How to Do Proactive Data Quality

How to Do Proactive Data Quality

Maintaining high-quality data is becoming more and more important for any organisation. According to Gartner, poor data quality can cost organisations around $12.9 million a year! However, many organisations also find themselves stuck in a cycle of reactive data quality measures, which often lead to short-term fixes rather than long-term solutions.?

In today's article, I will explore how to shift from reactive to proactive data quality management by leveraging a Data Governance framework.

Shifting from Reactive to Proactive Data Quality

Most organisations nowadays recognise the importance of data quality. They most likely have data cleansing routines as data is loaded into data warehouses. However, these efforts are typically tactical fixes addressing issues only when they are detected. For example, missing fields might be defaulted to a placeholder value, which may be better than an empty field, but does not ensure that the data is correct.

Proactive data quality involves preventing data issues from occurring in the first place. This shift requires more than just addressing problems as they arise. It means having a strong approach to managing data quality, which can be achieved through Data Governance.

Why Data Governance?

Implementing a Data Governance framework is crucial for proactive data quality. Data Governance establishes the roles, responsibilities and processes needed to manage data quality consistently across the organisation. It ensures that data quality is maintained at the source, reducing the need for repeated data cleansing and enabling more reliable data usage.

Data Governance is a massive support towards achieving proactive data quality rather than reactive. See below for some key steps in using Data Governance to make this happen.?

Steps to Proactive Data Quality Through Data Governance

1. Get Buy-In from Stakeholders - You will need to encourage senior stakeholders to understand and support the need for Data Governance. To do this, align your Data Governance goals with the organisation's strategic objectives to demonstrate its value.

2. Identify Data Owners and Stewards - These individuals are accountable and responsible for the data quality for their data.??

3. Define Data Quality Standards - Next, work with the Data Owners and Data Stewards to establish clear data quality criteria.. This involves defining what constitutes acceptable data quality and setting rules for data entry and processing.

4. Implement Data Quality Processes - Use the data quality rules to develop and implement processes for data quality reporting and issue resolution. Regularly monitor data quality and report any issues to the Data Owners and Data Stewards for resolution.?

5. Create a Data Glossary/Catalogue - Develop a Data Glossary that includes definitions and business rules for all critical data elements. This helps ensure consistency and clarity across the organisation.

6. Establish a Data Governance Committee - Form a committee that oversees the implementation of Data Governance policies and procedures. This committee should regularly review data quality reports and address any escalated issues. Read my previous blog on Data Governance Committee’s here.?

It's no overnight task?

It's true, that transitioning to proactive data quality is not an overnight task, but it is essential for long-term success. By implementing a Data Governance framework, organisations can ensure that data quality is managed proactively, leading to more reliable data and better business outcomes. Remember, Data Governance is not just an add-on; it is the foundation that supports all your data quality initiatives.

Feel free to book a call with me if you would like to find out how I can help you implement Data Governance and improve data quality.?


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Originally published on www.nicolaaskham.com

Samuel Babajide Tanimowo CDMP? MBA

Data Management | Data Analytics | Data Quality | Data Governance | Business Intelligence | Master Data Management

4 个月

Good quality Data is important for effective Data management and governance. Its importance cannot be overemphasised. Very good article Nicola Askham

Gideon White

Strategic planning | Operations excellence | Programme assurance | Continuous improvement

4 个月

A clear piece as always ?? One thing I wonder about sometimes is the juxtaposition of All Things Data Governance & Data Quality with All Things AI and All Things Digital Ecosystem. By that, I mean on the one hand how we view AI as a potential for automating more of the effort for data quality; and on the other hand how upstream consideration for enhancing data quality on a preventative basis is built into the design, upgrades and what have you of technology/ies, systems and applications etc. I only suggest this because of thinking about the technology 'tools' that both hold data and yet also how data quality is pursued. Does that make sense? When I also think about statutory reporting in UK HE at the moment, all those 'quality rules' and 'tolerances' that are such a 'mare, how do we tackle 'rules' like that on an ongoing basis, whether for statutory reporting or internal requirements.

Mathias Almfyr

Helping companies unlock the Power of Data | Data & Analytics consultant at Conpanion ???????

5 个月

Good article Nicola Askham! Tomas K?hlman, I think we are doing good when it comes to the steps for working more proactive with Data Quality. ??

Nur Muhammad IFtemam

Helping Coaches & Content Creators 10x their growth in 60 days with proven SEO & promotion strategies. | 100+ Happy clients

5 个月

Proactive data quality management is a game-changer! Looking forward to learning more about how Data Governance can lead to long-term solutions!

Surinder Ram

Data Analytics Leader | Passion for Driving Business Transformation through Strategic Insight and Innovation

5 个月

Spot on message! Many Thanks Nicola Askham

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