Nothing says you are wasting your time and energy more than referring to a process as plowing a field with a fork. I liked to use this phrase as a manager and auditor, especially in government. Good data analytics makes a big difference in problem solving.

Does anyone think a fork makes a capable farming tool?

If you've driven through farm country, you likely noticed the fields are vast. The visual of someone kneeling in the dirt with a fork is a vivid one – painful even.

I have seen public and private business processes that fit into this category. I found myself asking crazy things like, “why do we do this, isn’t there a better way?” You probably already know the answer and cringed at the thought – “we’ve always done it this way.” This is my least favorite response to the question of why.

Somehow, inefficient processes quietly march on unimpeded.  Sound familiar?

There is a solution to this problem. It’s called data.

Data - The Fuel for Business Intelligence and Data Analytics

Imagine you traded in your fork for one of those huge industrial plows that modern farmers use. This plow represents your analytics software – big, powerful, and efficient. Data is the fuel that powers your new super-plow. So, you can throw your rusty old fork away!

Unfortunately, many organizations don’t analyze and use the data available to them. A 2014 study published by Forrester found companies estimated that only 12% of their data was being analyzed. At the same time, respondents claimed data-related projects were a priority. I’ve experienced an organization not using data and data analytics well.

The Problem - Not Being Data-Driven Feeds Inefficiency

I inherited an audit team that produced about 1,400 reports each year to institutional healthcare providers. The audit findings related to payments for just one past fiscal year. It took our audit team nearly a year to prepare these reports. Clients complained about our staff requests for documentation to clear the draft audit findings. Providers argued our agency already had the data contained in the documentation we requested. When I investigated, I found these providers were right.

So, why weren’t we using this data instead?

We were plowing a field with a fork.

The original draft reports were created by an algorithm run in the claims production system that attempted to match key data. An audit finding occurred when certain data could not be matched. Staff auditors then manually searched multiple systems for information to remove “false hits”. Once done, all draft reports were mailed to providers.

The Great Paper Chase - Negative Effects of Not Using Data Analytics Wisely

Auditors then contacted providers to obtain documentation to clear draft findings. At each stage, the total dollar value of reports was reduced by 50%. Ultimately, only 25% of the original draft amount was recovered from the providers. Thus, 75% was removed manually using over 40 audit staff.

Collateral damage extended beyond the providers. Other local agencies that originated key data were bombarded with provider requests for documents. This pulled needed resources from today’s work to clean up the past. Auditors were reviewing fiscal periods that were 5 years old. So, documents had time to get lost. Also, audits needed to be brought current when providers sold their businesses. This unplanned work contributed to the our backlog. Other internal work units were also backlogged. Critical audit work could not be completed as inefficiency ate up staff resources.

Ironically, this process was created by a Kaizen effort several years prior. However, the Kaizen team failed to identify key data sources available to the agency. Thus, their solution was more bodies and a highly manual process. This is the opposite of lean and truly emphasizes the need for using the right data to drive innovation.

Believe it or not, this story got worse. Errors in programming the audit report-generation algorithm in the new claims system produced original draft reports that were ten times larger by dollar value. When I took over, the newly hired staff were leaving, and the existing staff were demoralized. Continuing the old approach was impossible as it required hiring 400 new auditors. We needed a revolutionary change.

The Solution - Maximizing Available Data

A small team was formed with auditors, data analysts, and database managers. Key missing data was traced to the systems of origin (truth). Data was validated, compiled, and a series of new data analytics were designed to better match data. Targeting was greatly improved and reduced false hits. This process was very agile. Queries were continually tweaked and tested using ideas from the line audit staff.

After laboring under manual review, the line staff knew the data very well, including whether data was high value or worthless. Staff input helped drive innovative changes and supported performing cost-benefit analysis for making decisions – transforming data to knowledge and finally to real insight.

The Results - How Data Analytics Improved Outcomes and Saved Millions

The final outcomes far exceeded our wildest expectations:

• 60% of these auditors were assigned to other critical work; only 40% remained
• Cash collections reached almost $23 million for a single fiscal year, using less than half the staff
  • 40% increase over prior year
  • 30% increase over single-year historical record
• Provider audits were stratified and multiple-year reviews completed
  • Group A had three fiscal years prepared in just 8 weeks
  • Group B had two fiscal years prepared in just 10 weeks

Work backlogs started disappearing. The right data with better data analytics was the catalyst!

Be the Solution - Open Your Mind to the Power of Data Analytics

Using available data and improved data analytics, we improved staffing alignment to better manage organizational risk. The providers and other stakeholders were happy.

More importantly, staff engagement shot through the roof as morale improved. Rather than fear data analytics was killing their jobs, the staff auditors realized data analytics was making their jobs better. Staff were more efficient, better focused, continually improving, and finding new work that needed to be done. But now, they had the right tools for the job.

Any process is a series of connections, but can you see them?  Data is often the connector.

Being the solution means taking a step back to see the bigger picture. You can treat all the symptoms you like. This won’t destroy a cancer that may be killing your organization. The data you need to solve your problem may just be waiting to be discovered. The best answers start with the right questions. So, talk to people – lots of people – together you can fill in the data gaps. You'll be glad you did.

If you are not fully using your data analytics, you may be plowing a field with a fork.