Data is useful. High-quality, well-understood, auditable data is priceless.Ted Friedman, VP Analyst at Gartner
Data is vital for a business and as Ted Friedman noted, high-quality data is priceless. After all, scientists rely on data to make hypothesis, governments rely on data to create policies, and businesses need data in order to make important business decisions.
That’s why the consequences of poor data quality is far-reaching for a business, no matter the size.
But you may ask yourself, when is a data considered “bad”?
Data can be considered “bad” for a variety of reasons, some of them may be due to:
Inaccurate Data – Manual data entry, such as filling out customer registration forms, is a tedious and risky task. Even with double-checking, the chances of data inaccuracy still exist when it comes to inputting data one-by-one into various systems.
Missing Data – It’s also possible that a form has been filed incorrectly and certain required fields are left missing. This kind of missing data may skew the final results if left unchecked.
Duplicates – One contact listed twice in the CRM or even a customer being registered twice in the database.
Out of date – Data that exists in a company’s database should be periodically cleaned in order to maintain quality or improve bad quality data. A data that’s out of date is damaging to the overall quality of the database.
How exactly can a data impact your business? Well, there are plenty of ways that it can. Some examples of that are:
So, what are some of the things that you can do prevent bad data and to increase your data quality?
Well, in terms of preventing data errors, implementing automation solutions can help. Solutions such as Robotics Process Automation (RPA) can help decrease human errors in data entry.
But when the bad data already exists in your system, then you should rely on analysis. Data analytics will help you not only mine the data from your database but also to analyze the quality of the data. It will be able to detect the data issues noted above: duplicates, missing data, and the like.