As a business owner, you know that data is critical to your success. But what happens when that data isn’t accurate? Implementing data quality controls can help ensure the accuracy of your information and improve your bottom line. Here are tips to get started:
What’s Data Quality?
Data quality is the ability of your data to meet its intended purpose. Data quality has seven distinct characteristics: accuracy, completeness, timeliness, consistency, uniqueness, validity, and integrity.
Data quality is a measure of how well your data meets the needs of your business. If your data is accurate, complete, and timely, then it can be said to be of good quality. Data that is not fit for purpose is said to be of poor quality.
Data is generally regarded as high quality if it is “fit for [its] intended uses in operations, decision making and planning”.
Good data is considered to be “useful” and “relevant”.
The process of data quality management (DQM) is adopted in order to improve and ensure data quality at all times. DQM is a continuous process that requires consistent care and attention in order to maintain high data quality.
Now, you might be wondering, “A process?” Because you can’t just purge a few rows or add a glossary with key terms and call it good. You have to focus on consistently improving and cleansing the quality of your data. That’s called DQM.
Data quality management(DQM) is the act of consistently improving data quality.
One of the important aspects of data management is understanding what constitutes good-quality data. With that in mind, let’s take a look at some of the key qualities of quality data.
Dimensions of Data Quality
What metrics are used to measure data quality? Here are six important data quality dimensions:
- Accuracy Data should reflect real-world scenarios. Verifiable sources can confirm the accuracy of the data.
- Completeness: Completeness measures the data’s ability and effectiveness to deliver all the values that are required.
- Consistency: Data consistency refers to the uniformity and consistency of data as it moves between networks and applications. Data values stored in different locations should not be contradictory.
- Validity Data should comply with defined business rules and parameters. It should be in the right format and within the correct range.
- Uniqueness: Uniqueness ensures that there are no duplicates or overlapping values across all data sets. A low score on uniqueness can be corrected by data cleansing and deduplication.
- Timeliness – Timely data means that data is always available when it’s needed. Data can be updated in real-time to ensure it is always available and easily accessible.
Importance of Data Quality Controls in an Organization
Many organizations are increasingly using data to help them make decisions about marketing, product development, and communications strategies. High-quality data can be processed quickly and analyzed quickly. This results in better and quicker insights that will drive business intelligence efforts as well as big data analytics.
Good data quality management can help extract more value from data sets and contributes to lower risks and costs.
It also helps increase efficiency and productivity, better audience targeting, and more effective marketing campaigns. This will give you a stronger competitive edge.
Poor data quality standards can obscure visibility and make it difficult to meet regulatory compliance.
The quality of your data is essential to the success of your business. Poor data can lead to incorrect conclusions, wasted time and resources, and missed opportunities.
Data quality is important because it can have a significant impact on a company’s bottom line. Poor data quality can lead to lost revenue, wasted time, and decreased productivity.
Create a Quality Control and Quality Assessment Plan
Just as the processes of data entry and verification are integral to good data collection, so is the practice of documenting the steps taken in these processes. Creating and following a plan to review your data before collection begins can help you anticipate the kinds of errors and other problems you are likely to find in your dataset.
When used in conjunction with the data and documentation, these quality-control measures ensure a full picture of the data.
A useful way to document your data checks is to record what steps you took to evaluate it, how you resolved any problems, and what action you then took at each phase of your data lifecycle.
Quality control & quality assurance should involve:
- Determining how to recognize potentially erroneous data.
- How to deal with erroneous data.
- How bad the data will be marked as/flagged?
When analyzing data, researchers often look for anomalies or inconsistencies. They compare their findings to original data sources and make changes if necessary.
Data sets that are consistent and contain the same information can be compared against each other.
- When collecting new data, make sure you use a similar process, environment, and technique to ensure that the new dataset is as similar to the original as possible.
- Ensure that there are mechanisms for comparing data sets against each other that can detect if differences are present. These comparisons can indicate an error if two or more of the sets don’t match.
Your plan should be reviewed and critiqued by others to make sure that it is comprehensive.
Data quality controls are essential for any business that relies on data to make decisions. By definition, data quality control is “a set of procedures used to ensure that collected data are accurate and complete.” Implementing these controls can help improve the accuracy of your information and ultimately lead to better decision-making.