Data Management Services and Consulting

Data management provides the processes and tools for managing information to drive business outcomes with the desired accuracy.

Data governance provides the rules of working with data – who and how decisions are made, who is accountable and responsible for what, and how your data is required to behave.
If you think about it like government/governance, and management of a company, it is easy to see: The government provides laws and regulations (rules) about what people are and are not allowed to do (don’t commit murder, do pay taxes, etc.).

Companies and individuals then develop ways to manage compliance with the rules – and how to achieve their goals. For example, a company would have a department responsible for managing the payment of taxes to comply with governance, and working towards its business goals at the same time (like making sure store sales staff are prepared to handle customer transactions).

So, in the data sense, data governance sets the policies and detailed rules (when required), like regulations. Data policies often look like “all must be owned” or “data definitions and rules must/shall be catalogued” and “rules for critical data must/shall be catalogued”.

While closely related to data governance, data management is much wider. It’s the processes through which a company achieves the data quality requirements for its operations and strategy, and the practices for complying with GDPR or keeping the data catalogue up-to-date to make the data teams efficient and effective.

Find out more about data governance

Explore why data is important for your business

When implementing data ownership, people and processes are the first important step. Ownership is a journey, and it’s important that you consider 3 things: executive buy-in, policies and documentation, and training and communication.

Executive buy-in is key to ensuring people take ownership of data. When assigning ownership, whether that is data owners, delegates, data stewards, or data custodians, you need these individuals to understand the importance of ownership, be driven to take on the responsibilities, and dedicate the required time to the activities.

To ensure this, executive buy-in is a core driver in ensuring colleagues take on accountability and ultimately drive ownership forward, rather than it being a tick-box exercise – or even declining their role in ownership.

Policies and documentation are then key in enforcing data ownership and defining roles and responsibilities to ensure that ownership roles are understood and actioned. It’s much easier for colleagues to get on-board with data management best practice if the ask is clear. It’s also very helpful to include their ownership roles and responsibilities in their job descriptions, objectives and performance reviews to further reinforce requirements and their importance.

Training and communication is the final element which is key in ensuring people take ownership around data management. When asking people to take on a new role and new responsibilities, training is key to building up their confidence and knowledge and supporting them through the journey.

By equipping them with the skills and capabilities to complete the task, you’re in a better position to drive forward their data ownership role. Communication is also very important for bringing the company along on the journey and building a data culture where everyone understands the importance of data, their role in looking after and using data, and who the data owners are.

Throughout the whole process of assigning and implementing data ownership, refer back to the pain points individuals have experienced with it themselves. By showcasing the benefits good data ownership brings, you’ll have them engaged and on-board in no time.

Head to our blog for more insight on data ownership.

Yes. As with most business transformations – and especially around data management – we believe in People, Process, and then Technology. While data quality might sound like a huge ask, you can make some real differences, feel business benefits, and increase return on investment without using a new tool.

First, do you understand your data quality? If not, look at doing a data profiling exercise. Choose a small subset of data to start with, like data surrounding a data quality issue that you want to investigate. Once you’ve chosen your data, start data profiling. You can do this using SQL, Databricks, Microsoft Purview, or even Excel! During this exercise, you’ll discover an abundance of information about your data, like:

• How many duplicate values there are
• What the character length range is
• What the most popular values are

From here, you can start asking the business some questions around the data. For instance:

• Should there be duplicates present?
• Do we have a set character length?
• What are the rules around this value? (For example, date of birth might need to be within a certain range; issue date might have to be a date later than purchase date.)

Once this is complete, you can develop business rules around each field of data to actively assess it. Remember to assign the rules to a dimension of data quality, e.g. accuracy, completeness, consistency, timeliness, uniqueness, validity.

During the data assessment, you’ll see where data fails the business rules or is below an approved threshold. Once you understand this you can then look at root cause analysis to understand what the cause of the issue is, followed by developing improvement options and then a remediation plan. Depending on the issue itself, you may need to look at short term fixes ahead of a long term remediation solution.

Some examples of what data quality improvements may involve include:

• Building rules on the data field itself to enforce certain data types or formats
• Providing training, supporting documents, a business glossary or data dictionary to those who enter the data itself
• Cleansing the data itself, like removing duplicates or filling in missing values

Once you’ve done the leg work to profile the data, you can set up regular monitoring to keep track of your data quality and set alerts for when there are drops in data quality or improvements need to be investigated.

A great way to do this without a fancy tool is to use PowerBI. You can create impactful dashboards to monitor, record, and track your data quality as well as present it to different audiences, such as data quality working groups, data stewards, project managers, and executive members.

But as with any data management initiative, communications are key. Once you’ve developed these business rules, they need to be approved in a steering function and communicated across the business. When your people understand the requirements of the data, they can support activities such as data entry and change impacts. It’s also key to broadcast any improvements and benefits made from reviewing and assessing the data quality, so people understand the difference it’s making and the quality of the data they are using.

For more details about the importance of data quality, read our blog: Why invest in data quality?