Data Governance Interview With Justin York

I thought it was time that I interviewed Justin York for my blog. I have known Justin for many years and he was my first Data Governance Coach Associate. You’ve possibly seen guest posts he has written in the past or seen him delivering Data Literacy courses for me, so I thought it was time to ask him to share how he got into Data Governance and to share some insights from his many years of Data Governance experience.

After a long military career in army logistics, with the final 15 years working in Data Management, Justin moved into consulting and eventually moved more into Data Governance. Justin has since worked in a myriad of businesses all with different challenges. Justin is also a qualified coach and said he enjoys the engagement with people whether on a contract or simply in day-to-day life.

How long have you been working in Data Governance?

I have been working in Data Governance formally (under that title) for around 16 years, however, I have been engaged with the ownership of data and the decision-makers for around 20 years or so.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

I have worked for a long time in the realm of information/Data Management and while employed on a contract at the UK Ministry of Defence met Nicola Askham and at that point realised that much of the work that I have been doing could loosely be called Data Governance.

The real realisation was that all the challenges that we face around data come under people and whether that’s Data Management or Data Governance you need to get them to understand what you need from them and why. So working under the Data Governance umbrella seemed the most logical step.

What characteristics do you have that make you successful at Data Governance and why?

Primarily I enjoy the engagement with people and the vast majority of the challenges involved in Data Governance spring from people after all people touch the data at all points of its journey and systems generally (notwithstanding the magic of AI) do what their human masters instructed them to do so if there’s a fault that was generated by a human.

So my key skills are engagement with and fast understanding of people and what makes them tick, coupled with excellent levels of patience and the ability to communicate across all levels and explain just what Data Governance is, its benefits and the way that people can engage.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

There are so many books out there and all sorts of models and to be honest, while I keep up with the latest trends I don’t tend to read a lot of books and to be fair many of them either reiterate the same material with a different name or refer to well-established materials. I use the DAMA DMBoK as a useful reference and then I typically use my own experience and adapt that to individual organisations’ needs where I work.

What is the biggest challenge you have ever faced in a Data Governance implementation?

Buy in from the management or the people on the ground, Data Governance initiatives to many are just another fad that will pass with time and so with their busy existences, they try to ignore it and get on with their busy jobs. However, they fail to understand what the benefits are such as giving them more time back to do more in their busy jobs, so I think it’s turning the supertanker of suspicion is the biggest challenge.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I have a particular interest in aviation so I would like to get some work in an airline or manufacturer or space.

What single piece of advice would you give someone just starting out in Data Governance?

Expect the journey to be difficult and challenging because people generally won’t welcome you with open arms.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

There are two:

• While working at a financial services organisation on director was quite challenging and dismissive of the Data Governance work and would not engage. Eventually, we had a couple of options, one was to go over his head which may create additional friction and the other was that we recognized that his data was not critical to the project so we simply sidelined him.  The strange thing was that when he was sidelined and the rest of the organisation kept moving forward he wanted to be involved rather than be left out.

• On a different contract I was faced with a member of the management who stated “We don´t need Data Governance as we already do it”, so I asked him to describe what it was that he did in that line and he replied, “well its what we do”, the defence rests!

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Defining Data Definitions and How to Write Them

Have you ever stopped to wonder what a "data definition" actually is? It's one of those terms that we often toss around in the world of Data Governance, but it's surprising that until now, no one has actually asked me to break it down. When I got an email with this query, I had a bit of an "Aha!" moment. I thought to myself, "Surely, I must have tackled this topic ages ago," but guess what? I hadn't!

Now, "data definitions" might sound a tad on the technical side, but they're an essential piece of the Data Governance puzzle. You might be wondering, "Why didn't this person just Google it?" Well, let me tell you, I did. I braved the labyrinth of Google search results, and honestly, I wouldn't recommend it. It spat out some super technical gibberish, like, "a data definition is the origin of a field that references a data domain and determines the data type and the format of data entry."

The reason these Google results—and others like them—are about as clear as mud is that they're designed to describe data definitions in the context of something called a "data dictionary." But data dictionaries are all about the nitty-gritty technical stuff—like where data lives in a database and its techy constraints. It's not exactly the thrilling stuff business users are itching to know.

So, when we talk about data definitions in the Data Governance realm, we're not diving headfirst into the deep end of tech talk. We're all about making data accessible to the people in your business who need to use it and gain insight from it. We're talking about the entries that populate your data glossary or data catalogue.

Ever notice that in organisations, people often throw around the same terms, but their interpretations can be like comparing apples and oranges? That's where data definitions come to the rescue. We're here to extract those varying interpretations, decide on one common definition, document them, and get everyone on the same page because when stakeholders aren't on the same wavelength, you end up with reports and decisions that are about as reliable as a chocolate teapot.

Now, some data terms are like old friends—easy to define, and everyone's on board. Think "date of birth," "first name," and "last name" in systems with personal info. You could define those in your sleep, and everyone in the organisation would nod in agreement. But then you dive into the murkier waters of terms like "customer…” that's when things can get a bit iffy. What does "customer" mean to you? And what about Bob from accounting? His definition might be worlds apart.

So, the name of the data definitions game is making sure your organisation understands its data inside and out. A big chunk of that process involves pulling those data definitions out of people's heads, getting them down on paper, and achieving a group thumbs-up on what these terms really mean.

Now, I know you might be thinking, "Crafting data definitions sounds like a colossal headache!" But trust me, it's not rocket science. When I talk about a data definition, I'm simply talking about a short, sweet phrase or a couple of sentences that lay out what an item is and what it's all about. No need to overcomplicate things.

Here's a trick I use with my clients: I ask myself, "Could someone who knows nothing about this organisation and its inner workings read this definition and get it?" If the answer is a resounding "yes," then you've nailed it.

Now, here are some practical tips for fantastic data definitions:

• Keep It Simple: Your definitions should be clear and straightforward. No need for data Shakespeare here—simplicity is your friend.

• Plain Language: Avoid techy talk and opt for plain language that even your grandma could understand.

• Stay Objective: Write definitions from a neutral standpoint. Ditch the department-specific biases.

• Team Effort: Get the relevant people in on the definition action. Consensus is the name of the game.

• Put It to the Test: Before you pop the champagne, test your definitions with non-experts. If they scratch their heads, it's back to the drawing board.

• Amp Up the Info: Consider adding extra tidbits like data lineage and usage to your definitions.

In a nutshell, data definitions are the unsung heroes of Data Governance. They bridge the gap between IT and a diverse range of stakeholders.

I hope that was helpful and don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Data Governance 2023 Round-Up

Welcome to 2024!

Happy New Year and welcome to 2024!

As we bid farewell to the past year and step into the new one, I want to extend my warmest wishes to all of my readers and network. May this year bring you success, joy, and countless opportunities for growth. 

The arrival of a new year symbolises a fresh start - a clean slate to set new goals and pursue new ambitions.

To kickstart your Data Governance journey in 2024, I've compiled a list of the top ten most popular blogs from 2023. These blogs cover a wide range of topics, providing insights, tips, and best practices to guide you through the intricacies of Data Governance. Whether you're a seasoned professional or just beginning to explore this field, there's something for everyone in this curated collection.

1. How to identify Data Owners, where multiple areas of the organisation use the same data?

2. How the COM-B Model for behaviour change can be used when implementing Data Governance

3. The 7 Potential Benefits of Having a Data Glossary or Data Catalogue

4. The First Six Months of Your New Data Governance Initiative

5. What is a Data Office?

6. What are the key components of a data culture?

7. Data Governance Interview with ChatGPT

8. How to Certify a Report through Data Governance: Importance and Best Practices

9. Data Management Disciplines - Separate Specialities or Better Together?

10. Who owns the Data in a Data Warehouse?

As we step into the promising realm of 2024, let's embrace the opportunities it brings for personal and professional growth. 

Here's to a year of data-driven success and innovation - Happy New Year!

If you would like support with your Data Governance initiative make sure to check out the training options I have coming up in 2024 or if you would like to discuss any of your Data Governance needs book a call. 

Data Governance Interview with Rini Choudhury

Rini Choudhury is the Data Governance Lead at Haleon, bringing with her over 8 years of experience in the field of Data Governance. I am thrilled to share her insightful interview with you.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

 I embarked on my career as a database developer with boundless enthusiasm, eager to delve into coding, create innovative applications, and master various programming languages. However, I soon recognized a critical gap in my approach – I was solely focused on solving immediate challenges without considering the broader context.

The recurring challenges of acquiring timely and reliable data became increasingly apparent. These data issues posed significant obstacles to our work, leading me to question the overall quality and effectiveness of our data-driven solutions. It was at this point that I realized the importance of understanding the bigger picture.

Motivated by the desire to address core data problems and their far-reaching consequences, I transitioned into a role where I could delve deeper into data-related issues. This journey led me to appreciate the critical need for data governance, a fundamental discipline for ensuring data's accuracy, accessibility, and usefulness within an organization.

What characteristics do you have that make you successful at Data Governance and why?

 Patience of a saint 😊, Persistence and ability to not get frustrated with zillion followups.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

Data Governance for Dummies

What is the biggest challenge you have ever faced in a Data Governance implementation?

Shifting people's perspectives and securing their commitment to a larger vision, particularly when immediate benefits aren't readily apparent, can be a formidable task. This challenge often arises in contexts like Data Governance initiatives, where the true impact may only become evident in the long run.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I have a strong desire to assist the healthcare industry in implementing effective Data Governance practices. This choice is driven by the profound impact that data can have on people's lives within this sector. Healthcare relies heavily on accurate and timely data for patient care, medical research, and public health initiatives.

By helping the healthcare industry establish robust Data Governance, we can ensure the quality, privacy, and security of patient data. This, in turn, can lead to improved patient outcomes, more efficient healthcare processes, and enhanced medical research efforts. Ultimately, it's a field where data governance can directly contribute to saving lives and improving the overall well-being of individuals, making it a particularly compelling and rewarding area to focus on.

What single piece of advice would you give someone just starting out in Data Governance?

Have patience, don’t give up and keep trying until you succeed. If you're facing challenges, remember, you're not alone. We've all encountered obstacles on our journey, so seek out supportive communities that can offer guidance, as they've likely experienced similar if not identical challenges.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

While the idea of ownership is often thrilling, becoming a data owner doesn't typically generate the same level of excitement. To illustrate, if I were to offer a car, complete with maintenance and upkeep responsibilities, many would eagerly embrace the opportunity to own it. However, when posed with the question of who would like to take ownership of a dataset, the enthusiasm tends to wane. This observation has taught me a valuable lesson: people are often uncertain about whether they should assume ownership of data.

Navigating Data Mesh and Evolving Data Governance: A Practical Guide

In my previous blog, I examined the concept of Data Mesh and its relationship with Data Governance. Now, let's take a closer look at practical insights and strategies for successfully navigating the complexities of Data Mesh while evolving our approach to Data Governance.

Strategies for Integrating Data Governance with Data Mesh

Imagine standing at the intersection of two parallel roads: Data Mesh and Data Governance. How do you ensure a smooth transition from one road to another without causing traffic chaos? The answer lies in a thoughtful strategy that integrates the principles of Data Governance with the unique demands of Data Mesh.

1. Embrace the Complexity: Data Mesh challenges the notion of a centralised data warehouse or lake. It advocates for a decentralised architecture where data products are accessible through APIs and various systems. This complexity demands an amend to your data governance approach so that it supports data democratisation while maintaining quality and consistency.

2. Define Clear Roles and Responsibilities: The emergence of data product owners and development teams introduces new roles to the Data Governance landscape. While traditional roles like data owners and data custodians remain vital, these new roles must align seamlessly to ensure effective governance across decentralised data resources.

3. Cultivate a Data-First Mindset: Data Mesh isn't just a technology trend; it's a cultural shift that requires everyone in the organisation to adopt a data-first mindset. Data Governance should promote collaboration between business units, data professionals, and IT teams to ensure that data products are valuable, understandable, and compliant with quality standards.

Balancing Complexity with Simplicity: Data Product Principles

The democratisation of data products within the context of Data Mesh poses both opportunities and challenges. How do you strike the right balance between making data accessible and ensuring its quality and usability? The answer lies in a set of core principles that define what constitutes a data product:

1. Accessibility: Data products should be available in various formats, catering to different user needs. This accessibility ensures that users across the organisation can easily access and utilise the data.

2. Understandability: Documentation and clear definitions are crucial. Users should be able to understand what each data product contains, how it can be used, and relevant examples that demonstrate its value.

3. Discoverability: Data products must be easily discoverable. Organisations need a data catalogue or glossary that enables users to locate and access relevant data products effortlessly. 

4. Interoperability: Data products should be designed to work well with other datasets, fostering a collaborative environment where various data products can be combined to generate valuable insights.

5. Trustworthiness: Data quality is paramount. Data products must adhere to defined data quality standards, ensuring that users can rely on the accuracy and integrity of the data they're accessing.

Evolving Data Governance in the Data Mesh Era

Data Governance isn't static; it's a living process that evolves to meet the demands of changing data landscapes. In the context of Data Mesh, this evolution takes on new dimensions:

1. Flexibility in Roles and Responsibilities: While the traditional Data Governance roles remain essential, the advent of data product owners and development teams introduces a layer of complexity. Organisations must be willing to iterate and adjust responsibilities to ensure effective governance and minimise conflicts.

2. Holistic Data Ownership: Data ownership gains even more significance in the Data Mesh paradigm. As data products span multiple domains and applications, having a holistic data owner is vital to ensure consistent decision-making and accountability.

3. Continuous Adaptation: Data Mesh isn't a one-size-fits-all solution. Expect the unexpected and be prepared to refine your data governance approach as you gain insights from real-world implementations. Flexibility and adaptability will be your allies.

Final Thoughts

Navigating the intersection of Data Mesh and Data Governance requires a delicate balance between complexity and simplicity. The democratisation of data through data products empowers organisations with valuable insights, but this must be paired with robust governance to ensure that data remains trustworthy and usable.

As the journey continues, keep in mind that Data Mesh isn't a static destination; it's a dynamic evolution that demands openness, collaboration, and the willingness to learn from successes and challenges alike. By embracing the principles of data product creation and adapting your data governance approach, you'll be better equipped to harness the potential of Data Mesh and drive meaningful business outcomes.

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Understanding the Basics of Data Mesh and its Impact on Data Governance

In the rapidly evolving landscape of data management, a term has emerged that is simultaneously intriguing and confusing: Data Mesh. If you find yourself puzzled by this concept and its implications for, you're not alone.

Many of us in the data realm have been grappling with the question: what exactly is a Data Mesh, and how does it impact our approach to Data Governance?

Imagine encountering a new client who casually drops the bombshell that they're embarking on a Data Mesh journey and expect you to oversee Data Governance for it. Panic might set in, as you realize that while you've heard of Data Mesh, you're not entirely certain how it impacts Data Governance.

In this blog, we'll dive into the basics of Data Mesh and explore its intersection with Data Governance.

Unravelling the Mystery of Data Mesh

Data Mesh isn't just another technological marvel, like the migration of data to the cloud that prompted a flurry of questions about Data Governance changes a few years ago. Data Mesh concept encompasses more than a fresh technology stack or a novel infrastructure. It's about a distributed architecture that breaks away from the traditional data warehouse or lake model. Instead, it envisions data as a decentralised resource, accessible through various APIs and systems.

The crux lies in the shift in mindset that Data Mesh demands. It's not just about IT delivering solutions; it's a cultural change that invites all stakeholders to think differently about data ownership and accessibility.

While previous data warehouses and lakes could operate without airtight Data Governance (albeit suboptimally), the same isn't true for Data Mesh. It hinges on a cultural revolution where data becomes the shared asset of the entire business, requiring robust governance to maintain its integrity and usability.

The Democratisation of Data: Introducing Data Products

At the heart of Data Mesh lies a fundamental shift in how we perceive data's value and accessibility. The term "democratisation of data" is more than a catchy phrase; it's a philosophy that shapes how we approach data products. Data products aren't massive data dumps; they're finely curated, bite-sized datasets that hold value on their own. These products are designed to be easily accessible and usable by a wide range of users across the organisation.

The concept of a data product may sound straightforward, but its implementation requires careful consideration. Not all data is meant to be a data product. The criteria for turning data into a data product hinge on its accessibility, understandability, discoverability, interoperability, and trustworthiness. By adhering to these principles, organisations can ensure that their data products are valuable, usable, and ultimately contribute to the democratisation of data.

Adapting Data Governance for Data Mesh

As we explore the intricacies of Data Mesh, the question of Data Governance looms larger. How does Data Governance need to evolve to accommodate this new paradigm? The first step is acknowledging that a one-size-fits-all Data Governance framework won't suffice. While a standardised framework can offer inspiration, each organisation's unique culture and challenges necessitate a tailored approach.

Roles and responsibilities play a pivotal role in Data Governance, and Data Mesh introduces some new players. The introduction of data product owners and data product development teams raises questions about the role of traditional data owners and data stewards. The evolution of Data Governance in the Data Mesh era involves reconciling these roles, ensuring that data ownership and stewardship align with the demands of democratised, decentralised data.

In conclusion, the confluence of Data Mesh and Data Governance represents a transformational shift in how we manage and utilise data. Data Mesh isn't just about technology; it's a cultural and architectural evolution that necessitates rethinking our Data Governance strategies. By embracing the democratisation of data and adapting our governance practices, we can navigate the complexities of Data Mesh and harness its potential for enhanced data usability and value.

Stay tuned for my next blog, where I'll delve deeper into the practical implications of Data Mesh on Data Governance and share valuable insights for successfully navigating this dynamic landscape.

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Data Governance Interview with Sophie Turner

Sophie is a Junior Data Governance Analyst at Penguin Random House UK. After graduating with a Law degree in 2018 she worked in Information Security Governance, Risk and Compliance before pivoting into Data Governance earlier this year. In this new position, Sophie is responsible for delivering Data Governance best practices across the business.

How long have you been working in Data Governance?

I’ve been in Data Governance since the beginning of June. Before this I worked in Information Security Governance and Awareness roles and I can already see how the same skillset is applicable in this new world of governance.

Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

I had been working at Penguin Random House for 2 years when the opportunity in the Data Governance team came up – I’d been completing an apprenticeship in data analytics which had sparked a new love for data, and was very lucky to have Rupal, the previous Head of Data Governance, as my mentor through the internal mentorship scheme. When the role came up it seemed like the perfect fit to blend my existing experience with my new interest in all things data.

What characteristics do you have that make you successful at Data Governance and why?

My most useful characteristic is my endless curiosity – one of my favourite parts of this role is the opportunity to speak to so many people across the business and learn about what they do. This helps me to understand their data and strategic priorities, as well as how they fit together with the help of effective Data Governance. People love to talk to you about what they do, especially if you’re willing to help them with it! The key is to make sure you’re then relating your Data Governance priorities to their passions and strategy.

It’s also great to be curious about the different learning opportunities a Data Governance career path brings. I enjoyed developing my change management skillset when I completed the APMG Change Management Practitioner qualification last year, and I’m now focused on the more technical aspects of data management.

Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

I had the opportunity to take part in Nicola’s training when I first started this new role, and it was a great way to immerse myself in the world of Data Governance.

What is the biggest challenge you have ever faced in a Data Governance implementation?

A challenging part of working in a governance role is the need to continually explain the benefits of what you do – we are often asking people to add more to their to do list, so they rightfully want to understand why! This is where effective communication and change management strategies work hand in hand with Data Governance initiatives. As you continue to deliver consistent messaging, you build a support network in the business who can then drive your message further and add their own success stories.

Is there a company or industry you would particularly like to help implement Data Governance for and why?

I love working in a creative industry – I have a particular interest in helping colleagues understand how we can bring data analysis to the creative process, and the importance of Data Governance to support this. 

What single piece of advice would you give someone just starting out in Data Governance?

My best advice would be to listen – to the colleagues in your team and your stakeholders in the business. Being a trusted ‘sounding board’ about their role, data issues or any day-to-day problems helps you piece together a cohesive view of your organisation and allows you to identify where Data Governance can aid in resolving genuine business issues. You’ll soon see that many people are facing the same problems, and it’s a great way to bring people together to work on your Data Governance initiatives.

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Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or, if you’d like to know more about how I can help you and your organisation

How to Certify a Report through Data Governance: Importance and Best Practices.

Trusted data is the cornerstone of effective decision-making. It underpins an organization's ability to operate efficiently, mitigate risks, comply with regulations, and gain a competitive edge. Without trustworthy data, decisions are made in a state of uncertainty, which can lead to suboptimal outcomes and missed opportunities.

However, as reports grow in both quantity and complexity, ensuring data governance becomes paramount. Certifying a report is a crucial step in affirming the accuracy, dependability, and security of the information it contains. This article delves into the process of report certification through data governance, emphasizing its role in preserving data integrity and enabling well-informed decision-making.

Table of Contents

• Introduction

• Understanding Data Governance

• The Significance of Certifying Reports

• Best Practices for Certifying Reports

  • Report Ownership and Stewardship

  • Clearly Defined Business Terms

  • Understanding Data Lineage and Provenance

  • Establishing Data Quality Standards

  • Ensuring Data Security Measures

• Regular Auditing and Compliance Checks

• Training and Educating Staff

• Tools and Technologies for Report Certification

• Conclusion

• FAQs

Introduction

As organizations rely heavily on data to drive business decisions, it is essential to ensure the quality, accuracy, and reliability of the data. Certifying reports for data governance provides a structured approach to validate data integrity and maintain a high level of trust in the information presented. In this article, we will explore the concept of data governance, discuss the importance of certifying reports, and outline best practices to follow for effective report certification.

Understanding Data Governance

Data governance is the system of decision rights and responsibilities for information-related processes that ensure that data is used in a way that supports the organization’s goals and objectives. It involves defining policies, processes, and procedures to ensure data quality and compliance. Effective data governance ensures that data is accurate, consistent, accessible, and understood throughout its lifecycle.

The Significance of Certifying Reports

Certifying reports is an integral part of data governance. When reports are certified, it means they have undergone a thorough review and validation process to ensure their accuracy and reliability. Certifying reports provides several benefits, including:

·       Data Integrity: Certifying reports helps maintain the integrity of data by ensuring that it is accurate, complete, and consistent. It helps identify and rectify any errors or discrepancies in the data.

·       Informed Decision-Making: Certified reports provide decision-makers with reliable and trustworthy information, enabling them to make well-informed decisions based on accurate insights.

·       Compliance and Regulatory Requirements: Certifying reports helps organizations comply with industry regulations and data protection laws. It ensures that data is handled  in accordance with legal requirements.

·       Increased Stakeholder Confidence: Certified reports instil confidence in stakeholders, including customers, partners, and investors. It demonstrates the organization’s commitment to data quality and governance.

Best Practices for Certifying Reports

To ensure effective report certification and data governance, organizations should follow these best practices:

Report Ownership and Stewardship

Effective data governance begins with clear ownership and stewardship of reports. Designate individuals or teams responsible for the accuracy and reliability of specific reports. These owners should ensure that reports are regularly reviewed and certified.

Clearly Defined Business Terms

Consistency in terminology is crucial for to ensure consistent reporting. Clearly define and document business terms used in reports to avoid confusion and misinterpretation. A shared business glossary can facilitate this process.

Understanding Data Lineage

Data lineage is a fundamental component of data management and governance that contributes significantly to the certification of reports. It ensures data transparency, compliance, quality, and effective communication, all of which are essential in delivering accurate and reliable reports to support informed decision-making within an organization.

Establishing Data Quality Standards

Define and establish clear data quality standards that align with business objectives. These standards should include guidelines for data accuracy, completeness, consistency, and timeliness to provide a level of confidence in the data sets feeding each report.

Regular Auditing and Compliance Checks

Conduct regular audits and compliance checks to ensure that data governance policies and procedures are being followed effectively. This helps identify areas for improvement and ensures ongoing adherence to data governance standards.

Training and Educating Staff

Provide comprehensive training and education to staff members on data governance principles, practices, and their role in delivering trusted analytics. This ensures that everyone understands their responsibilities and actively participates in the data governance process.

Tools and Technologies for Report Certification

Several tools and technologies can aid in certifying reports for data governance. These include:

·       Data Governance platform: A Data Governance Platform supports report certification by tracking data quality, tracking data lineage, automating workflows, facilitating collaboration, and providing the necessary tools and processes for maintaining the integrity and reliability of reports within an organization

·       Data Quality Management Systems: Software solutions that enable organizations to monitor, measure, and improve data quality across various systems and processes.

·       Data Validation Tools: Tools that automate the process of data validation, allowing organizations to quickly identify and rectify any data inconsistencies or errors.

·       Reporting and Analytics Platforms: Robust platforms that enable organizations to generate certified reports with built-in data governance features.

Conclusion

Certifying reports for data governance is crucial for maintaining data integrity, supporting informed decision-making, and complying with regulatory requirements. By following best practices, organizations can establish effective data governance frameworks, implement robust validation processes, and ensure the security and reliability of their data. Investing in tools and technologies designed for report certification further enhances data governance capabilities, streamlines the certification process, and facilitates efficient data management.

FAQs

What is the role of data governance in report certification?

Data governance ensures that reports undergo a thorough review and validation process, guaranteeing their accuracy, reliability, and compliance with data governance standards.

How does report certification benefit organizations?

Report certification enhances data integrity, supports informed decision-making, ensures regulatory compliance, and builds stakeholder confidence in an organization’s data quality and governance practices.

Are there any specific tools available for report certification?

Yes, several tools and technologies, such as data governance platforms, data quality management systems, data validation tools, data lineage solutions, and reporting platforms, aid in certifying reports for data governance.

How often should organizations perform audits and compliance checks?

Organizations should conduct regular audits and compliance checks to ensure ongoing adherence to data governance policies and identify areas for improvement.

How can organizations ensure staff members are knowledgeable about data governance?

By providing comprehensive training and education programs, organizations can ensure that staff members understand data governance principles, practices, and their role in maintaining data quality.

Gary Allemann has over twenty years’ experience in the delivery of data quality, data governance and master data management solutions, primarily in Africa. Follow Master Data Management on LinkedIn or subscribe to the Data Quality Matters blog

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I’m thrilled to invite Gary Allemann back to write another guest blog on an important topic. Keep your eyes peeled!

Don't forget if you have any questions you’d like covered in future videos or blogs please email me - questions@nicolaaskham.com.

Or, if you’d like to know more about how I can help you and your organisation