As a year-end activity, a lot of people write about the top-ten this and the top-ten that, from the year that is coming to a close.
I am not going to do that. Instead I am going to look back at the year and point out for my readers the top reasons that data governance and data management programs are having difficulties or trouble getting the support they need to be successful.
In this article, I am going to highlight what I have witnessed as the top six mistakes that organizations make when embarking on a Data Governance and/or Data Management Program.
The mistakes are not provided in any particular order but they all can stand on their own as reasons why the data management disciplines get looked-over or fail to meet the organizations expectations.
1) Senior Leadership does not understand the resources and activities that are required to govern data effectively and therefore are not able to support and sponsor management of data as a valued asset.
The very first data governance best practice selected by many organizations is that Senior Leadership supports, sponsors, and understands the activities of the data governance program and the people that are steering the ship. Without this level of support, sponsorship and understanding – at all times during the life of the program – there is typically a consensus that the program will be at risk of abandonment or failure.
Special attention must be paid to understand the values and mission of the organization, and the goals of data governance should be aligned with these matters. Some organizations include words from the mission within their definition of data governance. A recent client ends their definition with the words “to achieve operational excellence” while another directed their definition to “minimizing and eliminating data risk.” One other organization included “successful governance of data and information” in their C-Level’s 2019 goal, almost forcing the hand of Senior Leadership to understand the activities of their data governance initiative.
2) People in the business areas do not understand why they are being asked to do things differently or why extra controls associated with governing data are in place.
A gentleman recently asked me how to get the people in his business unit to follow the data governance program activities that are being set forth by his organization’s data governance team. The term “day jobs” highlighted the conversation as the people that remain at his organization after series of layoffs are very busy doing the jobs of several people. People do not have time for “extra stuff” and therefore data governance is getting a bad, or should we say invasive, reputation.
The difference between the Non-Invasive Data Governance approach – the approach that I am particularly fond of – and other approaches is that the non-invasive approach focuses of formalizing accountability where, in fact, the accountability was informal, inefficient, and ineffective over the years. This approach focuses on communicating effectively with business units to help them understand the value that data governance brings to their existing job, without requiring changes in priorities or asking them to do more work in the normal work day.
3) Organizations look for direct Return on Investment (ROI) from Data Governance rather than where their major investments lie, which is in information technology that depends on quality data.
It is not impossible to demonstrate financial return on investment directly from the implementation of a data governance program. It’s just not easy either. Organizations focus on how much will be gained versus how much will be spent when determining where to spend their resources. That appears to be extra true when organizations decide to focus resources on data governance and data management disciplines. Return on investment for these disciplines is difficult to articulate.
However, organizations appear to be ready and willing to spend their resources on the latest and greatest technologies including building out their analytical capabilities, business, and artificial intelligence capabilities, big data, smart data, and gobs of integrated applications. There is one common requirement to provide ROI from these investments: high quality data.
Organizations should look for ROI from these initiatives due to the availability, quality, protection and shareability of the data. Look at what you are getting from these initiatives and then turn up the quality of the data and look at the ROI again. Or recognize, in the first place, that you will never get any ROI from these initiatives unless the data is trustable, understandable, and available.
4) Roles and responsibilities associated with governing data are not defined to mimic the culture of the organization and are not agreed upon by the people participating in the roles.
Roles and responsibilities are the backbone of a successful data governance program and must be communicated effectively and approved by management and the people filling the roles. The roles become a vital part of governance accountability, processes and communications. Organizations should be very careful in how they define roles, taking extra care to make certain the roles imitate real-life within the organization. I share an Operating Model of Roles and Responsibilities in many of my webinars, articles and presentations, and I always suggest that – rather than trying to plug your organization into my model – you do exactly the opposite and attempt to overlay the roles over existing roles within your organization. Please reach out to me if you would like to receive a copy of the Operating Model.
I am known to say the saying “everybody is a data steward, get over it,” which always seems to grab people’s attention and often erupts into a debate on the subject. The reason I say this is that everybody who either defines, produces and/or uses data in the organization must be held formally accountable for that relationship as a steward of the data. Almost everybody has a relationship to the data so therefore the program should direct them on how to define, produce and use the data appropriately in order for data governance to be successful.
5) Resources have not been defined to administer the governing of data including the senior level sponsor and the person or people required to direct the effort.
This aligns perfectly with a second best practice that the majority of my clients define when assessing their organization’s data governance maturity. As I stated earlier, one of the criteria I use for defining best practices is that the program will be at risk if the best practice is not achieved. If there is nobody to administer the program and resources are not permitted to apply their time to improving the way data and information is governed, your program will be at risk (or maybe even dead) at this point.
Data Governance does not require a large team of people to run the program. But if NOBODY has that responsibility, the program will fail. Many organizations start by defining a Manager, Leader, or Administrator for their program. That person has the early responsibilities of defining the program, goals, scope, roles, processes, communications, etc. Later on, they have the responsibilities of administering and managing the activities of the program such as working teams, councils, steering committees, and the stewards themselves. Depending on the speed in which the program implementation is expected to roll out, additional resources may be necessary to cover all of the bases effectively. The program will not manage itself.
6) The organization has not formally approved and communicated the goals, scope, success measurements and expectations of governing data including what will change, how it will change and the impact it will have on people.
It is important to piggy-back this final mistake on the previous mistake of resources not applied. For an organization to be successful with data governance, it is important to make certain that the people in the organization from the Senior Leadership level to the Operational Stewards level understand the goals, scope, and expectations of the initiative. This requires that somebody in the organization has the responsibility for defining, vetting, and gaining approval of the goal, scope, and expectations of the data governance program.
Another piece of this mistake is that people from the top to the bottom of the organization must be told what will change, why it needs to change, how it will change, and the impact the change will have specifically on them. Many organizations define goals, scope, and expectations but few organizations become good or great at sharing how data governance will impact people within the organization. This needs to change if we expect people across the organization to pay attention to the fact the data and information require governance. This act of sharing how things will change is an important aspect of staying non-invasive in your approach.
This quick article focuses on six things that organizations can do better in how they prepare their organization to successfully govern their data. Please share if your experience is the same or different. The more people have access to the biggest mistakes they can make, the better off all of our data will be.
