Data Quality Analysis Simplified: A Comprehensive Guide 101

Today, data is a valued asset in every organization. When analyzed, organizations can derive hidden insights from data. This will help them to understand their customers, products, and operations better. The insights also spearhead the decision-making processes, helping the organization to make decisions based on evidence, which is key for growth.

However, most data collected or generated by organizations is full of noise and inconsistencies. Reliance on such data for decision-making can lead to poor quality decisions. Thus, organizations should ensure that their data is of high quality. This article is a detailed guide on the Data Quality Analysis process. This will help you learn how to ensure that your data is of high quality for a sound decision-making process.

What is Data Quality Analysis?

Data Quality Analysis is the process of analyzing the quality of data in datasets to determine potential issues, shortcomings, and errors. The purpose is to identify these and resolve them before using the data for analysis or modeling.

It can be as simple as inspecting the data parsed in a text editor to ensure that the previous step is working correctly. This can help to establish if any pattern is obvious or if some data points are very different from the other data. The data may be plotted in different dimensions.

Every organization that relies on data for decision-making should consider practicing Data Quality Analysis. This will ensure that their decisions are based on accurate and up-to-date data rather than incorrect and out-of-date data.

Cost of Bad Data

The cost of bad data is worse than most organizations realize. It costs 10x more to complete a unit task when working with flawed data than when working with perfect data.

Ringlead has created a visual that shows the cost of bad data depending on the number of records. It estimates the cost of bad data to be approximately $100 per bad record. Thus, organizations with tremendous amounts of data such as Google can incur significantly higher costs.

Another study done by IBM found out that the cost of bad data in the United States is approximately $3.1T- T for Trillion.

But why is bad data so costly to organizations? The reason is that managers, knowledge workers, decision-makers, and others must accommodate it in their daily tasks, which is expensive and time-consuming.

Gartner also did research and established that 40% of failures in business initiatives are caused by bad data. Bad data also reduces labor productivity by 20%.

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Dimensions of Data Quality Analysis

Data Quality Analysis assesses data based on the following dimensions:

1) Accuracy

This involves determining whether the data is correct to ensure that it reflects the real-world situation that is being looked at. To ensure the accuracy and precision of your data, it is good to optimize your data management strategy constantly. Data Accuracy and Data Integrity are closely related.

2) Completeness

Data Quality Analysis also checks whether data is comprehensive because incomplete data may be unusable. Although it’s not recommended to collect more data than what is needed, make sure you have the mandatory values when creating new entries into the database. Otherwise, you will have incomplete phone numbers, names without last names, and more such instances.

3) Relevance

Quality data should be able to serve the purpose for which it was collected, thus, you must ensure that you collect the data needed to serve the purpose at hand.

For example, when customers are signing up for a trial with your product, age may not be useful to you. It is data without a purpose. Even if it is correct, it is not relevant in this case.

4) Consistency

High quality data should not contradict the data stored in the other databases. Otherwise, you will have to consider one of them as wrong.

When inconsistencies exist between databases, it’s difficult to determine the accurate one. Thus, you should ensure that you only have one source of truth as far as data is concerned.

5) Accessibility

Data Quality Analysis also determines whether data is accessible to the right people. As a company interacts with customers, partners, employees, and prospects, data ends up being scattered in different tools. If there is no good software integration, you may fall into a data silos problem.

Even if data is accurate, consistent, and relevant, it won’t serve its purpose if the team supposed to leverage it can’t access it. To improve data accessibility, integrate the software systems in your organization.

6) Timeliness

Data changes constantly, and out-of-date data may not accurately represent the current situation of the matter being assessed. Even though it’s good to keep historical data, you should have a clear sense of time. If possible, try to get real-time data to capture changes as they happen.

Data Quality Analysis Stakeholders

The following roles take part in the Data Quality Analysis process:

Data Owner / Governance Team: The work of the data governance team is to establish processes and protocols that should be implemented for high-quality data. They also choose the data management and analytics platforms to be used.
IT Team: The IT team should take the responsibility of configuring and managing the data storage tools within the organization. They can grant and deny access to these systems for different roles within the organization.
Data Stewards: These are the employees spread across different departments within the organization who collect, analyze, and make evidence-based decisions daily. They should evaluate the data input to ensure that it meets the quality standards and policies established by the data governance team.

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Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Data Quality Analysis Best Practices

The following are some of the best practices to consider during Data Quality Analysis:

Determine the most important data quality metrics for your organization.
Let everybody in your organization understand data quality and its importance.
Perform data quality audits on a regular basis.
Invest in the right resources for data analysis, reporting, and quality training.
Establish the reasons behind data quality failures or successes made by your teams.
Put data management tools into use. These will help you eliminate or reduce human errors.
Rely on a single source of truth across the entire organization as far as data is concerned. This can be in your sales software, CRM, etc.

Conclusion

In this article, you gained a basic understanding of Data Quality Analysis. You also learned about the various stakeholders involved in the Data Quality Analysis. Furthermore, you learned about the best practices that can be incorporated to conduct an efficient Data Quality Analysis. In case you want to export data from a source of your choice into your desired Database/destination then Hevo Data is the right choice for you!

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