The Executive Veterinary Program (EVP) at the University of Illinois, College of Veterinary Medicine was developed in 1991 to enhance the business, communication and planning skills of swine veterinarians. Since that time, 225 of the top swine industry veterinarians from throughout the world have benefited from the program. I participated in the program as part of the 2015 class. Our fifth module, “Measuring Implementation: Leading with Data,” was facilitated by Brad Kolar of Avail Advisors. The module centered around data, but with a more holistic approach. This module has stuck with me as we approach health, production and financial information with our clients.
The value of data varies greatly due to variation in what we are collecting and also in how we utilize it. Basically, you increase the value of data through the four phases of data interpretation. You provide leadership and insight to your organizations through these phases – collect, summarize, analyze and syntheses.
Data Collection
Much of our time is spent in data collection and this can generate some nominal value for the production system. This has the lowest value but must be completed to allow further interpretation. In pig production, examples of this include recording the number of breeds, farrows, total born, mortality and culls. Much of this data collection is hand recorded and often times passed through multiple people before being recorded electronically which allows opportunities for data entry errors. Further complicating data collection is the fact that various types of data has to be entered in disparate systems – spreadsheets, barn charts, sow records, finishing records, diagnostics, feed mill and accounting are all different databases, each with different types of data entry needs. Simply put, our industry has a data management process fraught with opportunities for errors in the first step of the process, long before we’ve been able to extract significant value from our data.
This challenge has led companies to begin hiring data scientists and database programmers. Carthage Veterinary Service has hired four full-time employees over the past two years to structure data currently entered across numerous data systems so it can be pulled into a common database. This “data lake” allows storage of all the data in its raw form for additional analysis. Another new term for me is the “common data model” which allows us to bring data together from multiple systems and applications. The work of these individuals replaces the manual work involved for all our producers in opening multiple databases to look at data from different sources. Data lakes and common data models allow us to more easily summarize and analyze data, which in turn allows us to take more rapid action from our data resulting in decreased performance losses and increased producer profitability.
Data Summarization
Summaries of data collected allows you to start to make sense of the data. This is a common level that we get data to, explaining what is happening in the system. Many of our software packages will do this – sow performance, finishing performance, diagnostic database. This generally tells you what you expected to see – mortality is high, farrow rate is good. It states the facts of the system, but not generally meaning and insight. As you think about reports that are sent around in organizations, most of them stop at this point.
Data Analysis
Moving data to analytics tells you the “why” of what is happening in your system. Analytics is the work of identifying the correlations, patterns, relationships and cause and effect in the system. In all production systems, there are daily, weekly, and monthly reports (data summarization) that are emailed around to our teams. Think about the hours of time wasted expecting everyone on that email to look at the same data summary line by line and come up with their own analysis of what is going on! What if the person responsible for the data summary took it to the next step and analyzed it prior to sending it to the team? They could direct time and attention to the areas that need focus, versus everyone coming to their own conclusions about where to spend time and energy. These reports like mortality, sow farm production, marketing or feed usage could be streamlined to focus effort. Business Intelligence (BI) tools like Power BI or Tableau are now offering us the opportunity to write the data summary and data analytics reports that are most important to your business. These reports can automate the analysis of your system data to quickly focus time and effort.
Taking Action
The greatest value that you can drive in data is using it to take action. Context is important in interpreting the data - does it matter? How does this fit in to the organization as a whole? This is the application to your company’s strategic direction and the context behind them. It takes experience, knowledge of the system and industry perspective to provide data synthesis and in turn determine appropriate actions to take. This report does not come in the form of a table or chart, but a written document that gives company direction and initiatives. Data synthesis tells the story of your company and highlights where you should act and what level of action/investment is appropriate.
There is a quote that I believe describes our situation with swine production data, “We’re swimming in data and starving for information.”
With handwritten records, multiple data systems to review and everyone expected to do their own data analysis of massive reports, it’s no wonder we often abandon our own data and go with “gut feel” or “tribal knowledge” when making critical decisions. Our industry isn’t alone or unique in this challenge. BI tools give us the ability to overcome many of our historical challenges in extracting value from our data. Failure to incorporate these new tools into your existing data systems leaves you at risk of making worse decisions than your competitors – not because your competitors know more about pig production than you do but simply because your competitors may be able to take smarter action through more timely and accurate data management.
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