Although “Data Governance” is an important factor for any organisation to improve data quality, and drive ownership and accountability of critical data assets across their lifecycle, it is often unsuccessful. However, by implementing the following 5 key principles you can help deliver successful data governance initiatives within your organisation.
1. Sponsor buy-in
The vision of a data governance initiative is often difficult to grasp by managers and their direct reports. As with any organisation-wide initiative, a vocal and supportive executive sponsor that understands the value of managing data is critical in driving both cultural and operational change. A set of goals or objectives which underpin the Data Governance initiative and its vision should be continuously communicated by the Sponsor to the wider organisation, to build their readiness of what is to come.
2. Define the benefit, or the pain
Defining the benefit associated with a data governance initiative may be difficult; however, it is a critical activity to support sponsor buy-in, the business case, and driving change within your organisation. To define the benefits associated with good Data Governance, consider both the quantitative and qualitative benefits across the four business impact dimensions of People, Customer, Operational and Financial.
Benefits can also be defined by identifying existing quantifiable pain points, and how data governance will reduce or alleviate this pain for the business. For example, the inability to engage customers due to poor quality contact / address details may have a significant customer (low engagement scores), operational (high returned mail) and financial (churn or inability to reach to upsell/cross sell) impact.
3. Define a realistic scope for a pilot, prioritise, and gradually grow capability
Enabling data governance across the enterprise in one big bang approach can end a data governance initiative prematurely as it is perceived as too large an undertaking. It is critical to commence any data governance initiative with a short, high priority and well defined and understood pilot. The prioritisation and selection of the pilot phase should be directed by the area within the business with the greatest need, most pain and / or largest perceived benefit. This pilot can be selected based on priorities set across one or more of the following dimensions:
Critical data sets / domains: Prioritising governance across a data set / domain can normally rally a number of key individuals across the organisation. For example, the critical data set ‘Customer Data’ would require collaboration across multiple facets of the business.
The data function: Another approach to commencing a data governance pilot is to set up the required governance structure to support one or more data functions as defined by the Data Management Association (DAMA). For example, the pilot may focus on the Data Quality or Master Data function, and governance would include setting up the required organisational structures, forums, processes and policies to support this particular data function.
High priority scope items will naturally be a cross section of the above two dimensions. For example, the pilot may be Data Governance to support Customer (“data domain”) Data Quality management (“data function”). Following a successful pilot that provides a strong foundation to build on, the next priority based on the above dimensions will direct future governance initiatives that continue to grow your organisation’s data governance capability.
4. Identify critical data set ownership
For all critical data sets within your organisation, identify an executive level business owner upfront as they will be instrumental in key data governance decisions. These executive owners will help shape the data governance organisational structures required and most likely will need to foot the bill. For example, a suggestion to an executive that they require a ‘data steward’ in their team may be frowned upon if the executive data owners are not engaged and brought on the data governance journey from the start.
5. No one size fits all
Although there may be a set of generic governance organisational structures, roles and associated responsibilities, the way that data governance is implemented from one organisation to another is heavily dependent on the context of that organisation. Decisions on centralised vs. decentralised roles, the number of forums, the percentage of effort from existing FTEs to support data governance activities, the level of an organisation's maturity, the extent of the pain and the size of the data being governed all contribute to the design of a an appropriate governance structure that is fit for purpose for your organisation.
If you would like to learn more about how to implement Data Governance within your organisation or the 5 key principles above contact your nearest Altis office to talk to one of our data governance experts.