Business Intelligence Insights: The Six Steps an Agency Needs to Get Started

Insurance is a data-centric industry. Whether it’s detailed customer information, employee performance success rates or industry trends, agencies are always collecting data.

The idea of harnessing this data for better decision-making isn’t a new concept for insurance agencies. However, the insurance landscape is no longer static. It’s dynamic and agile. Reliance on outdated, manual reporting won’t cut it in this new environment. If agencies want to remain competitive, it’s time they invest in business intelligence to maximize the value of their data.

Making the initial investment is just the start of an agency’s data journey. Successfully integrating business intelligence solutions into an agency’s operations requires a thoughtful approach and a clear, concise plan of action. Following the six steps below will give agencies the groundwork they need to utilize their newly found business intelligence insights successfully.

Step One: Define Goals and Objectives

Starting with a clear objective is the first step in any business intelligence project. Recognizing the business problem that needs solving and setting well-defined goals makes it easier to determine which data is needed.

Data can solve many problems for a business, so it can be overwhelming to figure out where to start. Consider the recent report from business intelligence company MicroStrategy, which found that with the use of business insights, 64% of respondents cited improved efficiency, 51% reported better financial performance, 44% noted improved customer experiences and 43% gained more of a competitive advantage. That’s a wide range of benefits. A business could consider choosing one of these areas and using its data to drill down deeper into how to improve.

Step Two: Collect and Organize

Once goals and objectives are defined, it’s time to gather the data needed to create business insights. Data comes from many sources, so it must be gathered and organized into a central data repository.

This requires the agency to implement technology such as data warehouses, data lakes or other similar cloud-based solutions. An agency management system can also act as a data repository for customer and policy data for those who want to simplify their tech stack.

Each agency will have its own preference when it comes to technology. Some may choose to build their own stack, while others rely on solutions from software partners. Whatever technology the agency chooses, it should serve as a single place where internal and external data can be organized, integrated, engineered and modeled.

Step Three: Cleanse and Standardize

Now that the data is combined in a central repository, it needs to be cleansed, filtered and standardized. According to Accenture, the world produces five exabytes (the equivalent of five billion gigabytes) of data daily. By 2025, this is set to rise to a rate of 463 exabytes per day, demonstrating just how much data is flowing behind the scenes of every business. But that doesn’t mean all of that data is useful, and agencies must remove what’s not needed from their central repository and replace what’s missing.

Agencies relying on business intelligence tech outside of their agency management system may have data in various formats as it’s coming in from multiple sources. It’s important to standardize the format across all collected data to make it easier to analyze. If the data is unfiltered and irrelevant, the results of the analysis will not mean anything.

Each agency will have its own preference when it comes to technology.

Step Four: Analyze and Interpret

After the data is collected and cleansed, it is ready to be analyzed. Analyzing data is the process of using statistical techniques to examine the data and extract useful business intelligence insights.

To break down data and understand the opportunities it presents, consider the following framework: Know. Recommend. Assist. The “Know” stage is exactly what it sounds like. It uses data to understand what has happened and what is currently happening in an agency. This type of insight makes data actionable. Once the data is actionable, it can move to the “Recommend” stage. This stage leverages actionable data to better equip agencies to meaningfully impact their business goals. Think of it as the data making recommendations on how to improve the agency. Finally, “Assist,” is where data and AI-driven automation intersect to solve the challenges and open the growth opportunities identified in the first two stages. By having relevant insights when and where they are needed, agencies can optimize their time and focus on the highest value work.

Step Five: Communicate

Now that an agency’s data has been distilled into business intelligence insights, it’s time to communicate those insights to key stakeholders. Sharing this information is just as important as analyzing the data and it is crucial that everyone understands what is being presented. In their raw form, those insights may be hard to understand. Studies show that 65% of the population are visual learners, and 40% of all learners respond better to visual materials such as pictures, graphs, charts and illustrations than text alone. Using platforms such as interactive dashboards to create visuals can help tell the story of an agency’s data in a way everyone can understand.

Step Six: Learn and Improve

The business intelligence process shouldn’t end once the initial goals are met. After all, according to McKinsey, data-driven organizations are 23 times more likely to acquire new customers, six times as likely to retain existing customers, and 19 times more likely to be profitable.

With ongoing improvements, the business intelligence process can grow alongside the agency, ensuring it provides a continuous, positive impact on the business. Developing a methodology to measure effectiveness and accountability will allow the agency to determine if its current process is meeting the desired outcomes and make any necessary adjustments to meet its new goals and objectives.

Building a Data-Driven Culture

Data analytics is not just about the data. It’s also about the people who use the data to make decisions. To be truly data-driven, agencies must build a culture where everyone uses data to make decisions. This includes training employees on how to use data analytics and giving them access to the tools they need.

Having a framework in place will ensure an agency’s success when implementing business intelligence solutions. It will create an environment in which all stakeholders have access to insights to make better decisions in their roles. Embracing new ways to maximize business intelligence insights will keep agencies competitive and enable growth in today’s increasingly digital world.

Gupta is chief product officer at Applied Systems Inc. He is responsible for the company’s product vision and product management teams.