of data points, it’s humanly impossible to fully mine, analyze, and understand all of the available data. Fortunately, applying AI-based analytics solutions can turn overwhelming quantities of raw data into usable insights.

Business Access. Simply put, this is the interface layer for your business users. When considering this component, it’s good to evaluate various specialized technology tools that can help you build dashboards that make it easy for your business users to perform analytics modeling, obtain reports, and complete other self-service tasks.

Step 3: Include Capabilities Required for Delivering Continuous ROI

As data modernization initiatives bring together stakeholders across the entire enterprise, success should be continuously redefined as part of a process, rather than designated with a specific endpoint. To deliver business value continuously, you should build the following characteristics into your data strategy roadmap, as required: modularity, repeatable processes, automation, and effective support model.

Some basic approaches and explanations to building these characteristics include:

  • Modularity. Organize your deliverables into smaller, logical capabilities, as determined in the mapping step. Then leverage agile development and a DevOps approach to deploy capabilities quickly and frequently within each module, continuously iterating to add improvements and harvest gains to apply to the next cycle.
  • Repeatable Processes.Identify the processes within your data strategy and execution that can be repeated, and then standardize these processes to speed up development, rollout, and business utility. Throughout this phase, plan to work closely with business process analysts to determine which processes are essential and which are no longer needed in the transformed state.Given the size and scope of data modernization, you’ll need tools and technologies that incorporate increasingly sophisticated automation capabilities. Make sure you consider your organization’s current automation maturity and the roadmap for each technology in your stack.
  • Effective Support Model.Long-term data-driven operations require developing and implementing a support model that fits your enterprise. This requires you to look at the skills and resources on your team to plan your training and hiring accordingly. At times, this may mean partnering with vendors for necessary support.

Step 4: Focus on the Top 3 Target Outcomes

Although your data strategy roadmap includes nuances specific to your enterprise, a winning data strategy roadmap should measure these top three most important outcomes for any initiative at any organization :

  • Excellent Data Quality and Accuracy. High-quality, accurate data is fundamental to extracting real-time insights. Anything less sets your enterprise up for disappointment, and potentially even adverse business outcomes.
  • Democratized Data for Business Use. When data is democratized, everyone in your organization has access to it, meaning there are no gatekeepers that create bottlenecks during the process of gaining and using insights.
  • Reduced Time to Market. With the ability to integrate analytics into workflows, such as pricing and underwriting, you can significantly reduce time to market from the traditional industry timeline of months, down to weeks or even just a few days.

Step 5: Build in the Hallmarks for Success

Last but not least, your data strategy roadmap should build in hallmarks for success. Though not exhaustive, the following list contains characteristics common to those who achieve the highest levels of data modernization satisfaction:

  • Team up with the Business. Strong relationships with key business stakeholders provide you with powerful champions for overcoming organizational inertia and keeping your data transformation initiative on track. Regularly delivering business-centric features and capabilities helps sustain enthusiasm.
  • Establish Data Governance. Robust data governance is a critical predictor of favorable outcomes. Never compromise on this characteristic.
  • Use Proven Approaches. Leveraging proven architectures, frameworks, methodologies and delivery processes not only reduce failure risk but also boost the likelihood of advantageous outcomes.
  • Get the Right Technologies. As you identify and evaluate the individual tech components for constructing your data platform, take into account:
    • Maturity of the solution and the ecosystem surrounding it.
    • Availability of ample resources for understanding and modifying tools.
    • Strength of the user community for collaboration and problem-solving.
    • Ability to take an incremental development approach to enable continuous delivery and innovation.

Incorporating the aforementioned steps into your data strategy roadmap will set you up for modernization success and better business outcomes. With data-centric capabilities being a new imperative, insurers who haven’t already made the move to modernize their data infrastructure will risk not only their bottom line but also their position in the industry.

For a real-life example of building a data modernization strategy, check out Pekin Insurance’s 3-step approach to a winning data strategy.