Establishing trust, creating and promoting a diversified data culture, and data literacy, are three of the most common challenges that business leaders face when it comes to effective data management and analytics.

However, an effective data governance framework can change all that…

First, let’s start with the difference between data management and data governance. In a nutshell, data management is the umbrella term used for all the different approaches, policies, and procedures that you can leverage to manage and improve your data. In turn, data governance is one of the disciplines that fall under that data management umbrella.

Data governance is the processes that a business puts in place to support the management, accessibility, integrity, and security of the data stored within business systems, both on-premise and on external cloud-based servers.

It’s a critical component that can underpin success for any organization, however, it can also be challenging to implement. Why? Many businesses fail to develop a consistent and well-defined approach, which is needed to support effective data governance – a solid framework is required to manage all company data. It’s also critical that everybody all the way from IT and finance down to sales, marketing, and HR are on board with the framework, to ensure that data policies and procedures are adhered to and ultimately, help drive business success.

The role of data governance is to specify the rules of what is and isn’t allowed in terms of a business’ data policies. While this may sound like a daunting task, effective data governance can be broken down into more manageable steps, such as:

Once you have answered the questions above, it’s time to move on to the next stage, developing a strategy for your data governance framework.

An effective strategy should support business development, as it helps to identify opportunities, such as new features, products, and services, while also mitigating risk – everything has already been thoroughly researched, agreed upon, and outlined within the strategy, before implementation. A concrete strategy should guide decision-making by defining boundaries around acceptable behavior within those policies, so everyone knows where they stand when faced with difficult choices to make. It will also promote greater accountability if and when things go wrong, by holding employees accountable for following those policies consistently across different departments or divisions within an organization – this is especially useful if multiple stakeholders are involved.

Finally, it should offer greater direction for making tradeoffs between competing interests, such as cost versus benefit analysis when trying out new technologies, methodologies, or approaches before fully committing as an organization.

Perhaps the most important aspect to define as part of a data governance strategy are metrics – how are you going to measure if the framework put in place is successful at reaching the goals you’ve set out? Or identify what’s not worked so well and resolve those issues?

There are two common types of metrics that organizations use to measure whether they’ve achieved success in line with business goals:

Achieved Metrics – these are the results of an organization's efforts toward meeting their goals and objectives, these might include things like the number of integration failures prevented, or the number of exceptions reported, for example,

Performance Metrics – these show whether an organization is performing well compared to other organizations in similar fields, such as the average amount spent per unit versus another company that provides similar services within the same industry, and can also include more subjective measurements such as customer satisfaction ratings

When it comes to data governance, specific metrics need to be put in place – these metrics enable organizations to monitor the effectiveness of the data governance framework and help determine its value.

For example, if you want to ascertain if you’re on track with managing your company’s cybersecurity, you can ask the following about your data governance…

By regularly posing and answering these questions, you can quickly identify areas of improvement, and ensure issues are rectified before they can impact the business or the bottom line.

Policies and procedures are two key components of a data governance framework.

Policies define what the organization will do, while procedures define how they will do it.

For example, one policy might be that the company will maintain an audit trail of any changes made to the data in its systems or that the company will not allow access to personal information without express consent from the individual whose information is being accessed.

Procedures, on the other hand, are essentially operating manuals with clear step-by-step instructions on how certain tasks should be completed – for example, how to change someone’s address in your system.

Data governance should be a shared responsibility between the business and IT – the honus should never solely be put on the IT team.

Business leaders are responsible for defining their organization's data strategy, which will guide how they handle data across the enterprise. Data governance responsibilities also include building support for good practices within each department and identifying opportunities to improve performance and efficiency by leveraging analytics. In addition, business leaders must also ensure that all the decisions that are made support both short-term objectives as well as longer-term strategic goals.

Data governance is not just about technology; it requires an organization to have an effective culture in place to drive continual improvement in its approach to managing data assets across the enterprise. The CIO (Chief Information Officer) plays a crucial role in establishing this culture by becoming an advocate for impenetrable processes and standards, while also providing leadership on issues such as risk management, privacy protection, and compliance with industry regulations.

The CIO must also ensure that all stakeholders are aligned with the organization's data strategy. This means working with both business leaders and IT professionals to develop a common understanding of the new approach being taken to address challenges such as poor data quality, inefficient processes, and ineffective analytics. It may require making changes in some roles and responsibilities while adding new ones where needed.

Data stewards

A data steward is responsible for managing data quality, compliance, and security, it’s a key component of data governance as it ensures that the right people have access to the right data at the right time.

The ideology behind data governance is making sure that people who own a specific piece of data are accountable for it. The goal of this process is to ensure that companies can make good decisions with their information and not simply rely on what has been reported in other places, like dashboards, for example.

It’s important to note that data stewardship is not about restricting access to data or placing barriers in front of people who want or need to use it. Instead, it’s about certifying that people are using the right data for their purposes and taking responsibility for how that information is used and shared. Likewise, data stewardship is not a purely technical or business role, as it requires a unique combination of both the skill sets required for these roles. For example, hard skills like data modeling and programming are often paired with domain expertise like enterprise strategy and storage concepts.

Aside from developing a solid data governance strategy, implementing the right policies and procedures, and assigning roles and responsibilities, working with the right data management platform is crucial to supporting the implementation of your data governance framework.

The right solution should have the ability to connect your data platform, whether that’s on-premise, cloud, and/ or legacy pipelines so that you can manage all your big data assets in one central repository, which is often referred to as ‘the single source of truth’.

The platform should also support your data governance framework by providing a central place to store metadata, policies, and rules.

Another aspect of a data governance framework is the process. A data governance process needs to support the implementation of your data governance framework by providing a set of tools to manage the lifecycle of your data assets. You should be able to use these tools to create, update and delete metadata, policies, and rules as well as monitor compliance with them using dashboards and reports.

A data lifecycle is the sequence of events that a particular unit of data goes through, starting from when it’s initially captured or generated, to when it’s eventually archived. Data is separated into phases based on different criteria, and it moves through these stages as it completes different tasks or meets certain requirements throughout its lifecycle.

Data governance is the process of managing data from creation to deletion. There are five stages of data lifecycle management – creation, storage, usage, archival and finally, destruction.

Data lifecycle management is a subset of data governance that focuses on managing the lifecycle and processes related to what happens to data as it goes through its lifecycle. It can be thought of as an organizational approach for effectively managing the entire lifecycle of your enterprise’s information assets, including identifying where they reside, establishing policies and procedures for their use and disposal, implementing methods for controlling access, establishing compliance requirements and monitoring performance against those policies and procedures.

A data governance framework is a way to ensure that your organization's approach to data governance is consistent and well-defined. It’s the process of managing data across the enterprise, ensuring that it’s managed effectively and efficiently, used appropriately, and kept secure. This can be accomplished through a number of different approaches, but what they all have in common is the need for consistency in how organizations manage their information assets.

It’s also the set of policies and processes that help organizations ensure that the right people are involved in making decisions about how data is used and provides a consistent process for storing, sharing, and using the information within an organization.

It’s important to note that not every organization needs to create its own data governance framework. Small companies that may not have much data may not need one at all. However, if you do have various different teams working with lots of different kinds of information at any given time, then it can be helpful to create clear guidelines on how this should be conducted, while still meeting their goals as efficiently as possible.

Remember, to support effective data management and governance, you need to: