3 Tips: Spend More Time Analyzing, Not Organizing, Data

Companies spend 80% of their time preparing data for analysis. Let’s explore how Sagence helped a large pharmacy healthcare provider significantly reduce data preparation time, enabling the company to spend more time on data analysis and ultimately realize returns on its data investments.

One of our clients, a large pharmacy healthcare provider with integrated offerings across the entire spectrum of pharmacy care, has an inventory team responsible for reporting weekly drug returns and, more importantly, analyzing the drivers behind these returns. This team is responsible for answering questions like “Did a generic become available causing lower sales of the brand name drug, and as a result, expired inventory is increasing return rates? Or is improper inventory management the cause of returns?” It’s clear to see that the actions taken would be very different for each scenario.

Although the inventory team is responsible for identifying drivers and actionable insights on the drug returns data, they spend an unnecessary amount of time organizing and preparing data for analysis—at least two full work days during the course of the week. The inventory team is dependent on another team to gather the weekly drug returns data from the data warehouse. The inventory team then copies the weekly data into a local database, so they can keep a historical record. This creates inefficiency, redundant processes, and multiple sources of information. Millions of dollars and thousands of hours were spent creating the data warehouse, but when employees are not empowered to use the data in it, achieving ROI on analytics investments becomes even more difficult.

Further slowing the process, the weekly drug returns data provided by the outside team also lacks master data on various drug attributes, including the drug type and drug name. The inventory team has to manually extract the drug attributes from another data source, import it into Excel, and perform lookups to match the drug attributes against the drug returns data. In addition to Excel being used to organize and prepare data, it is being used to perform resource intensive analyses and calculations. These calculations require formulas that read more than 10 million records, causing the inventory team to frequently wait an excessive amount of time for the calculations. And as we’ve all experienced, using a high number of formulas and complex spreadsheets can lead to serious miscalculations.

Sagence was able to identify the key data sources from the data warehouse for its’ returns analyses, developed automated processes for key calculations and created a view of the data that includes both drug returns data and drug attributes. The time saved for the inventory team over the course of a year, at least 800 hours, is now being spent analyzing data, rather than preparing and organizing it. Additionally, Sagence connected the data view to a data visualization tool which gave the pharmacy company an improved and more flexible process for analyzing trends and actionable insights on its drug returns data.

If these inefficient data processes are occurring on this one team, odds are high that many teams within the organization face these same challenges. If 800 hours can be saved on one team over the course of a year, thousands of hours could be saved across the enterprise. These small wins really do add up, which may lead to savings of hundreds of thousands of dollars due to increased efficiency and spending more time on value-add activities.

For an organization to generate even greater ROI and scale these capabilities, management needs to pay greater attention to:

  1. Enterprise data management
  2. Tool deployment
  3. Organizational alignment

Contributed by Phil Liu.