Data does not come in a ready form. It is being collected. Data gathering may come from different sources such as: human input, surveys, forms submits, orders, data migration, web sites access, questionnaires, etc. Collected data is being sorted out and formatted. Next important steps are data maintenance and accessibility. When data is organized it becomes available to various teams in the organization. Company employees start using the data and it is being shared by different teams and departments. It becomes critical to keep the data as clean as possible. To secure data quality companies install various systems and tools. Despite data governance practices and data quality tools, dirty data still manages to exist in almost every CRM or database out there. Here are the top data quality issues:
Poorly established formatting is one of the issues that lead to data misrepresentation. Wrong format creates havoc and headache for data analysts and data scientists. For example, you need to append data from one or two sources to the existing table. If the date format is not consistent, i.e. there are DD/MM/YYYY, MM/DD/YYYY or YY/MM/DD formats, the end table would contain wrong data. Needless to say that any reporting based on such dirty data would be misleading and resulting in inaccurate insights. Also in many cases, it is simply impossible to join ‘date’ fields that have different formats. Data analysts or database administrators would require essential time to fix the data by creating consistent formatting prior to working on data analysis. Luckily, this problem is easy to fix and some tools have internal programs to fix this issue.
Another data quality challenge is incomplete data. Missing records that were corrupted during the data migration process or plain data input error that created empty fields. Imagine a call centre representative having empty phone line on the customer information screen or missing numbers in a revenue column. The result is an ineffectiveness and frustration of a customer service rep, lost time and money and wrong analysis of organization profit, faulty business decisions.
Duplicate records are hard to be noticed since the data is not null and the format is correct. However, it is a critical data quality defect. It may be a result of a computer system error, developer bug or multiple data input. The issue requires deduping meaning finding and removing duplicate records. There are various techniques that allow to recognize duplicated data including human apprehension, data massaging and algorithms. Duplicates are true enemies of quality data and, therefore, correct data insights and analysis.
Uncategorized data is data that is not categorized or standardized. Data that is not grouped is impossible to segment on or run any insights. Uncategorized data is a field that should have a drop down but instead allows free text. Example fields include: industry, title, age, revenue, interests and so on. This is an easy data quality issue to fix by replacing free text to a drop down, however, can be a nightmare to update historical data. StrategicDB is an expert in data categorization, using both machine learning technics and human intervention to categorize all your historical data.
Today organizations gather tons of valuable data that needs to be stored. Data storage is costly but absolutely mandatory to preserve data quality and data cleanliness. Data should be protected from any kind of negligence. Taking care of data storage helps avoid data loss, i.e. data quality issue.
Data is useless when it is not available across departments, teams and branches.. Efficient work flow depends on data sharing. But it does not mean that every employee would have unlimited access that can damage the data. Data access should be secured and protected. Administrative rights blocking data deleting and changing are extremely important here.
Data is central but it is also very fragile. Incomplete, dirty, inaccurate data is powerful enough to ruin any business. It leads to incorrect analysis, inaccurate takeaways and wrong business strategies, lost opportunities and decreased revenues. Modern business is data driven and quality data requires good and ongoing care. At StrategicDB we are happy to help you to make the most of your data by providing data cleansing services.