In today’s highly competitive business environment, sometimes the smallest details can make a big impression. If a business has low-quality data, it will make a poor impression on prospective clients and suppliers. Companies also need clean and correct data to perform their important internal tasks.
Achieving quality data is the first necessary step toward master data management. If the data in the system is old, inaccurate, or contaminated, there are going to be major problems with the integrity of the entire database.
Unfortunately, data quality management is often missing from companies’ business strategies. Profisee explains why this is a mistake and offers some solutions for companies that want to improve their data.
Data Underlies Everything
At its most basic level, every business collects data. From suppliers and vendors to customers and products, each individual piece of information has meaning to the whole.
It is helpful to think of business knowledge as a pyramid. The steps of business knowledge build on each other, starting with data at the bottom, leading to information, knowledge, and actions.
If the data at the bottom of the pyramid is shaky, the entire edifice stands the chance of collapsing. Information and knowledge cannot be useful if they are based on inaccurate data. Company executives will make poor decisions when they are working from bad data.
For example, an American airline once transposed the data for airline miles and the price of a ticket. This means that people who were flying on airline miles were instead charged the number of miles as dollars, leading to up to $750,000 tickets. This was infuriating for the customers and embarrassing for the company. With proper data management, this never would have happened.
Poor quality data can cause companies to lose up to $9.7 million per year. Another study by IBM found that the impact of poor-quality data can be up to $3.1 trillion per year.
Poor data quality can also take a serious toll on productivity. For example, if a salesperson is making calls, it is important that each phone number is correct. An incorrect phone number can lead to wasted time and effort on the salesperson’s part.
Bad data is expensive and time-consuming. When a deadline is looming, employees may attempt workarounds and may cause more problems in the end.
How Data Becomes Corrupted
Today’s corporations are often spread across many offices. Each local office may have their own data storage system. When these systems are integrated, there could be major problems. Vetting data takes a great deal of analysts’ time and energy, but it is worthwhile to protect the integrity of the database as a whole.
Data can also be contaminated if people work with a sloppy mindset. Allowing every employee access to the database may not be a good idea. Trusted employees should be tasked with updating the database.
Having to work around these issues saps productivity and makes employees frustrated. Employees may be annoyed at such a rate that they leave their jobs, leading to costly turnover and the loss of valuable human capital.
Bad Data’s Impact on Business Reputations
Poor data quality does not only affect the bottom line and waste employees’ time, but it can also make a company look bad in comparison to its competitors. Companies may make erroneous assumptions that their data is clean and complete. This can cause inefficiency, excessive costs, customer satisfaction issues, and compliance risks.
In today’s connected world, disgruntled customers can go on Twitter or Facebook and quickly spread their irritation with a company. Sometimes these exchanges go viral, leading to black marks on the company’s reputation. Often, these problems can be avoided with improvements in overall data quality.
When poor data has caused a problem, even employees may not trust it. They may need to ask a customer to help them validate the data, leading to an erosion of trust.
How to Improve Data Quality
Improving data quality can be a daunting task. Many organizations have databases containing millions of individual entries. Having a human employee check all of these data points can be a nightmare. Instead, some data management companies offer artificial intelligence (AI) analysis of company data.
AI programs are able to run through the data and pick up on any major inconsistencies for an employee to process. Using this automated system saves a great deal of time and provides accurate results.
Preserving Data Quality
Data quality management can be a difficult proposition, especially when data is coming from a variety of sources. Integrating these sources can require the full-time work of several employees. AI systems are also used for large-scale data management projects. When your company pays attention to proper data quality management, you will find that your work goes smoothly and that you are able to perform internal and external tasks with ease.
Founder Dinis Guarda
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