Building a strong and resilient procurement system has never been more important, given the current economic downturn. And starting with quality data can give companies a strong foundation for weathering uncertain times like these, now and in the future.
Today, procurement data sources can include internal, external, structured, and unstructured data obtained from automated processes. That data might provide insights that can be acted upon within hours or even in real time.
If these varying data types can be normalized and analyzed, the payoff is potentially tremendous. According to one report, for example, data analytics can help healthcare industry procurement departments realize cost savings of up to 18%, compared to traditional pricing models.
The catch is that many enterprises struggle with poor-quality data that is insufficient or stale—or both. And without the right data, success will remain elusive.
For example, you may not have all the data you need for a holistic picture, perhaps because some resides in siloed systems that are inaccessible. Or subsystems may hold relevant procurement data in different formats that are unreadable by other subsystems. Basing decisions on a partial spend picture can take you down the wrong path.
But breadth of data isn’t the only requirement. Procurement organizations charged with identifying purchasing patterns and trying to predict spending levels also require data that’s accurate, current, and reliable. As the cliché goes: “garbage in, garbage out.”
The question becomes, how can procurement organizations make sure the data they’re using to shape their decision-making doesn’t fall into the “garbage” category?
Identify and Normalize Data
Experts offer several tips. First, identify the many and varied sources of data relevant to your spend analyses. From there, the data flowing from those sources should be “normalized” or “harmonized” so it’s accessible for running analytics.
Part of the normalization process involves eliminating redundancies to make data easier to analyze. Also, logically group data that relates to each other together. If data is dependent on each other, for example, it should be in close proximity within the data set. It’s also important to resolve any seeming conflicts between data sets before moving forward with automation.
Finally, you need a way to harmonize data from existing or potential vendors in the procurement cycle, all of whom may use a different system, network, or procedure to store and transfer information. To make this data usable for analytical purposes, it must be translated into a single, clean source.
For this task, consider a universal integration and translation engine that allows systems running different languages or protocols to communicate. Such systems take in data, whether structured, semi-structured, or unstructured, and translates it into a single usable source that’s accessible to data analytics across the procurement process and throughout your supply chain.
There are also reporting tools that can be linked to your databases or enterprise resource planning (ERP) systems to extract select data, automate several types of reports, and display them through an integrated, friendly dashboard. This dashboard can do the following:
- Consolidate, organize, and weight data from multiple sources
- Provide meaningful data visualization
- Deliver drill-down capabilities into financial and operational data views
- Enable the tracking of trends across time categories
- Benchmark performance against competitors
At the End of the Day
Spend analytics is an important tool for proactively identifying savings opportunities, managing procurement risks, and optimizing your buying power. But at the end of the day, success involves having the right data to automate for analytics. That means identifying, and then unifying, relevant, current, and accurate data in a format that’s both accessible and actionable. The healthier your data, the healthier your company, during lean and prosperous economic times alike.
To learn more, visit www.GEP.com.