1. Creates a dependency on inefficient external reporting applications

Multiple platform architecture complicates the reporting process. While third-party reporting tools can be used to analyze data across multiple systems and produce unified reports, there are costs incurred. The State of Data Science 2020 report revealed that data professionals spend at least a quarter of their day scrubbing data. The report went on to observe that “data preparation and cleansing takes valuable time away from real data science work…”.

This inefficiency worsens in cases where reporting reveals a need to modify how data is captured or organized, forcing analysts and IT resources to trace data all the way back to its original source and then make changes.

In some cases, third-party reporting tools can also create a gulf between those who master the reporting technology and those seeking answers from the reports. In an interview, Christopher Ittner, chair of the accounting department at The Wharton School, discussed how this division affects the business process:

“What we are finding is that in a lot of companies, there are great data scientists and great business people but what is missing is business people who know enough data analytics to say, ‘Here is the problem I would like you to help me with.’ And then they can take the outcome from the data scientists and see how they can best leverage it. That is where we must get to in the next couple of years if we want to take advantage of the digital technologies.”

Providing users with direct access to reporting that requires no prep work solves both issues. End users can become their own data analysts and answer the business questions that apply to their work. Without the requirement to master the technical process of assembling, scrubbing, and joining data from multiple systems, reporting becomes more efficient, effective, and scalable.

2. Impacts customer service levels

Automation is an essential tool for improving customer service. As an example, Bring personalization to the claims process through automation points out the benefits of a claims system that “automatically sends notifications, assigns tasks, or generates reports to make sure any claim falling outside the pre-set parameters gets the extra attention it warrants. This happens instantly, every time, meaning no claim—and more importantly, no claimant—gets overlooked.” To provide this type of continuous red flag monitoring, automation must be applied consistently across entire processes.

When a combination of systems is used to handle different aspects of a process, this oversight benefit vanishes. Struggles with applying this automated red flag monitoring to a multiple-application environment and silos limit the ability to utilize automation in a strategic and comprehensive manner. A single application stack, on the other hand, improves customer service by ensuring nothing falls through the cracks at any point in the process.

3. Prevents the migration to digital underwriting

The role of the underwriter is transitioning from an administrative role to one that is analytical. For insurers, this means a key strategic objective will be tied to how organizations manage this shift. The challenge of digital underwriting comments on the transition:

“Rather than seeing the commercial underwriter as a function that will be completely automated, this view challenges the role of the underwriter itself. A Carrier Management article states: 'Accordingly, the underwriter’s role as a decision maker is also evolving, with some underwriters now being called data scientists due to their use of analytics to measure and manage risk.' In this view, the value of the underwriter depends on their ability to aggregate all of the data involving a customer, and then apply perspective and judgment when analyzing the data.”

The bedrock components of the digital underwriter are agility and unfettered access to data. The third-party tools referenced above put obstacles between underwriters and the data they need to analyze risk. Insurers database silos also slow down the entire underwriting process. Unless underwriters are armed with the speed and access to data required to excel as data scientists, this entire competitive strategy becomes difficult, if not impossible, to deploy.

4. Limits the ability to uncover insights from data

Data only has value if it triggers insights that lead to action. Ravi Mayuram, Senior Vice President of Engineering and CTO at Couchbase, writes about the effect silos have on the analysis of data in the article Collecting Data Is Easy — The Value Is In Connecting The Dots. “The challenge centers on the way most data today is stored. Most often, data resides in disparate databases, data silos and/or applications. This presents a major problem for organizations,” he states.

Siloed data is inherently difficult to analyze. Without effective analysis, actionable insight becomes impossible. An Alteryx study indicates that “Data professionals spend 60% of their time getting to insight, but just 27% of that time is spent on actual analysis.” A majority of the non-analytical time is spent either searching for data or preparing it for reports.

Mayum recommends that “these silos need to be combined in innovative ways to uncover the insights buried within.” Creating a single application stack is an effective way to eliminate silos and spend more time producing insights. “By tearing down the barriers between data silos and making data more fluid and more shareable, we unlock data’s inherent potential. These insights lead to countless benefits—from preventing crimes, to curing diseases, to driving business growth,” he concludes.

5. Reduces manager effectiveness

Managers are most effective when they provide guidance and direction to their team. Unfortunately, this is not how managers spend most of their time. A West Monroe survey of 500 managers across the country found that, “the majority of managers claim they are too bogged down with administrative tasks to provide adequate feedback and direction to their team.” Troubleshooting between multiple systems, as well as chasing data across multiple silos, adds to the administrative burden and contributes to this imbalance.

The solution lies in exception-based reporting, which is made possible through flexible reporting and trigger-based notifications. Dashboards indicate overall productivity and help users to visualize performance indicators. Any matters that exceed defined parameters—candidates for extra attention—are immediately flagged and escalated. In this way, managers spend more time resolving issues than dealing with bureaucracy. This directs the focus of managers to issues that have the greatest potential for impact and allows them to direct their team on appropriate next steps.

A siloed insurers database adds reporting inefficiencies, negatively impacts customer service levels, and prevents carriers from moving to digital underwriting. Additionally, insights remain locked in data while managers struggle to devote more time to directing teams. Single application solutions eliminate these issues and drive out the inefficiencies that affect the bottom line.

Start a conversation with us to learn more about how a single application solution can help eliminate silos and make better use of insurer data.