Data quality is key to a company’s success. Poor data quality can lead to bad business decisions and increase costs for your business. It’s essential to have a data quality strategy in place to minimize these risks and maximize the benefits.
Data analytics is one of the most valuable assets for a company to have. It helps businesses make decisions based on data/facts, understand their customers better, and find what they want and don’t want.
What is a Data Quality?
Data quality is the degree to which data is complete, accurate, and consistent. You can measure data quality through data profiling and data auditing.
The following are some of the factors that can cause poor data quality:
- Incomplete or inaccurate input of data into a system
- Data corruption or deletion
- Failure to maintain up-to-date or accurate records
- The use of poorly designed indexes in a database
- A lack of appropriate validation checks in software programs
How to measure data quality?
You can measure the quality of data by measuring the following metrics:
• Accuracy
Accuracy is crucial because inaccurate information can have significant consequences. Data should closely reflect real-world scenarios. Use verifiable sources to confirm the accuracy of data points as a measure of how close the values match up with verified accurate information.
• Completeness
Completeness is the ability of data to deliver all available critical or mandatory values.
All your data should be consistent and complete. When reviewing data, ask yourself if you’ve got all the information you need to present a comprehensive image. You might need a customer’s first and last name, but their middle initial might not be mandatory to make an impactful statement.
With incomplete information, it might be unusable. Let’s assume that you are sending out a mailing. It is necessary to have the customer’s first and last name to ensure that it goes to the right place – without it, you might not be sure where the mailing will go.
• Consistency
Data consistency refers to the uniformity of data as it moves across apps and networks. It also means that multiple sources should produce the same dataset and doesn’t conflict. If information contradicts itself, it’s hard to trust the accuracy of the data.
An example from the healthcare field is that if a patient’s birth date is 25 Jan 1995 in one system, but it’s 14 Jun 1998 in another, then that information can be unreliable.
• Timeliness
Timeliness refers to how up-to-date information is. Keeping data up-to-date and accurate is crucial in today’s day and age of business. Timely data is important because it allows you to be proactive, something that can help your business grow.
When there is a delay in information, it’s not actionable and can lead to bad decisions by people. It means any organization incurs time, money, and reputational damage due to the time delay.
• Uniqueness
Data quality issues are often addressed when it comes to uniqueness. Duplicates, sometimes two database rows that describe the same real-world entity, may cause customer master data problems.
You want to ensure no duplications in the data you’re using. Duplicate records would cause issues like inaccurate analysis and low uniqueness scores. Analysts may need to clean up their metadata or deduplicate their data to resolve this problem.
• Relevance
Relevance is one of the main aspects of data quality. Gathering irrelevant information will waste your time and money, as it won’t provide any helpful insights for your analyses.
When assessing data quality characteristics, it is essential to know how relevant the collected information is. Does the data collected make sense, and are we collecting it for the right reasons?
• Validity
Data validity is checking the data integrity, accuracy, and quality before using it in your business. It measures how close data points are to their predetermined values. To do this, you’ll need to compare data to a set of rules you have already defined to ensure that the data is correct.
Data is helpful only when it satisfies business rules and parameters set by your organization. The information must conform to accepted formats and not adopt values outside the defined range.
How to craft a strategy to improve Data Quality?
A data quality strategy is a planned set of steps and activities based on the organization’s needs to improve data quality.
Organizations need to develop strategies to improve their data quality by:
• Understand your current data:
You need to understand what you currently have before you go ahead with any changes. You can do this by listing all the information and then categorizing it based on its type (e.g., customer information, product information). Doing this will help you identify where these errors are coming from and what kind of errors exist in your current database. It will also help you decide the steps to be taken next for improvement.
• Create an improvement plan:
Once you have identified your current database’s errors, you can begin to brainstorm different ways you can improve the database.
• Data governance:
Data governance is managing data to achieve organizational goals. It is a set of rules, processes, and policies put in place to protect and manage the data that an organization collects.
Data governance helps organizations to improve their decision-making process by providing them with a clear understanding of the data they have and how to use it. It also ensures that they are complying with relevant laws and regulations.
• Data profiling:
Data profiling is widely used in data quality management to understand all only assets part of the pipeline and to make sure data is up-to-standard. Give due consideration to data profiling because many people populate the said information and change frequently due to many factors.
You can use data profiling to find the keys related to your data entities across different databases. It can also uncover any undesired relations between these entities and allow you to correct them.
• Data matching:
Data matching is all about looking for similarities in two or more sets of data and checking to see if they match. One example is deduplicating a database by finding repeating bits and removing them. You can also find matching entities across multiple data sources like databases, social media platforms, CCTV footage, etc.
• Focus on data quality reporting:
To measure the quality KPIs, you can use the data gathered from data profiling and matching. Reporting also involves operating a problem log of known issues, with documentation of any follow-up actions taken.
Organizations with strong data quality initiatives will find it useful to operate a data quality dashboard. This dashboard tracks KPIs as well as the trend for these KPIs and even displays issues in the issue log.
• Master Data Management (MDM):
Organizations need to be more proactive about data quality. It means picking a sustainable plan for preventing issues instead of spending time and resources on data cleansing every few months. One way to do this is through a master data management (MDM) framework.
Master Data Management (MDM) ensures that all the information is consistent across your business. With a unified master data service, you should be able to focus on other, more crucial aspects of your IT.
Data quality is a necessary evil to keep customers satisfied and happy. It’s not enough to have data; it needs to be accurate and relevant. The more accurate the data, the better it is for the company.
Data quality will not only help companies satisfy their customers but also improve their bottom line in many ways, such as increasing customer lifetime value, reducing churn rates, and generating more revenue from existing customers.
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