Data scrubbing like you mean it

Embarking on the journey of transitioning to cloud-based solutions is a game-changer for hospitals, that provides improved efficiency and team management. Drawing from my experience working in the Hospital and now on the tech side, I shed light on specific problem areas that demand attention, emphasizing the pivotal role of clean items and supplier data in addressing these challenges.

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Now when I say data scrub, I'm talking conceptually as a project not defragment tools. I always come to a personal experience that we need to consider. Not the AI-drawn picture of me above thats polysemous. The method is more important than the desired result. In every case. But the method isn’t cookie-cutter and dependent on the org structure.? I saw two instances of scrubbing that went horrific.? The first moved the decimal creating thousands of match exceptions and team overtime. The second was a v-lookup that started a series of meetings to expand the ICU service line of a hospital. Let us say this one got pretty far down the line before someone noticed. If we have met before, I likely have told them to you.

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Let's talk about my 12 key areas of the scrub first.

(Hint: Project management ownership is key)

• ?Accuracy: Ensuring the accuracy of data is paramount. Data scrubbing involves identifying and correcting errors, inaccuracies, and inconsistencies in the dataset to provide reliable and trustworthy information.?
• Completeness: Data scrubbing addresses missing or incomplete information within a dataset. It involves filling in missing values or obtaining the necessary data to ensure that the dataset is comprehensive and suitable for analysis.
• ?Consistency: Data consistency involves standardizing data formats, units, and structures to eliminate discrepancies. This ensures that data is uniform across the dataset, making it easier to analyze and interpret.
• Uniformity: Achieving uniformity in data involves standardizing data values and eliminating duplicates. This prevents redundancy and ensures that each data point is represented consistently throughout the dataset.
• Relevance: Scrubbing also involves assessing the relevance of data to the overall dataset. Removing irrelevant or outdated information helps maintain the dataset's focus, making it more meaningful and effective for analysis.
• Timeliness: Data scrubbing may also involve updating timestamps or date-related information to reflect the most recent and relevant data. Timely data is crucial for accurate analysis and decision-making.
• Data Integrity: Maintaining data integrity is a core objective of data scrubbing. It ensures that data is accurate, consistent, and reliable, supporting the trustworthiness of the dataset as a whole.
• Data Standardization: Standardizing data involves using consistent formats, units, and conventions across the dataset. This simplifies data management and facilitates seamless integration with other datasets or systems.
• Error Detection and Correction: Identifying errors in the dataset is a critical part of data scrubbing. This involves using various techniques such as pattern recognition, outlier detection, and validation rules to pinpoint and correct inaccuracies. I do love a spreadsheet lit up like a Christmas tree.
• Deduplication: Removing duplicate records or entries is crucial for maintaining a clean dataset. Deduplication helps prevent errors caused by redundancy and ensures that each data point is unique.
• Scalability: Data scrubbing processes should be scalable to handle large datasets efficiently. This scalability ensures that the cleaning process remains effective even when dealing with extensive and complex data. Macros dependent on computer memory are not the best option always.
• Audit Trail: Keeping a record of changes made during the data scrubbing process is essential. An audit trail helps in tracking modifications, understanding the cleaning process, and ensuring accountability. This is the most important when the enviable happens!

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Now let's talk about the effects of the scrub in Supply Chain management.

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• Inaccurate Inventory Management: Cloud-based solutions offer real-time visibility into inventory, but their effectiveness hinges on the accuracy of the underlying data. Inaccurate item information can lead to stockouts, and overstock situations, and ultimately compromise the hospital's ability to meet patient needs
• Compromised Procurement Negotiations: Clean item and supplier data are paramount for effective procurement negotiations. Without accurate information, hospitals risk entering contracts with suboptimal terms, missing out on potential discounts, and jeopardizing cost-effectiveness in the procurement process.
• Regulatory Compliance Risks: In the healthcare sector, compliance is non-negotiable. Clean data is essential for meeting regulatory standards, ensuring patient safety, and maintaining trust. Cloud transitions magnify the importance of data integrity, as non-compliance can lead to serious legal and operational repercussions.
• Data Migration Challenges: Transitioning to the cloud requires meticulous data migration planning. Incomplete or inaccurate item and supplier data during this phase can result in operational disruptions, delayed timelines, and increased migration costs. Investing resources in cleaning and validating data pre-migration is crucial.
• Limited Decision-Making Capabilities: The power of cloud-based analytics lies in its ability to facilitate informed decision-making. However, with dirty data, hospitals face the risk of basing critical decisions on flawed information, impacting everything from resource allocation to strategic planning.
• Lack of Transparency in Supply Chain: Clean data enhances transparency in the supply chain, allowing hospitals to trace the flow of items from suppliers to end-users. Without this transparency, hospitals may struggle to identify inefficiencies, monitor supplier performance, and proactively address potential disruptions.

The transition to cloud-based solutions holds immense potential for hospitals, but the journey is riddled with specific challenges. Clean item and supplier data emerge as the linchpin for overcoming these hurdles, addressing issues in inventory management, procurement negotiations, regulatory compliance, data migration, decision-making capabilities, and supply chain transparency. By tackling these problem areas head-on, hospitals can ensure a smooth and successful transition to the cloud, ultimately enhancing their capacity to deliver optimal patient care in the digital age of healthcare.

The views expressed on this LinkedIn post are my own and do not necessarily reflect the views of Oracle.

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