Centralizing data sources creates efficiencies and enables real-time analysis for better decisions. Here’s what to know.
It’s no secret that data powers modern business, but it needs to be warehoused to leverage customer insights, fuel process improvements, and enable business intelligence. You’re only getting part of the story without a unified view that combines all data sources, which is inefficient and could cost you money in the claims management process.
Data warehousing improves the claims process by providing a single source of truth – a central repository for all of the data in your organization, no matter where it is stored. Unified data provides a complete picture when combined with claims management analytics, helping to manage risk, improving customer insights, and creating operational efficiencies.
This article will look at the advantages of data warehousing, why centralizing data is imperative for claims management, how to improve data quality, and some of the related challenges.
Why you need a centralized data source
Imagine trying to construct anything with pieces scattered everywhere. You would not have what you need at hand, and likely couldn’t find some pieces at all. Or maybe you have all the pieces, but you can’t figure out what goes where because they are in separate boxes with a different labeling system.
The result? Whatever you are doing takes much longer, there’s a risk of not having the right piece in the right place, and some of the pieces may have broken in the process.
Now, imagine this is the data inside your insurance services firm.
The first step to greater operational efficiency is getting every piece of your data into the same place. When you have disparate data sources, you risk:
- Duplicate data
- Dirty data
- Inconsistencies
- Inefficiencies
- Security breaches
- Decentralized data analysis
Disparate data sources means you must collect data from every available source, and you could permanently lose information if one system fails. Having it spread out leaves your data vulnerable to multiple points of failure.
Centralizing through data warehousing provides a stable repository for the staggering amount of internal and external data your firm collects – historical and new – while enabling business intelligence tools. You’ll see improvements in:
- Data quality
- Business processes
- Business intelligence
- Decision-making
- Risk management
- Customer insights
Reporting and analytics deliver better business intelligence, which you can use to streamline claims management by cutting the time and effort involved in accessing and analyzing data. The risk of errors and inconsistencies is reduced, fraud is easier to detect, and you can implement automation that makes processes faster and more efficient.
But simply centralizing data sources via data warehousing isn’t enough. The data itself must be high-quality, or it is useless for business intelligence and insightful decision-making.
How to improve data quality to amplify data warehousing
Data quality is essential to claims management, but what makes data good or poor quality? Here are a few things to keep in mind:
- Quality data is complete, consistent, reliable, and up-to-date.
- Poor quality data creates operational problems, inaccurate analytics, and subpar business strategies and decisions.
- Quality data has no duplicates and is structured to conform to your firm’s choice of standard data formats.
- Poor data quality can create false flags and false negatives in security systems.
Data must be “cleansed” or “scrubbed” to remove or correct incorrect, corrupt, improperly formatted, duplicated, or incomplete records. Better data quality enables better data security by making it both manageable and more accurate. Quality data also streamlines compliance by making it easier to identify risk, and reporting is more efficient and accurate.
Now that you have quality data, it’s time to create your data warehouse. This isn’t about dumping all your data in one place, however. It takes proper design and architecture.
Best practices for data warehousing
There’s a best practices framework that can be customized for your particular practice to create a reporting and analytics-focused data warehouse. Yours should include the following steps:
- Use master data management (MDM) practices.
Develop a controlled process that ensures that only correct, consistent, and validated data is created. Your MDM system must confirm that quality data – no matter where it comes from – is only fed into the data warehouse. - Choose the right architecture.
Centralized data warehouse architecture has four basic parts: A source system flows into a staging area into a relational warehouse, where end users and applications can access it. This is one of the best and most successful architectures. - Create an operational data plan.
This is a mapped strategy of protocols for development, testing, and production and will help you determine your current and future needs as well as how much space your data will take up. An effective plan means considering the type of queries you will run and indexing your data warehouse accordingly. Consider disaster recovery, as well, and implement a governance, risk, and compliance policy for additional protection. - Define access.
Who on your team gets access to the data warehouse and when? This is important for both operations and cybersecurity. - Automate maintenance and management.
Automation takes a lot of the time, effort, and potential human error off of your to-do list. This helps to cut costs on infrastructure maintenance.
You might also want to consider performance and scalability. Your team needs cloud data warehousing to scale at ease, for example, as it creates scale on demand, is cost-efficient, has bundled capabilities, provides reliable uptime and availability, and offers superior cybersecurity.
Data warehousing challenges and data privacy
One of the biggest hin data warehousing is technical because you need to build a platform and tools plus implement data correction, integration, and automation. This is why your most efficient path to data warehousing and claims management analytics is an experienced software development partner.
There are also data security and privacy concerns. This means not only establishing robust security, privacy policies, and procedures within your organization, but also implementation of a comprehensive cybersecurity framework. This framework should include encryption (while data is at rest and in transit), state-of-the-art tools, and continual monitoring.
Bad actors are one side of the data security equation. On the other side are your employees, the most common threat vector. People are actually the root cause of 82% of data breaches, so you must also educate your employees on cybersecurity, how to avoid phishing, prevent vishing attacks, and the importance of strong passwords.
Data warehousing offers insurance services companies benefits such as improved business processes, increased efficiency, better data quality, increased data accessibility, robust data analysis, and cost savings. Partnering with Susco helps you create an optimized data warehouse that can exponentially improve your operations. We make your business more efficient, boost your workflow, close operational gaps, and help you communicate effectively internally and externally. Get faster, better, stronger, and boost revenue.
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