Change is the only constant thing in life. Everything around us changes and so does the Data you use for business entailing contact phone numbers, email addresses, and so on. Data used by you maybe a month ago become obsolete quickly and the requirement of new data arises for better decision-making. In such cases, you need to keep a track of your data by a thorough analysis that removes bias and uses historical data to create actionable recommendations and predictions for the future. This process of keeping a track of data and updating the changing data referred to as Data Quality Management.
Maintaining the data quality of your business is unquestionably important to stay competitive in an increasingly digital landscape. It has been also studied that poor quality of data can cost a business $9.7 million annually. Poor quality of data results in a 20% decrease in employees’ productivity and explains why 40%-42% of business initiatives fail to achieve desired goals. Poor data quality not only ruins the business reputation and misdirects resources but also slows down the retrieval of information, and result in false insights and missed sales opportunities. For example, if your business is using a poor quality of data entailing incorrect contact name or email address of a lead or a customer, then your marketing emails could go to the wrong recipient that results not only in time wastage but it also puts a question on your brand image.
Data quality matters to your business a lot especially when engaging in a merger or acquisition; in such cases, you need to unify disparate data sources under common data standards and technologies. Besides this data quality also plays a significant role in any enterprise resource planning or customer relationship management.
But now the question is how to maintain the data quality? In order to achieve this objective, you must have a robust team of Data Analysts who guarantee data quality. Generally, data issues happen due to the mistakes made by employees during data entry or if any software error occurs then data problems come into the picture.
Data analysts examine these issues and analyze the accuracy of data which is referred to as data profiling. This process involves removing outliers and irregularities in the data. It ensures there are no missing data fields and information has been registered correctly. The major aspects that cause poor data quality entail inconsistent formatting of dates and numbers, unusual character sets and symbols, duplicate entries, and different languages and measurement units. Data analysts while profiling a large volume of data go through various procedures like constructing data hierarchies, rules, and term definitions to analyze the interrelationships between types of databases. Data profiling rules are simple like contact’s full name must be capitalized and consist only of letters. Thus, data profiling keeps a track of the number of entries that meet the rules and in this manner, it checks if the results are as per the threshold required by the business.