According to a recent survey of 100 healthcare executives, only

of organizations fully trust their data. 1

1 Sage Growth Partners.( 2021, October 14). The high cost of bad data and analytics on strategic healthcare decisions. Sage Growth.

hink about that old laptop you have stashed away in a closet. Or the phone before your last phone, with all of the photos that were saved on it and were difficult to transfer. We all have data of some sort that we may want to hang onto and hope to bring over to a new device or system.

That small household issue is an exponential problem for health systems. Healthcare today is swimming in a variety of data: supply chain data, clinical data, operational data and more. Some of those data streams are on legacy systems that don’ t quite align with each other. And the volume, variety and velocity of information often outpace an organization’ s ability to harness it productively.

This phenomenon— known as data overload— can stall progress, obscure insights and leave decision-makers uncertain about their next steps.

Every stream of data flowing into a healthcare system promises insights, but collectively they create a flood. Healthcare systems generate massive amounts of data— from inventory levels and patient records to supplier deliveries and ERP system outputs. However, instead of empowering teams to make smarter decisions, this flood of data often leaves them overwhelmed

, unable to discern critical insights from trivial details.

Brian Wells, Medline senior vice president of operations, explains the issue simply:“ Organizations have data everywhere— files on computers, old systems, cloud storage— but they often don’ t know how to access it effectively.” Without proper management, valuable insights remain hidden, complicating decisions and potentially jeopardizing patient care and operational efficiency.

Root causes of data disarray Health systems typically rely on an array of data sources: enterprise resource planning( ERP) systems, item masters, vendor portals, mobile scanners, and legacy software. Each source may contain important information— but these systems often don’ t communicate well. This leads to duplication, discrepancies, and time-consuming reconciliation efforts.

In healthcare supply chains, data overload isn ' t just inconvenient; it can lead to critical issues like stockouts, financial waste, and compromised care quality. Marshall Lancaster, Medline chief information officer, highlights the common trap:“ You become paralyzed looking at the sheer volume of data— it’ s like staring at a football field filled with boxes. The key is to realize you only need a very small part of what’ s there.”

Wrangling data: Take practical steps Effective data management begins by clearly identifying organizational goals and linking them directly to data requirements. Instead of analyzing every available metric, supply chain managers should focus on specific operational pain points.

Wells outlines a straightforward method:“ Identify your key operational challenges first. Whether it ' s preventing backorders or improving order accuracy, ask yourself,‘ What data do I need to tackle this problem?’ That instantly narrows your scope.”

In other words, start with the decision, not the data. Begin with the problem you’ re solving and figure out which data gives you the information you need.

Cleansing and standardizing data The fundamental data challenge starts with infrastructure. Because healthcare organizations often operate multiple disconnected systems that don’ t communicate effectively, it can create a scenario where valuable information

exists but remains practically unusable. Or as Wells put it:“ They may have data, but they don ' t have access to it.”

On top of that, raw data from various systems often arrives messy— duplicate entries, inconsistent formats, missing fields— which complicates analysis. Data cleansing is an essential step that transforms raw data into actionable insights.

“ Cleaning your data involves verifying accuracy, ensuring consistency, and formatting information correctly,” says Wells.“ It ' s like spring cleaning for your digital house; everything must be in its proper place before you can truly use it.”

Wells advises starting small. Pick one area and look for ways to standardize at the field, file or format level.

“ With any individual data element, go in and make sure that you have the right information in the right formats,” he said, " so you can load that piece into your system.”

Prioritizing actionable insights Organizations frequently make the mistake of attempting comprehensive data analysis from the outset. Lancaster emphasizes iterative improvement instead:“ Solve small, high-impact problems first. Once you start narrowing down your data, you ' ll quickly find that many issues disappear or become manageable.”

Lancaster advocates for an 80 / 20 rule in data analysis. In most healthcare supply chains, a small percentage of products, suppliers, or processes drive the majority of problems or opportunities. Focus data analysis efforts on:

• The products that cause the most operational issues

• Suppliers that represent the highest spend or greatest risk

• Processes with the most significant impact on patient care

• Cost centers with the greatest potential for improvement

Harnessing technology Emerging technology can offer practical solutions for dealing with data overload, whether it’ s simpler dashboard tools or more advanced health data management platforms( HDMPs). By aggregating data from ERPs, procurement systems, and clinical records, HDMPs allow healthcare organizations to detect supply chain disruptions proactively.

Previous page: gremlin / E + via Getty Images; peshkov / iStock / Getty Images Plus via Getty Images; Just _ Super / iStock / Getty Images Plus via Getty Images

14 SUPPLY CHAIN OPTIMIZATION Issue 11 / 2025