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The Data Journey Into the New Normal

In the middle of a pandemic, no one seems argue that data isn’t essential. What many people don’t realize is that the same attributes that make data vital today won’t go away when the crisis ends. Data, effectively used, will always have ground-breaking, business-changing, mind-enlightening value.

Certainly, one of the benefits from the crisis is that data’s value is selling itself with a clear voice. While insurers were already on a dizzying pace of change, the pandemic has accelerated the need for adaptability.

But there is a hurdle. Without a very strong focus on data as a strategic 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 it knows the importance of data.  We say “believes” because if the company truly knew the value of data, there would be an enterprise data governance team that would: (1) treat all data as a true enterprise asset – as opposed to a department asset; (2) look at the data strategy and how the company plans to use data both internally and externally; and (3) have an organizational structure to fully support that strategy.

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. The company assumes that, once the data is in one of them, everyone would have full access to any type of analytics or reports that they desire from the data. Some insurers spent tens of millions of dollars, 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 the financial department.

Data Needs an Enterprise Strategy

But most companies do not have an enterprise strategy for data.  Many carriers leverage data for predictive analytics by the actuarial department, but these models can take four to six months to develop because, with each data set refresh, the data must be cleansed from scratch. Actuaries report that this cleansing takes 60% to 90% of the total effort depending on the quality of the data. Different departments, such as claims and marketing, have done analysis with their 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. 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 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 claims division’s data or product team’s data or marketing data into a consolidated store.” People 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 synergies.

See also: Getting Back to Work: A Data-Centric View  

While an insurer does want to integrate the company’s claims data with the company’s policy data, with 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, only the product team “knows” the product data and so on. 

Carriers must change their mindset about their data. It is the enterprise’s data and 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. 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 fail to adhere to the standards.

A similar approach is required for an insurer’s data strategy. Adding a data management prganization (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 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 providing 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.

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 its underwriting process, creating real-time updates to 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.

Has ‘Data Lake’ Idea Already Dried Up?

Well, that was fast.

Remember all those massive, megabillion-dollar data lakes we all kept hearing about over the past few years? With the exception of the U.S. government, we’ll probably never see their likes again. Many of the large organizations that were pursuing those data lakes (not to mention countless smaller ones) have largely changed course. Why? The answer is actually not so surprising, even if this particular outcome is.

Many of the CIOs I talk to these days are no longer thinking of their insight systems (analytics tools, data lakes, etc.) as separate from the rest of the business, or the enterprise systems that support them. They’re managing these insights systems more as a portfolio of analytics systems — a true play for return on investment (ROI). As a result, large investments are broken into smaller, more agile investments. The technology organization may be shepherding 500 analytics projects rather than just five high-profile initiatives — enabling and supporting 10,000 people, for example, rather than just 100. In that environment, a massive data lake starts to make less sense, even if all those 500 projects tap into it. A data lake is just too resource-intensive.

See also: Why Exactly Does Big Data Matter?  

Meanwhile, the need to tap into a large volume of data isn’t going away. In lieu of a huge, proprietary data lake, what options are there? This is where CIOs are getting creative, creating a network of smaller, more manageable data lakes, for example, supplementing their data with that provided by other organizations.

Your Data Strategies: #Same or #Goals?

Goldilocks entered the house of the three bears. The first bowl she saw was full of the standard, no-frills porridge. She took a picture with her smart phone and posted it to Instagram, with the caption #same. Then she came to Papa Bear’s bowl. It was filled with organic, locally grown lettuce and kale, locally sourced quinoa, farm-fresh goat cheese and foraged mushrooms. The dressing base was olive oil, pressed and filtered from Tuscan olives. It was presented in a Williams Sonoma bowl on a farm table background. She posted a photo with the caption #goals. By the time Goldilocks went to bed, she had 147 likes. The End.

Enter the era of the exceptional, where all that seems to matter is what is new, different and better. When Twitter came out, it didn’t take me long to pick up how to use hashtags. But then hashtags took on a life of their own and spawned a new language of twisted usage. Now we have #same — usually representing what is not exciting, new or distinctive. And we have #goals — something we could aim for (think Beyoncé’s hair or Bradley Cooper’s abs).

See also: Data and Analytics in P&C Insurance  

Despite their trendy, poppy, teenage feel, #same and #goals are actually excellent portable concepts. When it comes to your IT and data strategies, are they #same or are they #goals? What do your business goals look like? Are you possibly mistaking #same for #goals? Let’s consider our alternatives.

Are our strategies aspirational enough?

If you are involved in insurance technology — whether that is in infrastructure, core insurance systems, digital, innovation or data and analytics — you are perpetually looking forward. Insurance organizations are grappling daily with their future-focused strategies. One common theme we encounter relates to goals and strategies. Many organizations think they are moving forward, but they may just be doing the work that needs to be done to remain operational. #Same. When thinking through the portfolio of projects and looking at the overall strategy, it is common to wonder, “Isn’t this just another version of what we did three months ago, even three years ago?” Is the organization looking at business, markets, products and channels and asking, “Are we ready to make a difference in this market?” No one wants the bowl of lukewarm, plain porridge — especially customers.

Are we aiming one bowl too far?

On the flip side, our goals do need to remain rooted in reality. It’s almost as common for optimistic teams to look at a really great strategy employed by Amazon, only to be reminded that the company isn’t Amazon and doesn’t need to be Amazon. It just needs to consider using Amazon-like capabilities that can enable the insurance strategy.

Data lakes can be a compelling component in modern insurance business processing architectures. But setting a goal to launch a 250-node cloud-based Hadoop cluster and declaring you’ll be entirely out of the business of running your own servers is not a strategy that’s right for everyone.

If the organization is pushed too far on risk or on reality, it creates organizational dissonance. It’s tough to recover from that. Leaders and teams may pull back and hesitate to try again. Our #goals shouldn’t become a #fail.

Finding the “just right” bowl.

Effective strategies are certainly based in reality, but do they stretch the organization to consider the future and how the strategies will help it to grow? When the balance is reached and the “just right” bowl full of aspirations is chosen, there is no better feeling. Our experience is that well-aligned organizational objectives married to positive stretch goals infuse insurers with energy.

This example of bowls, goals, balance and alignment is especially apropos to data and analytics organization. It is easy for data teams to lay new visuals on last year’s reports and spin through cycles improving processing throughput. To avoid the #same tag, these teams also need to evaluate all the emerging sources for third-party aggregated data and big data scalable technologies. With one foot in reality and one stretching toward new questions and new solutions, data analysts will remain engaged in providing ever-improving value.

See also: How to Capture Data Using Social Media  

Even if an organization could be technically advanced and organizationally perfect, it would still want to reach for something new, because change is constant. Reaching unleashes the power of your teams. Reaching challenges individuals to think at the top of their capacity and to tap into their creative sides. The excitement and motivation that improves productivity will also foster a culture of excellence and pride.

We are then left to the analysis of our individual circumstances. If you could snap a photo of your organization’s three-year plans, would you caption it #same or #goals? Inventing your own scale of aspiration, how many of your goals will stretch the organization and how many will just keep the lights on?