Data quality has become increasingly more difficult with the emergence of new technologies. The time and money that is wasted when data is incorrect or inaccurate can mean any number of things for your company’s bottom line, from higher costs to lost customers to lawsuits. Organizations that succeed in overcoming data quality challenges will be able to operate more efficiently and make better business decisions.
We can’t stress enough on the impact of poor data quality on your organization’s overall performance. It can lead to an organization being unable to meet its objectives because it cannot accurately measure the effectiveness of its actions. It can also lead to increased costs, as organizations have to spend more time and resources on fixing the errors in their data.
In fact, Gartner recently stated in an article that poor data quality costs organizations an average of $12.9 million per year. For that, businesses need to take extra steps to improve data quality by identifying key challenges of data quality and try to overcome these issues.
Why is data quality a challenge?
Data quality is a challenge because it is difficult to tell the difference between accurate data and inaccurate data. It is also difficult to know what data needs to be collected, who should collect it, and how they should collect it.
It can be broken down into three categories: completeness, correctness and timeliness.
Data completeness means that all the necessary data for a given task are available in the system. Correctness means that the data is accurate and not corrupted or inaccurate. Timeliness means that the data is available when it needs to be used for a task.
The difficulty of solving this problem comes from the fact that there are many different factors affecting data quality, such as time limitations, human error, lack of resources and more.
What are the Key Data Quality Challenges?
Data quality issues can have an immediate and indirect impact on any business decision. Imagine the cost of a business decision based on inaccurate data, and you’ll understand the importance of improving data collection and storage quality.
Key data quality challenges are:
1- Lack of data standardization
Data quality challenges can arise due to a lack of standardization in data sets. For example, different departments may store information on different software systems so that the data is not compatible or interoperable between departments.
When different datasets have different formats, inconsistent naming conventions and other inconsistencies in their metadata, it becomes difficult for users to compare them with each other.
Data standardization can be done in two ways:
- Automating the process of data standardization by using a software
- Doing it manually.
The first option has some benefits like consistency, accuracy, and scalability. However, it also has some disadvantages like cost and time-consuming process. The second option is more time-consuming but cost-effective and doable by most organizations with limited resources.
2- Data is inaccurate
When data is inaccurate or out of date, problems associated with the quality will arise. These errors can lead to decisions being made on bad information that could have been prevented with better data quality checks at the time of inputting the data.
There are many ways in which organizations can make sure that data is accurate and reliable. One of them is by using artificial intelligence to clean up inaccurate data, identify errors and fix them before they reach critical mass. Another way to ensure accuracy of data is by establishing clear guidelines on how data should be extracted in an organization with regards to its purpose, quality, format and storage requirements etc.
3- Data is not current
Data quality challenges are the world’s biggest problem today. There are many factors that contribute to these issues, but the two most important ones are data is not current and data is inconsistent.
When you have outdated data, it can lead to poor decision making and poor business outcomes. There are many ways to make sure that your data is up-to-date. One way is by using a tool for updating the data in your organization.
4- Incomplete data
Incomplete data can have a great negative impact on organizations. It is important to ensure that the data is complete and accurate. There are many ways that incomplete data can affect organizations, for instance, it can lead to inaccurate forecasts and projections, which will result in an organization making decisions based on wrong information.
It can also lead to bad customer service decisions because there will be no way of knowing the needs of customers. In addition to errors in business processes or inaccurate reporting which could result in loss of revenue or other damages.
Overcoming this need to carefully examine the collection of data process and try to automate it using the latest technology in order to reduce errors and increase efficiency.
5- Lack of time to analyze data and identify errors
Data quality professionals play a vital role in the world of data science. They are responsible for making sure that the data collected and stored is accurate, consistent, reliable and useful. However, they face many challenges. These include the lack of time to analyze data and identify errors that may be present in it.
6- Lack of unified data collection process
Without a standardized process for collecting and storing data, there is no way to ensure that errors are corrected and that the data remains accurate.
The majority of organizations dealing with data quality issues face the same problem. In the absence of a process to standardize how they extract and store data, data inconsistency and incompleteness are common.
It is essential to have a well-defined process with extract steps for how your company will collect data from various sources and assess it before storing it.
7- Duplicate data
Duplicate data is a challenge for data quality because it can lead to inconsistent data sets, which can be difficult to manage. Inaccurate information is also a concern. Data duplication can come from multiple sources.
It may be an error in the source system or due to incorrect data entry by an individual or even a third party process that duplicates the same record across different systems.
How to fix data quality issues?
Data quality issues can be addressed by:
1. Identify the root cause of the data quality issue.
2. Take corrective action to fix the root cause.
3. Monitor for recurrence of the data quality issue.
4. Implement a strategy to prevent recurrence of the data quality issue in future (if possible).
What are data quality checks?
Data quality checks are necessary to ensure that all data is accurate. In an organization, there are many people who create data and use it. The data may be inconsistent with other data, which means that the company may not be able to make decisions based on accurate information and is at risk of making poor business decisions.
Data quality checks can minimize this risk by making sure that all data is accurate by using a process that includes a data quality control plan.
The following are data quality checks:
1. Verification of the source of information
2. Data consistency checking
3. Data transformation
4. Data formatting and normalization
5. Data entry accuracy check