By Patrick O’Neill, Founder and President, Redhand Advisors
If you feel like you’re living in the Wild West of lawless data, trying to wrangle disparate information into actionable takeaways for your organization, you’re not alone.
Of the 1,000 organizations surveyed in our 2023 RMIS Report, less than half of respondents (46%) were confident in their data analytics for risk management.
The problem: An absence of data standards
With all the data being exchanged within an organization’s ecosystem, there should be some form of standardization. Unfortunately, that’s often not the case, which leads to significant challenges for those evaluating risk. There are several key reasons for this.
Inconsistent data collection and reporting put a huge strain on those who have to map data in multiple different formats to corresponding fields in their own systems. For example, if you have multiple third-party administrators, each will likely use different data fields and codes.
Lack of data integration impeding inter-organizational collaboration across multiple organizations only compounds the data analytics challenge. Take a worker’s compensation claim, for example. There may be a medical bill review, nurse case management and a physical therapist who all have to share data. Mismatched information from numerous organizations makes collaboration difficult, at best, taking an incredible amount of manual effort to reconcile data streams and ultimately, impeding the ability to have a truly collaborative process.
So what?
The challenges to data integrity aren’t new, and yet they handicap a risk manager’s ability to execute tasks reliably. Unfortunately, there are multiple effects of limited or poor data, including:
- Limited market competition and innovation. A mess of data makes it harder to benchmark data and identify areas for creativity and improvement.
- Poor decision making. Decision makers become hesitant, uncertain, and prone to making suboptimal choices when they lack data confidence.
- Increased operational costs. When you have to scrub data regularly to make sure it’s properly coded or manually process it, errors are common.
The solution: Leveraging tech internally to solve a global issue
There are ways to address the issue of data standardization through technology. Here’s how.
- Employ data standardization tools to help your organization streamline data collection and storage. Risk management information system (RMIS) vendors are starting to match imported data from various sources into existing fields based on data mapping with user friendly tools.
- Use Application Program Interfaces (APIs) to enable communication between multiple systems. APIs can be pre-programmed to import data into a system, moving data to the right fields automatically. Furthermore, automations can be set up to do so in real time. There’s upfront work to enable such efficiency, but they can be leveraged to integrate data more seamlessly.
- Leverage artificial intelligence (AI) to refine data sets to analyze data sets and improve them through machine learning. Once the right data is in the system, AI can be trained to identify system breakdowns, model claim outcomes, and suggest improvements.
In the not-so-distant future interoperability platforms will function as a hub for data exchanges, standardizing data as it’s submitted and pushing it back out to any stakeholders involved. It may be that this function becomes a part of your RMIS, or perhaps it will be a stand-alone tool. One thing is for certain: when this technology is developed, data standardization will be solved on an industry-wide scale — even without data standards in place.
For more information on how technology can help your organization harness data, schedule a call with Redhand Advisors.