Data Quality Assurance In Enterprise Environments: Is Data the New Oil? (Part 2)
In the previous post, we described that business-critical information about a company’s operations, customers, and product portfolio is only valuable if it is accurate, comprehensive, reliable, and up-to-date. In other words, its quality must meet the informational needs of salespeople, analysts, and decision-makers. We also introduced several areas where data quality can significantly influence the profitability of enterprises. Here, we continue this discussion.
Daily Business Operations
Apart from data warehouses, CRM systems, and migrations, companies and organizations that handle large volumes of customer or product data may also face challenges with data quality and inadequately defined data management processes during normal daily business operations.
Situations may arise where customers cannot be clearly identified based on the available data during a transaction. This can compromise the smoothness and security of services. For instance, in financial institutions, cash withdrawals may require client identification, but incomplete information in the product management systems might force clerks to verify payment legitimacy using customer documentation. Such checks may lead to unnecessary delays for clients, not to mention potential conflicts if the necessary customer files are unavailable or incomplete at the branch. These issues, rooted in data deficiencies or data management issues, result in wasted work, time, and employee energy and, in extreme cases, may even lead to customer loss.
Similarly, inadequate handling of product master data can cause issues. During inventory checks or restocking, problems with financial implications may arise if the same product appears in multiple forms in the database. If one record indicates stock at the minimum and another at the maximum level, placing an order based on the first record might lead to significant problems, especially if the product is perishable.
Hidden Costs
In connection with daily operations, it is crucial for enterprises to recognize the risks and costs stemming from poor data quality.
Activities based on poor-quality information entail significant hidden costs, which a company continuously incurs without explicitly recording them in the accounts. The causes of hidden costs include:
- The time and effort required to obtain missing information retrospectively.
- The time and effort needed to correct inaccurate or incomplete information.
In contrast, high-quality information from the outset involves significantly lower costs, including the expenses of continuous quality assurance.
Particularly noteworthy is the damage caused by incorrect business decisions driven by faulty data. Simple examples include marketing letters sent to incorrect addresses or erroneous conclusions drawn from bad data to select a target audience for a new product launch.
Ensuring Data Quality
To summarize: whether it is the implementation of data warehouses or CRM systems, data migration, or the safety of daily operations, maintaining and managing data with appropriate quality must always be a priority. A quote from Philip B. Crosby succinctly encapsulates the risks of poor data quality, which should not be overlooked in today’s information society:
“Quality is free. It’s not a gift, but it’s free. What costs money are the ‘unquality’ things — all the actions that involve not doing jobs right the first time.”
In our next post, we will clarify some basic concepts related to data quality assurance Until then, please find a brief description of our data quality assurance solutions here.
Perhaps your company has already considered how to improve the quality of its data assets? Why not talk about this over a good cup of coffee?
