A Guide to Data Quality for a Data-Driven Organization

Introduction: What is Data Quality?

Data quality has been a topic of discussion for many years, but it has only recently become an area of research in computer science.

The term “data quality” was coined by Garson and Reddy in 1980. It was first used in relation to databases in 1988 when a group of experts from industry and academia met at the University of California, Berkeley, to discuss database management systems and data quality.

We may further define data quality as the degree to which data is free from errors and can be trusted for use. Companies may view data quality differently, but it is typically measured by the following five dimensions:

1. Accuracy — the degree to which data reflects reality and conforms to what was intended when it was created

2. Completeness — the degree to which data has all of its necessary parts; for example, a person’s full name or an order containing all of its items

3. Timeliness — how up to date and relevant a piece of data is

4. Integrity — the degree to which data has not been changed or corrupted since it was created

5. Relevance — how much a piece of data relates to what you want or need it for

What are the Possible Causes of Bad Data?

Bad data is a huge problem nowadays. The more data we create, the more difficult it becomes to manage and analyze this data. The quality of your data is the most important factor when it comes to its usefulness. However, there are many reasons why bad data occurs, including:

  • Data entry errors and typos
  • Data migration errors
  • Human error in the process
  • Incomplete or inaccurate data sources
  • Lack of quality control processes in place

What are the Costs of Poor Quality Data?

Poor quality data can have a negative impact on a business. It will affect the decision-making process and can lead to wrong decisions. In addition, poor quality data can cause operational inefficiencies, financial losses, and even regulatory violations.

Good quality data is not only a requirement for compliance with regulations, but it also helps to increase customer satisfaction and loyalty. Furthermore, good quality data leads to better decision-making, which will result in higher profits for a company.

Practical Steps to Improving Data Quality

The first step for improving data quality is to identify what you want your organization’s level of accuracy and precision to be by asking the right questions, i.e. those that are relevant to your organization. The answers will be used to determine where data quality needs improvement, which will help you set goals and priorities for your organization’s data management.

Secondly, take a look at your current processes and identify where improvements can be made. For example, are there some steps that could be automated, or other steps that could be eliminated? 

Thirdly, make sure you have a plan in place for how your organization will maintain its level of accuracy and precision over time. This might include updates to hardware or software or new policies and procedures. There should be channels for people within your organization to submit feedback about the accuracy and precision of their data. A process for reviewing this feedback and determining what improvements need to be made must be put in place, including regular meetings between managers, supervisors and employees, as well as a time frame for when changes will take effect.

Establishing a Data Management Strategy

Data quality management is not just about ensuring that a company’s data is accurate and reliable, but also that it can be used in ways that are most beneficial to that company.

Data quality management strategy should include:

– Data governance: The responsibility for data governance should be given to an individual or team with authority and accountability. 

– Data stewardship: A data steward ensures that the data is relevant, accurate, complete, timely and useful.

– Data compliance: Ensuring that all stakeholders comply with all legal obligations and regulatory requirements.

– Data privacy: Privacy policies should be developed and communicated to all employees.

Conclusion: Taking the Next Steps Towards Creating a Better Data Quality Culture

Finally, implementing a data quality culture within your organization goes a long way to solving most of the problems with data quality. It should be a culture that promotes and emphasizes the need for data accuracy and consistency. Start by making sure that your employees are aware of the importance of data quality and have the necessary skill sets to be able to execute it effectively.

Create an open culture in which employees feel safe sharing their mistakes, and implement policies that allow employees to take a break when they need it to avoid human errors through tiredness. Moreover, invest in training programmes that will help employees identify errors and learn how to avoid them in the future.