Every business and organization around the world depends on data – to understand their customers, serve their patients, attract the best talent, and guide their day-to-day operations. While a mind-boggling amount of data is collected, aggregated, and analyzed each day, volume is not what makes data valuable. The quality of the data is the most important factor in deriving true value from it.
To analyze the quality of your data and ensure that it is fit for purpose, here are six questions you should ask about the quality of your data and how to address it if it does not measure up.
1. Is it Complete? Do I have all the data I need to make sound decisions?
First, look at your entire operation and determine what information should be collected in order to achieve your objectives. Train your people on what data to collect, and be sure to have dedicated fields in your database for the information you need. What is critical will vary by industry and even by department, so gather input from across your enterprise to get a complete picture of what to collect and what to skip.
Then, analyze your existing data to see how complete it is. Data profiling tools can quickly identify if any fields are routinely being skipped or entered in such a way that would render the data unusable. Creating a profile of your data allows you to put processes and procedures in place to address issues and ensure that your data is complete.
2. Is it Consistent? Is the same piece of data consistent across all systems?
When aggregating data from multiple systems or collecting input from several departments, it is important that the same piece of data is consistent. Sales in each month, for example, should be the same regardless of which database you query. The same holds true for web traffic, billing contacts, physical addresses, and any other piece of data that may reside in multiple systems. Profiling tools can quickly rate the consistency of your data and highlight areas of concern.
3. Is it Accurate? Does my data represent reality?
One of the most important aspects of data quality is whether your information is in fact correct. A billing address that is complete, properly formatted, and consistent across all systems has no value if it is not the correct address for your customer – or is not a real address at all. Data quality solutions can improve accuracy by automatically validating addresses.
4. Is it Formatted? Are all similar data types formatted in the same way?
Telephone numbers, websites, e-mail addresses, and dates are all examples of data that require a specific format to be complete. Establishing good data entry practices and structuring database fields properly will help ensure that data is correctly formatted. Data quality solutions will cleanse your data to standardize formats, turning 4125551212, (412) 555-1212 and +1-412-555-1212 into a consistent format that fits your defined standard.
5. Is it Timely? Do you have the most current data when you need it?
Making decisions based on outdated information is never a sound practice, so it’s important that you can access the most current information when you need it. For some departments this might be quarterly, while others require daily or even up-to-the-minute information. Data quality solutions can process data in batches – at the close of business each day, for example – and in real time as data is received. The best data quality solutions are built to minimize latency and maximize processing speed to ensure that the most current data is available when you need it.
6. Is it Valid? Does your data meet your specific needs?
To be of value, data must include all of the unique criteria a business or organization needs to make decisions. A record that does not include a vehicle VIN number might be fine for a hospital but is of little value to an automobile insurance carrier. If only a portion of your customer records have country information, it will be difficult to make an accurate analysis of customer distribution. Data quality solutions include profiling and cleansing tools that help fill in missing information and flag invalid entries.
By asking these six basic questions, you can create a plan to improve your data quality that begins with addressing existing issues and establishes rules and guidelines to ensure that incoming data remains of the highest quality. The most effective data quality improvement initiatives take a holistic approach to enterprise data and leverage solutions such as the Enlighten Data Quality Suite from Innovative Systems that address every phase of data, from entry to ongoing monitoring.