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Manufacturing IT: Why You Need a Data Quality Strategy


By: Dan Johnson

In modern manufacturing, data is everything. More important than just about any other company asset, data is the fuel that runs the literal and figurative machinery of a manufacturing operation. 

Without accurate, reliable, and consistent data, manufacturing IT suffers. Your customer relationship management (CRM), enterprise resource management (ERP) and supply chain management (SCM) systems would all grind to a halt under a cloud of suspicion that your data is not telling the truth. Productivity would slow, quality would suffer, and both vendors and customers would question the reliability of your work and your products.

See why it’s so important for manufacturing IT to have a data quality strategy? 

Developing a strategy may seem to be an overwhelming task, but it’s simply a framework for understanding where all of your data comes from; how it flows through your organization; and establishes rules for inputting, managing, and cleansing it. There are five basic elements to a comprehensive data quality strategy:

  1. Data Origin: Identifies the type and use of data and where it comes from
  2. Connectivity: Lists options for connecting data with functions and establishes accessibility
  3. Data Flow: Shows how the data moves through the ecosystem and where it is captured, manipulated, and stored
  4. Governance: Establishes the people and processes responsible for managing data. This is the most important element of a data quality strategy because it determines the rules and definitions that govern data usage and is the foundation of all information management
  5. Data Monitoring: The collective processes for regularly and consistently validating, reporting, and cleansing data

To ensure that you’re achieving the quality level required of your data, your strategy should provide for the identification, documentation, and validation of data quality expectations. As part of manufacturing IT governance, these expectations can be translated into data quality rules and metrics you can use to track data quality issues and events; provide ongoing data quality measurement, monitoring, and reporting of customer expectation conformance; and assess the business impact of poor quality data.

A successful data quality management strategy has proactive and reactive aspects to it. 

The proactive components minimize the potential for new data problems to arise while the reactive components address any data problems that already exist in current databases. The proactive part of your data quality strategy establishes the overall governance, defines roles and responsibilities, establishes quality expectations and the supporting business practices, and deploys the technical infrastructure that supports those practices.

The reactive component deals with any issues that might come along with old data from legacy systems and databases. Very often some amount of legacy data is kept simply because no one wants to get rid of it, afraid that it might still have some value, even if workers have to struggle with it. In reality, the quality of data in legacy systems that were developed without a data quality management strategy in place may be inadequate for meeting new business needs, causing the old data to clog up your system. For example, the accuracy of customer information may be good enough to invoice a customer, but not good enough to understand the profitability of that customer; parts information may be good enough for each manufacturing facility’s needs, but not good enough for understanding inventory levels and moving to a virtual parts warehouse approach.

Once you have a data quality strategy in place, you’re ready to put it to work performing the three critical tasks it’s designed for:

Profiling: Profiling data helps you assess whether data meets the baseline quality standards you’ve established. Properly profiling your data saves execution time because you identify issues that require immediate attention and avoid unnecessarily processing unacceptable source data sets.

Cleansing: After a data set successfully satisfies profiling standards, it still requires scrubbing to ensure that all business and schema rules are being properly met. Successful data cleansing requires the use of flexible, efficient, and intelligent techniques to handle complex quality issues that could be hidden deep in large data sets. 

Auditing: Auditing is perhaps the most important aspect of data quality. Auditing provides a history of all of the data cleansing operations performed. With auditing, you can track and score overall data quality and evaluate how well data processing has met desired business, technical, and regulatory standards.

If the concept of creating a manufacturing IT data quality strategy is new to your organization, you might consider starting with a simple strategy to lay the foundation and then build it up over time, creating, say, a more comprehensive sets of rules for governance as you go along and learn the strengths and weaknesses of your data environment. 

Having a solid data quality strategy is crucial for today’s manufacturer. Without a strategy in place, your company is exposed to the unnecessary risks that come with unregulated data use. The benefits of reliable, consistent, high quality data make the case for establishing a data quality strategy abundantly clear – increased productivity, efficiency, and profitability. Who can argue with that?