How Often Should You Revisit Your Data Governance Maturity Assessment?

One of my clients recently asked how often they should be assessing their Data Governance maturity. Now, this is a good question because I think so many people underestimate the speed at which they're able to implement Data Governance and, as such, a Data Governance maturity assessment is a great tool for seeing what progress has actually been made and what needs to be focused on next. With that in mind, let's explore Data Governance maturity assessments in more detail.

Understanding Data Governance Maturity Assessments

Data Governance maturity assessments provide a structured framework for understanding current strengths, identifying gaps and highlighting areas for improvement in an organisation's Data Governance. But, the timing of carrying out these assessments is important because, as I mentioned above, Data Governance does take longer than you think, so you don't want to overestimate the amount of assessments you need. You want to find a balance between making improvements and not overwhelming the team or resources.

Recommended Frequency of Assessments

Based on my experience, assessing your Data Governance maturity should be done no more often than once a year. The rationale behind this is that, realistically, organisations can only make so many changes within a short space of time. If you carry out a reassessment too soon not enough will have changed within the time frame to justify the effort of a full reassessment. And you don't want to bug people, who are already very busy, by asking for their time too often.

A yearly assessment would be the best option for most organisations, depending on the pace of change and the maturity of their Data Governance program. The key is to match how often maturity is assessed with the organisation's ability to make the changes between each reassessment. I think the best way to do this is to have a look and understand what's been moving on in your organisation and whether it's worth reassessing at this point.

Beyond Maturity Assessments: Communication and Culture

These assessments are also really good at telling an organisation's progress in creating a data culture and effective communication. When you're looking at the results of a Data Governance maturity assessment, don't take every result to mean that you're not doing certain things - it might be your communication at fault rather than the fact that you haven't done something!

I can recall times during my early career in Data Governance when I'd got results back from a maturity assessment and been devastated because it stated that we hadn't done something that we had actually worked really hard on doing! I remember thinking, ‘We've done that. Why are they saying there are no data owners in this area? There clearly are!’

And then when I actually thought about it, I realised that yes, we'd done the work as a Data Governance team but what we hadn't done was communicate it to the wider audience. And the problem with this is that Data Governance doesn't work unless everybody's on board. You need to make a culture change and for that, you need to communicate. If people don’t know what you’ve achieved it’s as though it hasn’t happened for them!

Conclusion

Data Governance maturity assessments are brilliant tools for guiding and measuring the progress of an organisation's Data Governance efforts. However, they are most valuable when done at a pace that aligns with the organisation's ability to make change. Whether done every six months or annually, the focus should always be on actionable steps and creating a culture that values data as a business asset.

As always, if you have any questions or need further support with optimising your Data Governance initiatives, feel free to book a call with me using the button below.

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 blog, 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. 

Why is Data Governance Training so Expensive?

Data Governance is a critical function for organisations looking to manage their data effectively and ensure compliance with a growing array of regulations. However, many organisations and individuals are often taken aback by the high costs associated with Data Governance training. 

So, I thought I’d take a deeper look at why Data Governance training is so expensive.

Expertise and specialised knowledge

Data Governance is a highly specialised field that requires a deep understanding of industry best practices. Additionally, there’s a LOT of information out there - all you need to do is type ‘Data Governance’ into Google and you will be overwhelmed by thousands of results it yields, many of which are ambiguous and in some cases even incorrect.

Because of the above, organisations need to rely on trainers who are seasoned professionals, who bring years of experience and extensive expertise to the table. Organisations are typically paying for the depth of knowledge and practical insights that these experts provide, which are essential for building a robust Data Governance framework.

Comprehensive and updated curriculum

The curriculum for Data Governance training is extensive, covering a wide array of topics. Creating and maintaining such a comprehensive curriculum requires significant investment. 

Moreover, the field of Data Governance is continually evolving, with new regulations, technologies, and best practices emerging regularly. Training providers must constantly update their materials to reflect these changes, ensuring that participants receive the most current and relevant information. 

Customised training solutions

Data Governance training might need to be tailored to the specific needs of an organisation. Customisation ensures that the training addresses the unique data challenges and regulatory requirements that the organisation faces. Developing customised training solutions involves additional effort and resources from the trainers, including time spent on research and preparation, which can contribute to the overall cost.

Expert networking opportunities

Data Governance training isn’t just about learning from instructors - it’s also about connecting with fellow data enthusiasts. Networking with industry experts and peers can provide invaluable insights, support, and collaboration opportunities. However, facilitating these networking opportunities requires additional time and resources, contributing to the overall cost of the training.

Return on investment

Despite the high initial cost, Data Governance training offers a substantial return on investment. Properly trained personnel can help an organisation, reduce inefficiencies, comply with regulatory requirements, improve data quality, improve decision-making and provide a solid foundation from which to embrace emerging technologies such as Gen AI, which requires good data. It will also help create a data culture at the organisation (click here to read my ‘What is the impact of a poor data culture’ blog).

These benefits far outweigh the costs, making the investment in training a strategic decision that pays off in the long run.

Value of training

Training extends far beyond merely mastering Data Governance. Quality training holds the potential to generate a broad and positive impact on your organisation.

Infopro Learning suggests that the success of an organisation is significantly influenced by its capacity to provide successful training. With the right training, organisations can ensure their employees are well-equipped to perform their jobs effectively (Infopro Learning, 2023). 

Additionally, recent research from Continu in 2024 shows that training doesn't just help employees perform better; it also strengthens the workforce, aligns everyone with company goals, encourages knowledge sharing and innovation, boosts brand reputation and keeps top talent around. (Continu, 2024)

In short, investing in good training isn't just about teaching skills - it's about building a stronger, more successful organisation overall.

If you’d like to know more about how I can help you and your organisation be successful with Data Governance then please book a call using the button below.

What is Data Lineage?

In this post I want to talk about something that sounds a bit daunting but is actually super helpful when it comes to Data Governance, and that is Data Lineage. 

What is Data Lineage? 

In its simplest form, Data Lineage can be thought of as a diagram that shows you how data flows through an organisation from the first point that it comes in at.

For example, imagine a customer placing an order on a website. That's where the data journey begins. Then it might travel through various systems like order processing and inventory management, before landing in an organisation’s data warehouse for reporting. 

Now, that is a very straightforward example and of course things can get more complex than that, but the purpose of Data Lineage remains the same - to show what systems and processes your data goes through no matter how simple or complex. 

The benefits and challenges of Data Lineage

Sometimes data takes unexpected routes when it is being moved from system to system, which can lead to hiccups. That's where Data Lineage comes in handy. It can help you spot potential issues and understand how your data is flowing.

Nevertheless, creating Data Lineage diagrams can be challenging at times. There are tools made specifically to help with these challenges. Automated tools can scan your databases and do Data Lineage for you. The problem with this is that they often churn out tons of detailed diagrams that can be overwhelming if this level of detail is not needed. 

My advice? Keep it simple.

Start by focusing on the most important data for your organisation and work backwards. Ask those who use that data where they get it from, then follow the breadcrumbs all the way back. I say this because it's really hard to work forwards when you're trying to create a Data Lineage if it's never been documented before. 

Another thing I'd recommend if you're perhaps not sure where your data starts is to talk to some experienced long standing business analysts in your organisation. They probably have some good ideas about where data is flowing through. 

So, there you have it. Data Lineage isn't scary - it's actually fairly simple to create high level Data Lineage diagrams when you break it all down first.

Prefer this content in video form? Click here to watch the video.

If you found this helpful and would like to know more about Data Governance, feel free to book a call with me.

How long does it take to implement a Data Governance framework?

A question I get asked frequently is about the duration of time and the complexities involved in implementing Data Governance - especially if it’s someone’s first time implementing Data Governance.

First things first - Data Governance is not just an agenda that can be crossed off once implemented. 

Data Governance is a long-term ongoing practice that constructs a healthier data environment in an organisation to support its goals. Data Governance extends beyond a single activity or project. 

With that in mind, let's address the question of duration.

Does Size Matter? 

Undoubtedly, the size of an organisation, the number of systems in place, and the complexity of the organisation and its systems will impact the timeline for implementing Data Governance. 

I always recommend a phased approach, this allows prioritisation and the gradual application of Data Governance principles across your organisation.

The Art of the Rollout

Many people think that the implementation of Data Governance can be approached in one of two ways.  That it can be introduced all at once - like a seismic shift.   Or , alternatively, you might decide to tackle it by business function, data domain, or a combination of both, depending on where the greatest need for Data Governance lies within your organisation at the time.  I’m a fan of the latter approach - having next seen it be successful when the first approach is attempted!

What is the Time Commitment?

Designing your Data Governance initiative and beginning to realise any benefits can typically take between a year to 18 months. However, this should be considered a ballpark estimate - as your organisation's complexity and size are variables that could extend this timeframe. For large multifacted organisations achieving comprehensive Data Governance could span several years.

Reflections

Embarking on the Data Governance journey requires thorough planning and a measured, phased execution. The size and complexity of your organisation will influence the timeframe. That being said, don't let this discourage you! Data Governance is an ongoing practice, and keeping the wheels in motion is critical in ensuring your organisation's approach to data remains adaptable and robust.

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

Data Governance Interview with Danielle Titheridge

I met Danielle when she attended my training course and loved her enthusiasm, so I thought she would be a wonderful person to interview for this blog. Danielle describes herself as a ‘people’ person, sociable and likes to find solutions to problems.

She’s from Dorset and enjoys long coastal walks with her Hungarian Vizsla and likes spending time with family and friends too.

Like many of us, Danielle has had a number of different careers from a primary school teacher to working in HR and now Data Governance.

How long have you been working in Data Governance?

I have been working in Data Governance for approximately 6 months as a Data Governance Analyst. It is quite different to my HR Advisor role but there are similarities with working with people and being another support function for the organisation.  

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

I was working at the RNLI in the People department and then I saw an advert for a Data Governance Analyst and wondered what it was. It sounded really technical but after speaking with the Data Governance Team I learnt that a lot of it was about managing change and stakeholders and not having to be too technical at all. I applied for the job and was fortunate to be offered the role.  

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

People skills! I was put off originally thinking it was a really technical role (because I am not technical), however you don’t need to be really technical to be good at data governance. Don’t get me wrong, you need to understand some areas so you can understand how it can benefit the organisation but a lot of what we are focusing on is change management and increasing people’s confidence. We want them to have an understanding of what data governance is and how it can benefit them, their teams and the organisation as a whole. Once people understand the ‘why’ then they are more likely to engage and want to make positive change and efficiencies.

You do also need to have a lot of resilience at times and be able to adapt.   

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

Your website and blogs I found really helpful as well as your training course. It really cemented ideas in my head and really started to eliminate the imposter syndrome so I honestly can’t recommend it enough.

Also my wonderful colleagues in my immediate team but also our wider team to take the time to explain to me what we do and how it fits into the bigger picture and organisational strategy.

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

People not wanting to take accountability, specifically relating to data roles and the importance around this.

The ‘Why’- people still don’t really understand it. I think because it links with data protection and information security, people struggle to know the difference.

People see it as us asking them to do something else in their already busy day but the ironic thing is once they have it will provide them with more time as it creates efficiencies – work smarter not harder!

A recent project we have been working on is around classifications (sensitivity labels) and from a technical, system perspective there are a few difficulties but the main thing is the engagement and culture of people. So we have definitely had to redirect our focus and next steps and timing is definitely a crucial factor we have had to consider too.  

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

As I am working for a charity now I would be interested to see what it would be like to work for a private organisation one day and the difference in tools, culture and behaviour towards data governance.  

Finally, what single piece of advice would you give someone just starting out in Data Governance?

You don’t need to be an expert at it all and as we know technology changes, has updates etc so you will never 100% be up to date so learn to be ok with that… I am still learning to be ok with it.

It’s all about story telling and change management - so show people in a way they can relate to, pull on heart strings and then they will see the benefit.

What’s the Difference between a Data Governance Clinic and a Data Governance Mastermind?

With over 20 years of experience in Data Governance, my focus has been on sharing knowledge through coaching, consulting, and delivering courses and workshops. Two of the most popular programmes I offer are the Live Online Data Governance Training and Clinic and the 1 Day Data Governance Mastermind. 

Both these sessions are designed to help elevate your Data Governance capabilities, each catering to different needs and learning styles. I often receive questions about the differences between the clinic and the mastermind, as both involve collaborative learning. In today’s blog, I’ll be exploring the differences and sharing some tips on how to know which programme is right for you.

What is the Data Governance Clinic?

The Data Governance clinic is part of my Live Online Data Governance Training and Clinic. This programme is spread over two days - the first day is my Getting Started in Data Governance training course and the second day is the clinic. You can either sign up for both days or you can attend either just the training or just the clinic (if you understand the theory of Data Governance but are finding it challenging doing it in practice).

The Data Governance Clinic is a workshop format where you get the opportunity to ask questions to better understand Data Governance concepts or to work out how to make Data Governance work in your organisation.

The day is all about turning theory into practical actions and you will get the opportunity to ask detailed questions about implementing Data Governance in your organisation and receive advice on how to overcome the challenges you may be facing.

There are always lots of questions to be covered so all questions are collated and prioritised by the attendees so that we focus on the issues that matter most to them.

Key Features of the Clinic:

• Workshop Format: This hands-on session allows you to discuss your specific Data Governance challenges and receive practical advice.

• Interactive Learning: Collaborate with peers facing similar issues, share insights, and network.

• Practical Application: Turn theory into actionable strategies with focused guidance on implementation within your organisation.

• Expert Facilitation: Benefit from my 20+ years of experience in Data Governance, ensuring in-depth insights and personalised advice.

This structured approach allows participants to gain actionable insights and build skills crucial for effective Data Governance practices in their respective organisations.

What is the Mastermind?

In contrast, the 1 Day Data Governance Mastermind is all about collaborative learning, collective problem-solving, and peer support among data professionals. 

Participants come together to share experiences, discuss challenges, and brainstorm solutions related to implementing and maintaining effective data governance practices within their organisations. My 1 Day Data Governance Mastermind is run once a quarter and attendees can come along to just one session or as many as they like. 

Everyone at the Mastermind gets time in the hot seat where they get to share their current challenges or questions and everyone on the mastermind (including me) share advice and guidance to help resolve the problem or suggest a way forward.

Key Characteristics of the Mastermind:

• Peer-to-Peer Learning: Members contribute diverse perspectives and insights from their experiences in Data Governance, fostering a rich exchange of ideas and strategies.

• Accountability and Support: The mastermind provides a supportive environment where members hold each other accountable for implementing Data Governance best practices and achieving organisational goals.

• Holistic Approach: Discussions encompass various aspects of Data Governance. 

• Long-term Growth: Masterminds focus on continuous improvement and adaptation to evolving Data Governance challenges, promoting ongoing professional development and skill enhancement.

Choosing Between the Clinic and the Mastermind for Data Governance

Data professionals aiming to implement or enhance Data Governance practices should consider their specific learning preferences and objectives:

• Choose the training and clinic If… 

• You require structured instruction and guidance. 

• You are focusing on mastering specific aspects or techniques within Data Governance implementation.

• You prefer a curriculum-driven approach to learning about Data Governance frameworks and practices.

• Choose a Mastermind If… 

• You value peer support, collective problem-solving, and collaborative learning experiences.

• You seek accountability and motivation from a group of peers to implement and sustain data governance initiatives.

• You want to engage in discussions that encompass broader aspects of Data Governance strategy and implementation.

Both the clinic and the mastermind offer valuable opportunities for data professionals to deepen their understanding and implementation of Data Governance. By choosing the right forum, data professionals can effectively navigate the complexities of Data Governance, contribute to organisational success, and drive data-driven decision-making. 

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If you’re still not sure I've designed a short quiz that evaluates your current expertise, goals, and learning preferences to help determine the most beneficial path for you.

Data Quality: The Secret Sauce for AI and Generative AI Success

I’m so please to introduce this guest blog written by the fantastic Tejasvi Addagada. Tejasvi is a seasoned business strategist, data and software architect with an impressive track record. In this blog, Tejasvi delves into the critical importance of data quality in training Large Language Models (LLMs) and the profound impact it has on the accuracy and reliability of AI-generated predictions and decisions.

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We often marvel at the sheer scale of Large Language Models (LLMs). These behemoths owe their ‘largeness’ to the vast volumes of data they are trained on, collected from a myriad of sources. The lifeblood of these models is the quality of this big data. It’s through this data that the models learn the intricate dance of language patterns, enabling them to generate coherent and contextually accurate responses.

However, like a grain of sand in a well-oiled machine, inadequacies in data quality can introduce noise into the model training process. This noise can lead to spurious outcomes, much like a radio catching static between stations. This noise significantly impedes the model’s ability to generate the correct embeddings - the mathematical representations of words in high-dimensional space. This, in turn, affects the model’s capacity to comprehend and generate accurate and meaningful context. In essence, while the size of LLMs is impressive, it’s the quality of the data they’re trained on that truly determines their effectiveness. It’s a reminder that in the realm of AI, quality often trumps quantity.

Considering the impact of data quality on AI outcomes, how might erroneous training data lead to unreliable predictions, and what steps can be taken to ensure the integrity of AI-generated results?

As a data executive, I’ve often found myself fascinated by the intricacies of artificial intelligence and the relation with quality of data. However, it’s important to remember that AI, like any tool, is only as good as the data it’s trained on.

Consider this: Inaccurate Predictions: If an AI model is trained on data that’s full of errors or inaccuracies, it’s like trying to navigate a maze while blindfolded. The model may stumble and falter, leading to predictions that are unreliable or downright incorrect. It underscores the importance of using accurate, high-quality data when training these models.

Then there’s the Ripple Effect of Biased Outputs: Imagine feeding an AI model data that’s skewed or biased. The model, in turn, might churn out results that perpetuate these biases, leading to outcomes that are unfair or skewed. It’s a stark reminder of why we need to use unbiased data when training AI models.

And what about Non-usable Content? If the data fed into the model is incomplete or inconsistent, it can leave the model confused. The result? Outputs that are gibberish or make little to no sense.

Lastly, let’s not forget the potential for Misleading Information: If the AI is trained on erroneous data records, it could end up generating information that’s misleading. This could be harmful, especially if such information is used for decision-making.

In conclusion, the quality and integrity of the data used in AI training are paramount. It’s a topic that deserves our attention as we continue to explore the vast potential of artificial intelligence.

How can poor data quality impact customer satisfaction and loyalty?

In organizations, we often discuss the marvels of artificial intelligence and data-driven decision making. However, an often overlooked aspect is the quality of data that fuels these systems.

The Cost of Poor Data Quality: Imagine a scenario where the quality of data is compromised. This could lead to inaccurate predictions and decisions, which in turn could result in significant financial losses. What is the confidence that an organization can have on it’s financial statement, regulatory returns or key-strategic decisions that it takes. All such aspects are assumed to be 100% accurate basis the quality of data that fuels them. It’s akin to building a house on a foundation - the structure is bound to be supported if it’s qualitative.

The Role of Data Quality in Generative AI: Generative AI, a branch of artificial intelligence that excels at creating new data from existing datasets, relies heavily on the quality of the input data that is used for training as well as fine-tuning using techniques like re-inforced learning. The better the data, the more accurate the insights it can generate.

The Data Scientist’s Dilemma: According to data researchers, data scientists spend a whopping 80% of their time just preparing and organizing data. This underscores the importance and the challenge of maintaining high-quality data.

The Impact on Customer Satisfaction and Loyalty: Poor data quality can also have a ripple effect on customer satisfaction. Inaccurate predictions can lead to wrong decisions, which can leave customers dissatisfied with the product or service they receive. This could, in turn, decrease customer loyalty.

The Solution: Systematic quality control and verification of data can help mitigate these issues. It’s like having a robust quality check in a production line, ensuring that the final product meets the desired standards.

In conclusion, the quality of data is not just a technical issue, but a business imperative that can impact financial outcomes, customer satisfaction, and loyalty. As we continue to navigate the data-driven landscape, let’s remember - quality matters.

Why is data quality crucial for accurate predictions and decisions in both traditional analytics and Generative AI?

Some use cases for AI and generative AI include natural language processing, image recognition, and automated generation of content. Generative AI can also be used to automate the process of data analysis, allowing for faster and more accurate results. Generative AI has a wide range of applications in a variety of industries.

Financial Document Search and Synthesis:  Generative AI can assist banks in finding and summarizing internal documents such as contracts, policies, credit memos, underwriting documents, trading agreements, lending terms, claims, and regulatory filings. It can quickly summarize complex documents like mortgage-backed securities contracts.

Personalized Financial Recommendations: AI can provide personalized financial advice by analyzing customer data, investment portfolios, risk profiles, and market trends to generate tailored investment recommendations. This can help clients make informed decisions about asset allocation, risk management, and financial planning.

Enhanced Virtual Assistants: Generative AI-powered virtual assistants can automate tasks, handle customer inquiries, and provide real-time support. This frees up human agents to focus on more complex tasks, improving customer service efficiency.

Which dimensions of data quality are important for AI and Generative AI?

The dimensions of quality that a data office has to prioritize for data collection are as follows:

Ø  Accuracy - The term “accuracy” refers to the degree to which information correctly reflects an event, location, person, or other entity. How well does data reflect reality, like a phone number from a customer?

Ø  Completeness - Data is considered “complete” when it fulfills expectations of comprehensiveness. Is there complete data available to process for a specific purpose, like “housing expense” to provide a loan?

n  Column completeness – Is the complete “phone number” available?

n  Group completeness – Are all attributes of “address” available? Is there complete fill rate in storage to process all customers?

Ø  Validity: The “Validity” dimension of data quality refers to the extent to which data conforms to a specific format or follows predefined business rules. For instance, many systems require you to enter your birthday in a specific format, and if you don’t, it’s considered invalid.

The use of Artificial Intelligence is increasing to generate insights that advance customer journeys. Use cases like credit decisions, personalization, and customer experience are increasingly using AI. The quality of data across the diverse collection of data-sets must be assured to reduce the vulnerability of data-driven models.

Is there a direct implication of less quality data on outcomes of AI models?

Data quality significantly dictates the efficacy of machine learning models. The creation of accurate AI models hinges on the availability of high-quality data, which requires stringent quality control and verification measures. The influence of qualitative training and testing data can be particularly emphasized. As accurate training can result in accurate outcomes when the model is implemented. The importance of automated data quality assessments for AI has been underscored, with a variety of data-oriented techniques and tools being recommended to facilitate this process.

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Tejasvi Addagada is a seasoned business strategist, data and software architect with an impressive track record. He has held prestigious positions such as Head of Data & Analytics, Chief Data Officer (CDO), and Privacy Officer in global organizations. Tejasvi is also a bestselling author, having written two books on data management and risk. His expertise extends to assisting over fifteen organizations in developing winning business models. He provides contingency-based strategies, culture-oriented operating models, and customized organizational structures, all while leveraging cutting-edge technology engineering.