Guide: Data Governance

The Basics of Data Quality

Without a doubt, data is becoming a core part of every business operation. Thus, the quality of the data gathered, stored, and consumed during business processes will determine the success of doing business today and tomorrow.

In this guide, we will learn:

This is part of our extensive data governance guide.

What is Data Quality?

Data quality is a metric that assesses data quality based on accuracy, completeness, consistency, reliability, integrity, and timeliness.


As data processing has become more tightly integrated with business operations and businesses increasingly use data analytics to assist drive business decisions, the emphasis on data quality in enterprise systems has increased. Data quality management is an important part of the overall data management process. Efforts to improve data quality often link to data governance initiatives, which try to guarantee that data is formatted and used similarly across an organization.


Data Governance programs typically start small, undertaking the following steps:


  1. Identifying roles and responsibilities
  2. Defining your data domains
  3. Establishing data workflows
  4. Establishing data controls
  5. Identifying authoritative data sources
  6. Establishing policies and standards


Leaders in data governance must educate their organizations on creating collaboration and implementing a successful data governance program. Companies that follow the six stages outlined above will be in a good position to build and strengthen their data governance program, increasing acceptance and amplifying the impact of data governance on data-driven and innovative business decisions.

What is the Difference Between Data Quality and Data Governance?

First and foremost, Data Governance is not synonymous with Data Quality. Although they are connected, data quality is considered as a part of data governance, but may also be defined as a completely different field, intertwined with data governance.


Data quality refers to the degree to which data is accurate, complete, timely, and compliant with business regulations. In contrast, data governance refers to the delegation of authority, control, and shared decision-making over data assets management.


Because data governance and data quality are so intertwined, their connection, symbiotic, as it gets built on mutual interdependence. The result is that you will require both if you want to properly manage and increase the quality of your data in the long haul: You may find it difficult, if not impossible, to accomplish one without the other.


Unfortunately, many businesses are still unaware that they must do both. While it is uncommon, if not impossible, to come across a company adopting a data governance framework without improving data quality, it is very normal for organizations to begin data quality initiatives without a data governance framework in place to support them.


Unfortunately, this reduces many data quality projects to tactical solutions with only short-term benefits.

Why is Data Quality Important?

Bad data can have a substantial impact on a company’s bottom line. Poor data gets blamed frequently for operational blunders, erroneous analytics, and ill-advised company plans and strategies. Added expenses when products get supplied to the wrong client addresses, lost sales opportunities due to erroneous or incomplete customer records.


Fines for inappropriate financial or regulatory compliance reporting are just a few examples of the economic damage that data quality issues can create.


On the other hand, good data quality enhances the accuracy of analytics applications, leading to better corporate decision-making that boosts sales, improves internal processes, and offers companies a competitive advantage over competitors. High-quality data can also assist in broadening the use of Business Intelligence (BI) dashboards and analytics tools. If analytics data gets viewed as reliable, business users are more inclined to depend on it than their gut feelings or spreadsheets when making choices.


Thus, data quality is critical since businesses:


  • Need accurate and timely data.
  • Need good data for managing service effectiveness.
  • Have to prioritize and make the most efficient use of resources.
  • Have to report to auditors and inspectors, who will assess the company’s performance and governance.

How to Improve Your Data Quality

Data quality is not something that happens by chance. If you have not already, it is time to be more proactive and plan for data quality.

Here are some practical ways to increase data quality in your company:

Adopt a Lead Generation Data Capture Strategy

Allow users to update their information on the form by using restricted values, field validation, or field pre-population. You greatly enhance the likelihood that users will not exit the form and submit it by assisting them in filling it out automatically. Such solutions can help auto-complete certain parts on the form and enhance remaining fields with contact and company data on the backend, making it easier for users to fill out and for sales reps to learn more about the lead.

Avoid and Correct Duplicate Records

Duplicate records are often recognized by email address; however, in B2B contexts, finding all duplicates is not necessarily sufficient.


Duplicates waste money, cause sales confusion, and disrupt the marketing automation process. Thus, catching duplicates as quickly as possible is ideal and occasionally erasing the duplicate entry – before it accumulates history and activity – is the best approach. Set up trigger alerts to receive notifications automatically to accomplish this. Run weekly reports with existing duplicate records, and look into the sources and processes that resulted in copies. You can both prevent and solve systemic problems this way.

Standardize the Data

Because data gets obtained from diverse sources, it is vital to normalize it because it may include several spelling alternatives, such as the spelling of the United States as the U.S. vs. the USA. This detail directly impacts smart lists, scoring, segmentation, and similar features. People may think of the United States, the United States, and the United States as the same country, but marketing automation tools and CRMs recognize them as three separate data points. As a result, data standardization is critical for developing that one-of-a-kind method of entering a data point.

The Takeaway

Implementing data quality processes and strategies will make your organization more efficient and generate more income. As data processing has become tightly woven with business operations and as businesses increasingly use data analytics to assist drive business decisions, the importance of data quality cannot get overstated now more than ever.

Agile Data Governance with Satori

Satori helps you with DataSecOps for your modern data stack. This includes continuous sensitive data discovery, integration with existing data governance tools to make data governance more efficient and immediate, as well as means to streamline access to sensitive data and create security policies that are independent of the specific data infrastructure you’re using.

Last updated on

December 26, 2021

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