Having bypassed People and Process, the team is stuck with an inexperienced driver and, as if this were not bad enough, an inexperienced pit crew. There is no clear direction on what the plan is for the race or pit stops, and this impacts team morale as they are no longer striving towards the same plan or vision for winning the race. 

It seems so obvious in this example – you would never buy or heavily invest in the best Formula 1 car in the world before you have the correct people to drive and maintain it, just like you’d never start a race without a strategy in place to ensure the team is aligned and the vision is set.  

Many of you will be familiar with the above “PPT” approach, and using this Formula 1 analogy, you can see why it is crucial to get people involved early on and the reasons why in this well-known methodology, but what does this mean in terms of Data Governance? 

When starting a Data Governance journey, you can get your business on board and get quick buy-in by starting with the people. Doing it this way can avoid wasted tech spend, identify value, adapt to the business’s culture, and get more people managing your data at minimal extra cost. 

When we focus on data, our first instincts tend to draw us to think about technology because this often seems like the easiest solution, and in a world where technology seems to run everything, why not expect it to be that silver bullet to solve all our data governance needs too? 

However, This focus on tech masks the true root causes of our issues, which often lie in our people, processes, and culture. 

For those who do not know, the PPT framework has existed since the early 1960s, building on the idea that people are a fundamental, foundational, and critical part of an effective management approach in business. 

Our take on PPT in Data Governance? – add one more element after People… Culture. 

“People, Culture, Process, Technology”  

Data Governance

By adding “Culture” to the widely known PPT methodology, this framework now encompasses the basics for any successful Data Governance initiative. 

What is Data Culture?  

This topic can often be complicated, but how do you start to create a culture that values data?

1. First, you need to set the expectation that all decisions should be underpinned by insight and facts drawn from data.
2. Secondly, perceptions surrounding data need to be reshaped. Data is not just for reporting; it is a strategic tool that can reduce costs and drive revenue when utilised.

We like to think of data culture as the collective behaviours and beliefs of employees who share a mutual understanding of enterprise data and use data in their roles to help make better decisions and improve operations. Having a data culture promotes an inclusive and empowering community approach to use and understand the value of data within a business.

Effective data governance requires the active participation and collaboration of people from different departments, levels, and functions within an organisation. It involves defining policies, processes, and procedures to manage data quality, security compliance, and privacy. However, these policies and processes are created and executed by people who also use, analyse, and provide feedback on the data. Without people’s engagement and buy-in, data governance efforts can and do, fail, or remain incomplete, leaving gaps in data management and increasing the risk of errors, inefficiencies, and the loss of trust in data. Involving People in data governance initiatives, including training, communication, and incentives, is critical to achieving a sustainable, impactful data governance outcome. 

Here are just a couple of reasons why people are crucial to data governance: 

People create and use data while setting the data culture within an organisation. 

Think of Data Owners and Data Stewards as the champions of Data Governance within an organisation. They are the “knowledge keepers,” the “Quality Inspectors,” the “approachable gatekeepers,” and the “Change agents.” All these roles will be critical to setting the data culture within an organisation. 

Data is generated, stored, and processed by people and used to support decision-making by people – in other words, without people, you do not have a business in the first place. People have the most direct impact on data quality, consistency, and accuracy. Through training, education, and communication, people will be empowered to follow data governance policies, appropriately use data, and contribute to quality improvement initiatives. 

People provide feedback on data quality. 

Data governance relies on people’s feedback to identify data quality issues, including errors, inconsistencies, or missing data. Data users can provide input on the data’s relevance, accuracy, and completeness, which helps data stewards and owners improve and maintain data quality standards. This list includes security and privacy protocols, legal and regulatory compliance, and alignment of data management practices with business goals. 

At Oakland, we pride ourselves on doing the right things in the right way.  

We don’t offer a one size fits all Data Governance solution, or a technology focussed solution, because we have seen all too many times DG tools so complicated they sit unused and unloved. Data Governance is not an off-the-shelf package. It requires engagement with people at all levels to understand the business,  industry, and the problems and pain points being faced. 

We can design a bespoke approach with people at its heart with this understanding. 

People, Culture, Process, Technology. 

So as we’ve seen, People are first for a reason; to embed culture, we need our people to adopt and drive the initiative forward. Get this right, and we have the right foundations to deliver a successful Data Governance initiative. 

Mat Wilde is a Senior Data Governance Consultant at Oakland

The post People, The missing element in Data Governance appeared first on Oakland.

But how much should you invest in data and data quality? This question is easy to answer: Don’t bother investing in your data if you’re not willing to invest in its quality.

This may seem like a hard-line position, but unless you recognise that data quality improvement isn’t, unfortunately, a one-time activity but a continuous process that requires ongoing effort and commitment. As attractive as a big bang–silver bullet program may seem, this is doomed to failure and repercussions in the board room.

But deciding how much you should invest in data quality though can sometimes feel more like an art than a science. But what are the costs of getting it wrong?

The UK Governments COVID-19 test and trace system is an interesting use case. Designed to identify and track people who had come into contact with COVID-19-positive cases, it suffered a technical glitch, resulting in 16,000 positive COVID-19 cases being omitted from the official reporting.

What caused this monumental failure? An Excel spreadsheet reached its maximum file size, which caused data truncation and resulted in the loss of thousands of COVID-19 test results. As a result, individuals who had tested positive for the virus were not notified, and their contacts were not traced or advised to self-isolate undermining the effectiveness of the whole test and trace system leading to a huge loss in public confidence.

But poor data quality isn’t just limited to the public sector. In the infamous case of Knight Capital Group, which experienced a loss so large, the company had to be acquired by another financial firm to avoid bankruptcy after a software upgrade no longer in use was reactivated and started sending a flood of erroneous buy and sell orders to the market.

The data quality issue stemmed from the failure to adequately test and validate the software changes before deploying them to production, costing the company approximately $440 million dollars.

In both use cases, we see the potential risks of poor data quality.

But how do we improve data quality?

Make people care!

Before any progress can be made in improving data quality you have to make business leaders care about the issue. You may want to share the sobering outcomes of the 2 use cases above, which may help you illustrate the point!

The trick is to find the areas in which your leaders have a personal interest and show how poor data quality is detrimental to the business.

The first step is to expose the pain caused by poor data by identifying key business outcomes and priorities and showing how trusted high-quality data is critical to business success and can give you a competitive advantage. Find out the impact of poor data quality and connect its impact with your data and analytics initiatives. Look at historic risks and any internal audit reports highlighting the impact that your poor-quality data has or could have on your business. But don’t forget to validate your problem statements with your key stakeholders to make sure you use the right language and priorities. This is about winning hearts and minds without being the zealot in the room spreading misery.

Show & Tell (show the impact your poor quality data is having on the business)

Being terribly British and a nation of armchair activists, we all know when something is going wrong but doing something about it is different. We work in data, so use that data to demonstrate how your business is suffering and what your bad data is costing the business today. Identify those critical business processes and their owners and determine key indicators (KIs) that are the most impactful to those processes.

Connecting the impact your poor-quality data is having on your data and analytics initiatives can be a huge help. We increasingly see at board level senior management increasingly frustrated that their investment in data hasn’t reaped the rewards first promised. Use your data quality profiling to analyse critical data elements and their impacts on business performance. This way, you can provide concrete evidence of the impact of poor data quality on your business. If you can’t get the basics sorted, how can you hope to move on to the ‘sexy’ Artificial Intelligence and Machine Learning?

Identify the root causes of your problems and show how you can fix it

As we’ve highlighted, data quality isn’t a one-hit wonder but a process of continuous improvement requiring you to get to the heart of the problem. Don’t attribute all of your issues to a single area such as technology or data but be objective in scoping out the underlying causes in an objective way. By understanding the root causes and systematically sorting your solution scope based on your business priorities, you can identify the practical scope for improvement.

At Oakland, we’re experts in improving data quality and management and have many war stories on the value of data quality applied in business. For example, good process management in shipping data means less shrinkage of profits through over- or under-shipping.

Great data quality in warehouse management has led to operational efficiencies that reshape balance sheets and business models. Accurate and trusted customer data has enabled delightful experiences across retailers, infrastructure companies, and manufacturers.

Likewise, poor data quality defeats company efforts and delivers slim profit margins and gains in production, poor advertisement targeting, and poor customer service.

In short, quantifying the value of data quality is not an art, it is a part of each decision about how and how much business you will do.

If the data is poor quality, then anything that relies on it will likely be poor quality as well, if data quality is effectively managed, it will add significant value to products, services, and your company.

Please reach out to The Oakland Group for a data quality assessment to kick start your data quality improvements and so add value to your products and bottom line.

The post What are the costs of poor quality data? appeared first on Oakland.

They talk about the benefits of having access to a team of experts, the value of a data governance maturity assessment, and the importance of culture in establishing effective data governance.

So Terri, what always goes through my mind is why would you choose to work with a data consultancy versus doing the work in-house?

Great question, Andrew, so the biggest advantage of working with a data consultancy like Oakland is the wealth of experience you gain. When working in-house, you may only have a small team, or I’ve worked in organisations where I have been the only data governance person relied upon to establish governance, making things challenging, to say the least. On the other hand, bringing in a consultancy means you have access to an entire team’s expertise, gained from many different projects, which means you’re not just bringing in one person, but a whole team of knowledgeable professionals. For instance, bringing in an expert in data architecture can be hugely beneficial, when looking at how you’d like to use your data to drive data analytics and machine learning further down the line. A consultancy can share use cases and client testimonials to demonstrate where they have helped deliver effective data governance for organisations that look a lot like yours.

But it’s not just about the quantity of experience, it’s also about the fresh perspective a consultancy can bring. When you’re working in a role, it’s easy to become blinkered and miss the bigger picture. By bringing in a consultancy, you get a new perspective on the organisation as a whole and can identify gaps and areas for improvement that might otherwise have gone unnoticed.

I’ve also found that a consultancy team can engage with the rest of the organisation differently than an in-house team might. They aren’t bogged down with day-to-day operations, so they can focus on the bigger picture and collaborate with various teams to develop and implement effective data management strategies helping you to drive better data-driven decision-making.

Thanks, Terri, there are some really great insights there. So, what are some of the pain points people need to look out  for when considering hiring a consultancy?

It can be really hard knowing where to start with data governance. It’s not all about data governance frameworks! I always find that looking for recurring data problems, such as a lack of trust in the data, data quality issues, or a lack of data sharing across different departments, is a good place to start. Very often governance is driven by regulatory compliance rather than being seen as adding business value to your enterprise data organisation.

Here at Oakland, we generally start with a data maturity assessment, which helps you to identify the right place to start. By doing this, you can get a clear picture of your current state, what needs to be improved, and what steps you need to take next.

To ensure that your organisation is making the most of its valuable data, there are important factors to consider that the data maturity assessment can help with, such as data literacy, education, and building a culture that supports good governance processes. Data governance is not a destination but a journey that requires constant revaluations and improvement as people and data evolve. As organisations create more and more data, it is necessary to keep checking the maturity levels of their data governance and literacy and continuously engage with your stakeholders so that they understand that data is an asset that adds value to the organisation.

The biggest shift I have seen in the past few years is that the chief data officer role has driven a step change in helping organisations with their risk management and recognising what is ‘right data’ and how that data can be used to improve operational issues. The data maturity assessment really helps to determine where you are on that journey and where improvements are needed.

One of the things I’ve come across repeatedly is how you get people to understand its importance. In different organisations, different roles may be more or less engaged with data governance. That is why it is so important to identify the right stakeholders. This is where a data consultancy can be really helpful with influencing your decision makers and change management, building roadmaps, and bringing their experience of working with different organisations to help you get senior leadership on board with the process, which can be half the battle. This is especially important because data governance is a foundational building activity, and it’s crucial to have buy-in from all levels of the organisation otherwise, it is very difficult to make a success of your data governance programme.

To be effective, data governance cannot operate in isolation. It is important that everyone who handles data in your organisation understands how their actions impact other areas of the business. I’ve seen organisations who have set up a central data governance function without giving it the authority or connectively to ensure the data is managed well throughout the organisation. This results in the data governance team constantly fixing problems after the fact instead of addressing the root causes and preventing issues from happening in the first place.

We talk a lot about data ownership, can you tell me why you think it is so important?

Ownership is a key concept in data governance, and a strong ownership model is really important. This involves articulating the roles and responsibilities of the people who will help drive your data agenda across the whole organisation, not just in siloed teams. A consultancy can help build the skills, behaviours, and capabilities to create data stewardship roles which support data governance within your organisation.

What other services can a consultancy help with Terri?

Data quality assessments are also important, as is understanding the materiality of poor data and how data governance integrates with other aspects of your organisation such as data analytics, data glossaries, and data dictionaries.

It’s also essential to measure the value and return on investment of data governance. This requires benchmarking and understanding the current state of your organisation, which a consultancy can certainly help with. They will have worked with similar challenges and will be able to help you to build the foundations for strong data governance by helping you to create a culture that values its data.

So Terri how can Oakland help?

Oakland have a team of incredibly knowledgeable experts who are able to support with both strategic and implementation of data governance. Cross functional teams that work together on projects, including governance, platform architecture, target operating models, and data strategy. They can focus on the end to end data journey with a team of technically excellent people who are passionate about their area of expertise and have a long track record of delivering successful projects and engaging clients. The Oakland website is a great resource with lots of helpful content including blog posts, working guides and case studies. We run regular webinars and podcasts and the Oakland LinkedIn page is the best place to see what we have coming up.

Andrew Sharp is a Principal Data Governance Consultant at The Oakland Group

The post What are the benefits of working with a data governance consultancy? appeared first on Oakland.