The Impact of Poor Data Quality (and How to Fix It)
The dangers of poor data quality can cause significant damage to a business. Poor-quality data can lead to poor customer relations, inaccurate analytics, and bad decisions, harming business performance.
The sources of poor data quality may seem like a small issue, but it can easily become magnified as repeat errors and different types of errors increase and accumulate.
Missing or erroneous details in email communications can result in customers feeling insulted. An accumulation of errors in data being used for research will almost always lead to skewed conclusions. The combination of skewed conclusions and the accidental process of insulting the customer base does seem like a recipe for losing potential profits.
Good-quality data is not only helpful, but is also necessary for managing projects, controlling finances, assessing performance, and delivering services efficiently. Although data quality is considered important on a superficial level, it is often treated as a low priority. By giving data quality a high priority, the business can benefit from improved sales forecasts, more pleasant customer experiences, and better business intelligence.
Business intelligence is only as good as the data supporting it.
The reasons for collecting and storing data of poor quality are pretty basic. Generally, the problems have to do with translating data from one format to another, but there are other sources, as well. The basic reasons for having poor-quality data are:
Data integration issues: Conversion errors can happen when data is collected from a variety of databases that don’t integrate with the organization’s database. Converting one data format into another often leads to mistakes. CSV files, as a simple example, are typically separated by commas, and converting a spreadsheet to CSV files can result in data being stored in broken up chunks. Conversion issues can become even more complicated if the data taken from an older legacy system is converted for storage in a NoSQL system.
Data-capturing inconsistencies: Data capture is about taking the information stored on a document and transforming it into data for computer storage. This allows employees to retrieve, search, organize, and store documents quickly and efficiently. An organization having two or more departments that use different formatting processes should be concerned with data quality because of variations and inaccuracies. For example, one department may list the customer’s name as Rocal Ltd., while another lists it under the business owner’s name, Joe Jones.
Poor data migration: This commonly happens when data is moved from a legacy system to a new database, or to the cloud. Moving data into a new system comes with some risks. Some of the data’s values can be missing or irregular. If the data isn’t of good quality upfront, new problems can arise, such as data corruption and missing data.
Data decay: Data decay describes the deterioration of data quality, typically in the marketing and sales departments. Data decay is often an expression of old, outdated information. (For example, approximately 40% of email users will change their email addresses every two years. Has your organization developed a response to that issue?) Some have referred to a database crash as a form of data decay.
Data duplication: Duplicated data may skew business intelligence. It is possible for problems to develop if duplicated data is used for statistical purposes. Also, if duplicated data is correct in one location, and missing parts in another, problems can arise.
The consequences of using data of poor quality can range from minor to disastrous. It can result in lost income, employees quitting in frustration, and even painful monetary fines. There are several potential consequences:
A loss of income: Losing clients and potential clients will have a negative impact on revenue.
