At Oakland, we know that running a data quality check can seem daunting, especially within an organisation that handles an abundance of data. It can also sound expensive. So, do you need to invest in a shiny new tool to carry out the exercise, or is legacy software enough?

Read on to find out and understand the impact of regular data quality monitoring along the way.

Why is Data Quality Important?

Data quality is a key part of successful data management. It makes sure the data you’re inputting, analysing, and reporting on is accurate, complete, consistent, valid, and reliable. 

If you’re handling poor quality data, then you open up space for a host of negative impacts to be felt across the business. For instance, outdated customer data can lead to upselling opportunities being missed. Or, incomplete data that causes employees frustration and impacts both their satisfaction and productivity. Check out our blog for more information on the costs of poor quality data.

Not to mention if you want to use AI to improve your business processes and efficiency you have to get the basics right, which starts with the quality of your data.

The Impact of High Quality Data

The higher the quality of your data, the higher the overall quality, accuracy, and reliability of the decisions you – and your people – are using it to make. The more you invest in your data, the more opportunity you have to add value to both your people and processes, resulting in increased:

• Confidence
• Productivity
• Profitability 

Are Traditional Data Quality Checks Outdated?

It’s no secret organisations are handling bigger, more varied volumes of data than ever. And it’s only going to grow. Traditionally, data quality checks were a manual task, meaning they were prone to human error. They also ate into a vast amount of time, reducing efficiency and risking inaccurate or low quality data being detected too late.

Today, there are plenty of new and evolving technologies out there to support organisations in upholding data quality. Think artificial intelligence, which reduces the need for manual intervention (and the risk of human error), or machine learning algorithms, which can quickly spot anomalies and patterns in data. 

Learn how we can help you leverage AI to unlock more value from your data: Artificial Intelligence.

Do You Need to Invest in New Technology?

If you’re just starting out on your data quality journey, expensive technology and tooling isn’t necessarily the answer, and in many cases, it can be a costly distraction. We often see organisations with low data maturity jump straight into the tech, hoping they’ll solve deep-rooted issues. But without a clear understanding of what you’re trying to improve and why technology alone won’t deliver meaningful change.

For organisations that have a clear strategy for managing and improving data quality, the right technology can absolutely accelerate progress. They can bring speed, automation, and scalability to what you’re already doing well. But choosing the right tool is key, and with so many options out there, it’s easy to pick something that doesn’t quite fit.

There’s no silver bullet. Data quality tooling should be chosen based on the specific challenges you’re trying to address. It sounds obvious, but we’ve seen too many teams invest in tech that, six months in, isn’t meeting their needs.

It’s amazing how many organisations manage their data quality through Excel, so you don’t always need an all-singing, all-dancing data quality tool to get an overview of the state of your data quality. 

So, how do you get started? Here’s the advice from our data quality consultancy specialists.

How to Ensure Data Quality Without New Tools

1. Perform a data profiling exercise

First things first, you need to make sure you understand your current data quality. A data profiling exercise is the best place to start. This will give you an overview of information about your data, from the most popular values to if there are any duplicate values, and how many. This usually highlights anything that looks out of the ordinary.

To run out a data profile, you need to choose a small subset of data to investigate. For instance, customer contact details (addresses, emails, and phone numbers) As we mentioned you can use simple Excel to help with this, but if you are looking for more heavy lifting then  Databricks, or Microsoft Purview can be good options to help carry out the profiling and analyse the results. Then, you can start to ask questions to better understand the quality of the data, such as:

• What are the most common values and value distributions in key fields?
• What percentage of fields are null, zero, or default values?
• Are there outliers or anomalies we weren’t expecting?
• How many formats/structures are present in supposedly standard fields?

Enhance your decision-making even more with our Data Analytics consultancy services!

2. Assess data by developing business rules for each field

Now, you can actively assess your data by developing business rules around each data field. Make sure you assign each rule to data quality dimensions, such as:

• Completeness
• Accuracy
• Validity
• Timeliness
• Consistency
• Uniqueness

As you carry out your data assessment, you’ll begin to spot data that’s below an approved threshold or doesn’t follow the business rules you’ve set. With this knowledge, you can then carry out a root cause analysis to find out the cause of the issue. From here, you can put together improvement options and a long-term remediation plan. You may need to look at shorter-term fixes, too, depending on the types of issues you find in your data.

Here’s an idea of what your data quality improvements could involve:

• Ensuring values are updated in suitable timescales.
• Providing training, supporting documents, a business glossary or data dictionary to those who enter the data itself.
• Developing regular scorecards that track quality KPIs (completeness, accuracy, timeliness) for high-impact datasets.
• Ensuring that key reference data (like country codes, product categories, or customer types) are aligned and centrally managed.
• Setting up automated alerts or dashboards to flag anomalies, missing values, or outliers in real time.

• Assigning responsibility for data quality to specific individuals or teams to ensure accountability and continuous improvement.

3. Put regular data quality monitoring in place

With the bulk of data profiling done, you can put regular monitoring in place. Not only will this ensure you’re proactively keeping track of your data quality, but it enables you to set alerts when data quality drops or there are improvements that need investigating.

Power BI is one of the best ways to do this without a new or fancy data quality management tool. Developed by Microsoft, Power BI is a software product that helps you visualise data. Primarily, it’s focused on business intelligence – hence the name ‘Power BI’!

You can use Power BI to create dashboards that record and track your data quality, with fast access to results. The Microsoft software is also great for presenting your findings to colleagues or stakeholders, such as data stewards and project managers or your executive board.

For insight on how you can get executive buy-in for data-driven excellence, read: How to deliver a successful data strategy presentation.

Remember, Communication is Key

As we’ve set out above, you don’t need a new or state-of-the-art tool to run a data quality exercise and return insightful, actionable findings. However, there’s one thing you do need: good communication.

The better your people’s understanding of the requirements of the data your organisation holds, the easier it is for them to play their part in improving data quality activities, like data entry. Make sure you have your business rules approved and then communicated across the business, so everyone’s working with the same data quality ideals in mind. 

We also recommend you share any improvements that come from data quality reviews and assessments, so your people understand the tangible difference it’s making.

Data Quality Consulting

For more support on how you can improve data quality within your organisation, please contact our friendly team. And if you’re interested in more insight from our data quality consultants, read our blog: Why invest in data quality?

The post Can You Improve Data Quality Without New Tools? appeared first on Oakland.

At the heart of data management is data ownership – i.e. who’s responsible for data, and what this involves. It goes without saying that the better your people’s understanding of the importance of data ownership and their roles in it, the better the overall data management of an organisation. 

We know getting people to take ownership of data can be a hurdle in itself. So, we’ve pulled together advice on how you can implement a data management plan that promotes ownership of data and returns powerful results for both your people and processes.

Barriers to Data Ownership

Traditionally, anything and everything ‘data’ has fallen under the responsibility of an organisation’s IT department. They’ve held the keys to data access and management, but it’s fast becoming outdated in a business world where users need to retrieve and analyse data, fast. 

Data is at the heart of more target operating models than ever, so democratising it needs to be a priority for organisations wishing to remain competitive in a rapidly evolving landscape. On paper, it seems relatively simple. But the reality is a challenge. People who have had little, or not even any, data responsibilities may feel apprehensive about owning data, or unprepared for these new accountabilities. 

Need support with your data strategy? Find out how we can help!

What is a target operating model?

A target operating model is the ideal operating state of an organisation. You can think of the target operating model as an organisation’s blueprint for carrying out its strategy to run efficiently and meet targets. People, systems, and data are all fundamental to target operating models.

Read our blog, Target operating model: Delivering your business strategy, for more details.

Why Data Ownership is Important

On average, poor data quality costs organisations $13 billion a year (according to 2020 research by Gartner). The quality of data is directly linked to your data management system and ownership, because it’s a result of how people enter, process, and use it. So not only can data ownership empower users to better understand data, but it holds influence over decisions, not to mention the bottom line.

Dive into the impact of poor data quality further in our blog: What are the costs of poor quality data?

Overcoming Data Management Challenges

The first challenge you have to tackle is understanding your organisation’s strategic goals and pain points. Once you understand your organisation’s ambitions you can start to look at people and processes. Data ownership doesn’t happen overnight – it’s an ongoing activity that you’ll need to continue refining for the best results. When data owners understand how their data contributes to key business outcomes such as customer satisfaction, operational efficiency, or regulatory compliance they are far more likely to prioritise accuracy, timeliness and completeness. 

We’ve broken down three areas to look at to take the first step to better data ownership for your people and processes:

1. Executive buy-in
2. Policies and documentation
3. Training and communication 

1. Get executive buy-in

When executives or leadership are bought into data ownership, you’re immediately proving its worth and importance to your people. Plus, you have someone to allocate the resources (people, time, money etc) necessary to prioritise and solve problems. Where data ownership is practiced organisation-wide, it naturally becomes intrinsic to how people work. With executive buy-in, people are more likely to buy into the importance of data ownership themselves, and feel driven to make time for the responsibilities it involves. 

Whether you’re wanting to make someone a data owner, delegate, steward, or custodian, make sure you’ve got executive buy-in first – and watch as your people’s confidence in their data management role grows. Our blog has advice on how you can talk about everything data to your board of execs: How to deliver a successful data strategy presentation to the board.

2. Nurture confidence with data ownership policies and documents

On this note, confidence comes when your people are clear about what’s being asked of them, and why. Having the right policies and documents in place (as well as related training and support) are the basics of defining roles and responsibilities so that data ownership activities are understood and carried out. 

We also recommend you add details of data ownership roles and responsibilities to employee records, like objectives, and performance reviews, so they can be referred back to as and when needed. Remember to update these as ownership responsibilities evolve, too!

3. Invest in ongoing data ownership training

To make sure your people have the necessary knowledge and skills to confidently carry out their ownership role, providing the right training is imperative. Alongside standard courses or training, speak with your new data owners to understand how else you can support them with the additional responsibilities. Communication is key to building a culture where everyone understands:

• Why data is important for business
• How to use data effectively
• Their individual role in looking after/owning data
• Who the data owners are

Better Data Ownership for Better Results

The clearer you are with your data owners about what they’re responsible for, and why, the better your data quality – and in turn, data insights, will be.

For advice on how to tailor a data management plan that empowers people to take ownership of data, please contact our friendly team.

And for support with building the data strategy to take your business to the next level, check out our guide: How to write your data strategy.

The post How Does Data Management Help People Take Ownership of Data? appeared first on Oakland.