The reports and milestones agencies use to make decisions and measure themselves require accurate and detailed data. When managing this data, it’s crucial for agencies to reduce and prevent mistakes with workflow changes and automation. Ensuring correct data details for accurate tracking is also essential.
From automation to auditing, there are several areas where attention to data details is especially important. What follows are specific pain points to be aware of, tips for maintaining data accuracy and improving your team’s reporting processes.
Metrics for Decision Making in Automation
To start, it’s important to identify where your agency struggles with data accuracy or excessive time spent. From there, you must identify how much effort, time, and manual fixes or monitoring are needed to correct these problems. Then, identify specific, measurable ways to compare the correction processes before, during, and after implementations.
After deciding which process will have the greatest impact, start working with an internal team that is knowledgeable with automation tools, or a third party that specializes in automation. Consistent, meaningful reporting of those measurables is what you can use to identify the success and the progress that was made.
The Importance of Quality Assurance and Auditing
The adage “garbage in, garbage out” stands true within our Epic databases. Auditing and quality assurance are incredibly important to data-driven organizations. Users entering data in Epic don’t always know that their inputs affect the reports that leadership view and rely on. Being able to convey this is key to accurate results.
Addressing these issues starts with knowledgeable team members who can identify how workflows affect the end result in terms of data from Epic. After identifying problems in your data and making meaningful workflow changes, these team members can translate why the issues are happening and the steps to fix them. From there, have your data team create measurable and consistent reporting on the issue and regularly follow up in order to sustain progress.
Automation and Coding in Data Processing
Automation will be the future of efficiency in organizations, but that doesn’t mean it has to involve a large robotic process automation (RPA) that takes months to develop. Using tools like Python, R or visual basic for applications (VBA) can be an easy first step for your data and reporting teams to embrace automation.
Using coding to partially or fully automate reporting processes can save a large amount of time, allowing teams to focus on other projects. Coding languages like Python are incredible at taking large data sets and allowing you to manipulate them, producing results similar to someone analyzing a report. A process like monthly tracking and identifying how many contact emails a department has filled out, left blank or filled out with your “no email” identifier can be easy to feed to a script and have it identify all of those items instantly. Alternatively, it would take someone 10 minutes filtering in an Excel sheet to find those numbers. Steps like this to automate can make auditing and keeping up with data governance an easier task than it may seem.
One Example of Maintaining Accurate Data: Acquisition Tracking
Our agency places a specific focus on mergers and acquisitions (M&A) tracking, as we manage many acquisitions a year. Data integrity issues can easily pop up during these processes, especially as the source data is usually in a different agency management system (AMS), with completely different workflows and processes. Tracking this data as it comes into your system can make it much easier to identify data issues and address them quickly.
When possible, we try to place them in their own branch, making it much easier to report and separate out. We use the policy source field set to M&A acquired, a line level servicing role called M&A origination agency, as well as mapping the account level conversion prior account ID and date converted fields. All of these items are to be retained on renewal or rewriting of lines to facilitate tracking that M&A business over time.
We tend to use Applied conversion services when possible; when we do our own, we use the import tool and have an “IMPT” activity created upon import to each account. This is especially helpful if an M&A needs to be converted into an existing branch, as it is an easy way to identify all accounts, even if they do not have any lines entered yet. Focusing our attention on identifying and separating our M&A data has helped tremendously in keeping that data clean and identifying workflows to ensure completion.
Planning and Problem-Solving for Accurate Data
For data-driven agencies, the data is the program. Keeping track of it, identifying issues and creating better ways to solve these issues is the goal. Agencies can make this daunting task easier to tackle with automation and careful planning, as well as leveraging team members who can identify and translate how a numbers problem relates to workflow changes.