July 31, 2019      5 min read

One of a business’s most valuable assets is the inventory stock used to generate profits and revenue, however in today’s business environment quality data is now becoming as equally valuable.

Quality data is the foundation of operations, inventory control and in driving vital financial decisions. Maintaining accurate, complete and consistent data is crucial to business success and sustainability.

What is quality data?

Quality data tells the truth. It should be accurate, complete, consistent, timely and unique. The consequences of bad data can be significant, ranging from bad planning and incorrect payments to inefficient business processes, delays and poor decisions.

Data quality means different things across different functional areas of an organisation and data quality is all about being fit for its desired purpose. For some, this can mean having complete profiles for prospective customers to help with marketing segmentation activities. For others, it is ensuring that inventory stock data is accurate to ensure products are available or that customer contact information is correct so that product deliveries are received in a timely manner.

Accuracy is paramount and is the measurable degree to which data represents reality. Limit free-form text for names, addresses, short descriptions and notes. Coded fields help to improve accuracy, facilitate reporting and ensures consistency across the board.

Consistency requires that data is consistent across several data stores. The values contained in the various fields of a database record should be entered and spelt correctly and users must understand the complete scope of the data and be clear as to what comprises each data element. It is important to ensure that data fields, formulas and calculations are tested for accuracy and consistency across all points of entry.

Data must also be current and timely with respect to the business needs, representing reality from the necessary point in time that the data is to be used. For example, inventory control data should represent current, real inventory stock counts.

The validity of the data means that it should match set rules and be unique to ensure it is not duplicated. Data can be validated automatically as it is entered by automatically flagging inconsistent, unexpected or missing information.

Reports are the final stage of checking and verifying data integrity and should be run frequently. Regularly run sets of core baseline reports to review your data and identify any unexpected results so that you can then return to the source and make any needed corrections.

Improving data quality

Improving the quality of your data will no doubt help better inventory control. The key to improving data quality is to know your data. Know what data you are collecting, where you are collecting it from and most importantly, why you are collecting it. Be as forward-thinking as possible and focus on the right data necessary to achieve your business goals. Ensure that every piece of data is coming from a trusted and knowledgeable source.

The first step in improving data quality is to undertake data profiling to examine your data defects. Data profiling will analyse the accuracy and uniqueness of data, checking whether the data is reusable by gathering appropriate statistics. Likewise, data mining tools can also be used to assess data quality.

Centralise the management of business-relevant data and establish corporate standards to reduce inconsistency in data interpretations. Correct any data issues at the source and be vigilant when bringing in any new data sources.

The onboarding of data from new sources requires attention to detail that guards against improper data conversion or data loss. For instance, if inventory stock data is integrated with POS data systems, it is important that the information is accurate to avoid any stockouts or unnecessary product procurement caused because the systems report different counts of inventory stock.

Data quality is only as good as the information entered into your system. When you discover problems with incoming data like incorrect financials, inventory stock quantities, the wrong codes or missing data values, it’s important to track back to the original source to make corrections.

This can be achieved by establishing specific contacts for each data provider who is then able to resolve issues as they occur and manage data quality at the source.

Quality data and inventory control

Each role in any organisation should involve clear tasks that are geared towards company-specific goals and quality data management. The data strategies for inventory control should define rules, plans, requirements and processes for quality data delivery.

For example, quality data from numerous sources is necessary to accurately calculate inventory costs.

Implement technology

Considering the sheer volume of data that organisations are gathering today, it’s not surprising that data quality technology is an essential piece of most data improvement plans. Manual efforts no longer meet the standard of sophistication required.

The best way to achieve high data quality with technology is to integrate the different phases of the data quality cycle into operational processes and match these with independent roles. Consolidate data into a single integrated system to achieve greater control over the integrity of data flow.

Much of the technology available to businesses today is aligned with the data quality cycle to provide in-depth functionality that supports multiple users in their individual processes.

Software tools assist in different ways by providing data profiling functions that cleanse, standardise, match and manage hierarchy. It also provides user-specific interfaces to support workflow and integrates with other applications to cleanse and enrich.

By investing in data technology, you can optimise and enrich data quality to improve outcomes, reduce costs and make better-informed business decisions. Before implementing any data quality improvements however, you should first determine what you want from your data and how to evaluate the quality of it.

When you do implement new software or processes, ensure there is good change management in place.

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