How to shape meaningful data governance processes that add value to your organisation.
In part 1 of our Data Governance series, we looked at why you need to develop a data governance framework. In this blog, we delve into the process of developing a data governance framework, creating an operating model, and making a plan.
Your motivation, needs, and support structures.
The first step is to develop a framework. The framework determines what needs to be managed from a data perspective. We recommend you consult broadly across your organisation to identify your organisation’s data governance assets and needs. In having a variety of people and business units identifying needs, you will also be recognising current capabilities and culture.
To build your framework, start with your vision for data and guiding principles, and clarify what your drivers are and why you need this data governance framework. Guiding principles may be “Our data is shared”, “Our data is accessible”, “Our data is trusted”. Drivers are likely to be your strategic objectives, accountability, and/or compliance.
Then determine which needs will shape your framework, and how they will be enabled. These needs may include data quality, metadata, data sharing, access and security, and reporting and analytics. They may then be enabled by data quality management, data ownership, data lifecycle management and risk management, just to name a few.
And, finally, review what supports the implementation and application of your framework. There are three main areas that support your framework – policies, procedures and standards; structure, roles, responsibilities and skills; and measurement and continuous improvement.
How your people will affect good governance.
Now that you have developed a framework, the next step is to develop the model. This determines how the data governance framework will work, and describes the relevant roles and responsibilities of the people for the framework’s success.
Regardless of the size of an organisation, the roles and responsibilities can be considered from two perspectives – the business who has ultimate accountability for the data, and data experts who will support the business.
From a business perspective there are two critical roles, Data Owners and Data Stewards. Data Owners have the overall accountability for the data. Data Stewards are the subject matter experts, and are the people who are using and managing the data day-to-day, and supporting the users of the data.
From a technical perspective, you need to include any roles that will support the business in the application of the framework. For example this may be a Data Governance Manager or Officer to assist at an operational or tactical level. You should also think about the role that a Chief Information Officer or Chief Data Officer has at a strategic level to support the framework.
The final aspect when creating the model is to consider who is going to make decisions regarding the data priorities and investment, and the resolution of any escalated issues. For instance, this responsibility may be with a formal governance committee or part of the executive leadership team.
Data governance is an organisational responsibility, and it will be most successful if people right across the organisation care about the data, not just the Data Owners and Data Stewards.
Cross organisational actions – a shared approach, mindset, and time.
Once the framework and model have been developed, the next step is to develop the plan. This identifies all of the activities that you need to do to roll out the framework. What are the priority areas?
The key to a strong plan is to not only map out the technical implementation, but to factor in your people. Implementing the framework can result in a lot of change for your team, so factoring in change management is important. This can include internal communications, marketing, staff buy-in and ultimately having all staff understand the importance of applying the vision and principles and the framework. For some staff, the implementation of the framework can result in a change to their role as well as their daily activities.
At Data Agility, we recommend you implement the framework in an iterative and phased manner, rather than taking a big bang approach. By rolling out the framework in an iterative fashion, based on priorities and key needs, this allows you to measure what works well, and identify what doesn’t so that you can rectify it. There is no one size fits all approach. The order of implementation will be different for every organisation based on the priority.
It is also important to recognise that structuring and implementing good data governance will take time. At Data Agility, we regularly work with our clients over a 12-24 month period for a project like this. The first 12 months will allow you to focus on foundational elements whilst implementing in high priority areas. Following that it is about embedding the framework enterprise wide so that you have a consistent and mature capability across your organisation.
Do you need help structuring data governance for your organisation?
This is part 2 of our three-part series on data governance. If you missed part 1, read more on why you need to develop a data governance framework here.
Coming up in part 3 of our Data Governance blog series, we explore the steps you need to take to implement your data governance strategy.
If you would like assistance developing your data governance framework, model and plan, speak to one of our data governance experts today.