We want to become a data-driven organization: A common scenario
Today I want to start with a series regarding the questions, how and where do we start with data management. And right away the disclaimer: This will be ‘a’ method you can use, not necessarily ‘the’ method. It is a follow-up on my info-graphic movie about ‘what is data management’ enriched with experience I’ve gained during my last years as a (certified) data management professional.
Let’s focus on a relatable example. You’re leading your department through a digital transformation and want to become data-driven. You and your colleagues are excited to set up a data management program and really want to make the company data-driven.
Unfortunately the rest of the company, doesn’t share your enthusiasm. Maybe they have experience with previous attempts to set up a data management program or have no clue what it entails.
All of the positive changes you want to bring to your company seem to be a dream and dream only. Where to start?
Let’s start a Data Management program.
The desire to start with a data management program is usually driven by reducing risks, improving processes or solving issues. However if your Data Management program is not in line with the organizational strategy you may encounter the problem that the program is not supported by executives. Managers/departments have the task to align their initiatives with the company strategy and therefor so should you when starting up a data management program. The clearer it becomes what the company challenges or problems are to be solved, the more likely it is for the people to change behaviors and adopt this new way of working. And why I say a new way of working, is because Data Management is not a one-time thing you do. It is a cultural change and only sustainable when adequate business leadership, sponsorship and ownership is present.
One of the possibilities to address change is with the help of the ADKAR method by Prosci. ADKAR stands for the
• Awareness of the need to change
• Desire to support and take part in the change
• Knowledge how to change
• Ability to implement change
• Reinforcement to sustain the change
If you keep these steps of change in mind and have a look at the building blocks for a data management program or roadmap, which in my opinion are…
Phase 1: Plan
To start of today. As many of us in the field know is that, for data management, DAMA international has created a specific framework for Data Management.
This framework contains 11 data domains, and describes all topics that should be addressed.
If you have a closer look to the context diagrams per data domain you can recognize, besides the techniques, tools, metrics, input and deliverables, the activities that need to be done in a Plan-Do-Check-Act format. Easy right? Well, unfortunately no. The framework does not explain ‘how’ and or ‘where to start’ with a data management program. I really like the Plan-Do-Check-Act format in general and believe it is applicable in many cases, if not all. For this part of the series I will share with you what, in my opinion, is part of the ‘Plan’ phase to start a data management program. Additionally I’ll become more specific by providing an example.
For the “plan” phase, the assessment of the current status and the desired future status is central. As mentioned before, the driver to start a data management program is mostly driven by reducing risks, improving processes or solving issues.
Issues could be for example, duplicate customer data, wrong or missing documentation, differences in naming conventions/definitions per system or re-use of numbers/fields for other purposes which causes reporting problems.
When the drivers are clear, choose a framework to work with (in this case DAMA DM-BoK) and decide the scope for the assessment (keep this as small as possible). Define an interaction approach and communicate this! You can’t start early enough with communication, this is part of the Awareness part of ADKAR. Start gathering information from your stakeholders and execute the assessment. When the assessment is finished, make summarized briefings and give recommendations.
State the vision which is connected to the organizational vision/enterprise architecture, its benefits, name where the program will help to reach the (long-term) organizational goals and express the value in costs. Make a stakeholder analysis (attitude, interests and constraints). Define guiding principles and proposed measures (KPI) for data management success. Plan short-term (12-24 months) SMART objectives /deliverables. Make a description of the data management roles & organization (operating model), their responsibilities and decision rights and do not forget to draw your subject area model.
Finish the ‘Plan’ phase and start the ‘Do’ phase with a (draft) implementation roadmap, I’ll elaborate on this next time…
Phase 2: Do
In this part of the series I’ll elaborate on the ‘Do’ phase. In many cases, business emphasis lies on the data governance domain of data management. (Because you cannot do data management without proper data governance!) Do recon that data management is not the same as data governance. In short:
Data management = Managing data to achieve goals (execution).
Data governance = Ensuring data is managed (oversight & control/ownership).
Therefore I will show you an example of a short-term roadmap regarding Data Governance.
Before executing the roadmap, make sure that there is executive sponsorship (Data Governance Council / Data Owners), alignment on leadership and keep your stakeholders informed (and check their level of ADKAR readiness). Keep in mind that when you want to truly engage with the business, focus on problems that they can relate to and communicate with them in their language. (Thus do not bother them with the DAMA DM-BoK framework, and use this for your own roadmap). Data management is cross departmental, but it is known that not every employee is aware of every step in the process or entire data lifecycle. Focus on overcoming small problems and create quick-wins to keep employees motivated. Work your way, step by step towards the end goal and most important: Integrate in the current processes/way of working.
Phase 3: Check
During the ‘Check’ phase a comparison is made between the actual results and what was planned. Thus do a re-assessment , evaluate your compliance with the guiding principles, goals and objectives (KPI’s). Measure the adoption rate (ADKAR) and evaluate the differences and its causes. Report these results to your stakeholders and keep in mind that your communication is aligned with the specific stakeholder. For an executive, keep it simple with nice pictures, traffic lights and points of attention. For the data stewards, keep it precise and in detail.
Example SMART KPI metrics template:
Phase 4: Act
When there hasn’t been enough progress, management or the data owner(s) should take adequate action to reach the initial set goals. Furthermore within the ‘Act’ phase have a look at your initial vision and research if it is still adequate. Is it still in line with the company’s strategy and enterprise architecture? If not, make adjustments and continue again with a new plan phase.
… and don’t forget to use wisdom acquired from lessons learned, common mistakes, create short-term wins and focus on what goes well! Also if some aspects of data management are already established, spread the word. Incentive starts with the idea to improve something, however people stay motivated if they see progress.
Wrap up:
Thank you for your time. I hope this was useful for you, and I wish you all the best with your data management journey!