Is your data supply chain a liability?

Organizations today have more data at their disposal than ever before, and data architects, analysts, and data scientists are pervasive across all business functions. Yet as companies compete for skilled analyst roles to use data to make better decisions, they often fail to improve the data supply chain and the resulting data quality. Without sound data supply chain management practices, data quality often suffers.

Poor data quality is cited as a top reason for initiatives not delivering their expected value – Up to 60% of business initiatives fail because of data quality issues. Data quality becomes an even more pressing issue as organizations move toward AI/ML-enabled decision making. If the data used for AI/ML models is inaccurate, incomplete, or out of date, the models will not deliver the desired results.

Data is the most important raw material for analysis and decision-making. Every successful business leader asks, “How do we improve data quality so that we make the best possible decisions?” The answer is to improve the results of an organization’s data supply chain to ensure it doesn’t rely on analytical skills.

How can we improve our data supply chain outcomes?

• Understand the impact of first mile/last mile dates
• Reduce supply chain complexity/costs
• Improve data quality monitoring and reporting

Supply chains consist of three main elements:

First mile/last mile impact

That first mile / last mile challenge requires consideration of the entire supply chain, starting with the acquisition of the data (upstream). The urgency of having data available for analysis and decision-making drives companies to invest more effort in the “last mile” – bringing the data downstream to the customer. In the case of the data supply chain, of course, the customer is an internal department or team that needs the data for analysis, reporting, and so on. The challenge is to capture the data source correctly from the start and ensure that the data quality does not degrade during the move across the entire data supply chain.

A key supply chain management metric used to assess the performance of physical supply chains is OTIF – On-Time-In-Full. While this is a strange acronym, improving value has dramatic results as it relates directly to the end customer and their ability to get their job done. For example, if you need 10 attributes to generate a customer satisfaction rating, but only 9 are available, the calculation cannot be performed. Using a metric that focuses on the impact of data quality and availability on downstream processes can help increase organizational awareness.

• Recommended Action Plan: Create a map of your data supply chain. The concept of supply chain visibility and sourcing applies to data supply chains as much as it does to physical supply chain management. Understanding the data sources, any transformation activities taking place, as well as the “customer lead time” helps organizations identify and mitigate risk. Implementing metrics to assess how well the organization is meeting customer needs helps sharpen the focus on improvement.

Complexity of the supply chain

Supply chain complexity is the term used to describe the network of capabilities required to meet downstream requirements. The greater the number of suppliers, business functions and distributors required, the greater the complexity.

Each additional element in the supply chain increases complexity, and more complexity contributes to increased variability. Variability is a major quality challenge. In physical supply chains, companies try to reduce the upstream complexity. There are a variety of sources of internal and external data in the data supply chain (from data brokers, social media/sentiment analysis, etc.) and just like a physical supply chain, reducing complexity in the data supply chain helps improve overall quality.

How can reducing complexity improve quality? Fewer systems mean fewer data transformations, increasing data availability and accuracy.

• Recommended Action Plan: Inventory the data available for downstream use and map it to the source system (internal vs. external). Common attributes are often created in more than one system, adding complexity. For each data element, identify/select a single system for downstream consumption and set up a System of Record (SOR) with the goal of getting data from as few systems as possible.

Data Monitoring and Reporting

Data quality should be a key performance indicator (KPI) for almost every company today. The quality of the output depends on the quality of the input. Think of every great meal you’ve had and what made it great; Of course, the company and ambience of the setting matter, but the quality of the ingredients has a direct impact on the outcome – freshly caught seafood is always better than fresh-frozen.

The methods and frequency of data quality assessment often vary within an organization. Different functions in an organization may use different methods of assessing quality; For example, accounting can be stricter than marketing. But why should different functions be valued differently? Good decision making depends on quality data, and shouldn’t every function make the best possible decision?

• Recommended Action Plan: Establish a common formula to measure data quality and use the measurement consistently across features (data quality score). The volume of data to evaluate requires sampling and estimation, and the approach should be consistent. One approach can be to examine 100 records, review each and identify errors, and then count the error-free records to determine the percentage of data created correctly.

The data supply chain is an emerging and evolving concept for many organizations. Finding and retaining talent to improve data supply chain outcomes is critical to an organization’s competitive advantage. Certainly there are differences between tangible and intangible products, but many of the concepts and tools from the physical world can be applied to data and the result will be as powerful as improving physical supply chains.

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