Separating data management from project management
Data is the silver lining that binds every project together
More often than not, the responsibility to manage data falls onto the project manager. To a certain degree, they can handle this, but keep in mind: project managers are trained to manage projects, not data. Although many can successfully sustain the project and data management dynamic, others cannot. Eventually, they will start losing control and project management will become more chaotic over time. The border between chaos and control is tough to spot. It’s undefinable or fuzzy at best. It poses a massive problem as the transition into chaos could be a subtle journey. On top of that, it’s challenging to identify when and where they are in this transition as each manager has unique skills and personality that define their chaos tolerance.
This chaos is not caused by the projects being over budget or delayed. Instead, it’s due to the lack of insight: unknown budget status, unknown task status and lacking team updates. The unknown creates chaos, and chronically missing information creates the unknown.
Every project manager has different strategies and ideas for controlling that chaos/unknown by implementing various processes and workflows to capture, use, and store data. However, more projects are being added in time, and more information is being captured and stored. The fixes that worked for a number of projects usually create problems when scaling. If data management isn’t correctly set up, it grows into a crippling mass that will crush your efforts to manage projects.
This is why I want to focus on data management. It’s part of why the journey from control to chaos is a brewing process and is hard to pinpoint exactly when the point of no return has passed.
So how would a project manager approach data? The most straightforward answer is to DTR (define the relationship) between project management and data management.
Defining and re-defining
The most common understanding of project and data relationship looks roughly like this diagram. Here, data management is part of a project. Data indeed needs to be managed, but the question lies with “who” should be the one to manage it. This is where project management and data management diverge. Fundamentally, approaching both these aspects of management needs to be completely different. Project management is task-oriented, whereas data management is system-oriented.
For example, a PM tracks tasks, budgets, and plans. They organise and personally glue a project together. Arguably, a project manager ultimately manages people and their time. Therefore, time and efficiency are measuring metrics to determine how well a project is managed. Execution is a huge part of a project manager as the outcome mirrors control.
Data management is something entirely different. It doesn’t care about tasks, efficiencies, execution, or people’s use of time. Lasted, it focuses on how information is stored, recorded, organised, and retrieved. Each task, budget, form, contact, and input create data. An individual doesn’t fulfil a data management role; it’s resting on everyone involved. It’s a systematic process that each person must follow. Data management focuses on a method. Data management is control.
An example to illustrate my point
Imagine you’re hiring a cleaner to clean your entire house. The problem is you’re not only dirty, but you’re also messy. You’re a borderline hoarder. How effective will the cleaner be able to clean? Not well. Why? Because the cleaner is hired to clean, not tidy up the house to prepare it for cleaning. The mess represents data. Cleaning the dirt represents the task. The house is the project. The cleaner is the project manager. To clean efficiently, this house needs to be tidy and organised before any cleaner can start.
Now, in real life, this scenario happens all the time. A typical solution is to hire more cleaners (project managers) to do the job. But without first organising the mess, no amount of cleaners can effectively do their job. The ultimate solution is to create a system that keeps the house organised in the first place. That system is data management.
The Fuzzy Line
Any management process must be developed with clear definable objectives, roles, and expectations. I understand the frustrating effects of ambiguous outcomes while expecting clear ones. It’s impossible and is a flawed method for an organisation to operate. And when project management and data management are not well defined, it isn’t easy to separate them. It takes experience to know where one boundary ends and another begins. To further complicate things, each PM has their own level of competence and comfort. Not to mention the practical implications of developing a data management system from the ground up. The reality calls to define their relationship despite how fuzzy that line may be.
The right time isn’t perfect
In a perfect world where there are no surprises, and we are equipped with all the knowledge and experience, nothing will go wrong. Every organisation has a data management system designed and implemented from the ground up in such a world. Unfortunately, we don’t live in a perfect world, and if we did, I don’t think it would be as fun or exciting as the reality we live in today. But, however exciting the real world is, the challenge of finding the right time to upgrade is real.
And (not-so-shockingly), there’s no easy answer. The best I can offer are two principles that can help frame your ideal time to tackle data management. Of course, like all principles, it's open to your interpretation on how relevant they are to your unique and specific situation. Regardless, I’m confident that the ideal outcome is to take data management out from project management.
Principle 1: The 80–20 rule
Also known as the Pareto Principle. It’s a ratio that can help guide our efforts to be more productive. This is a good rule of thumb when project managing: 80 % of your time is project management, and 20% of your time is left to manage data. This principle can be a helpful framework to gauge your effectiveness.
I believe this is a tangible way to consider establishing a data management system. If you notice you’re devoting increased time finding, organising, or retrieving information, then it’ll probably be a growing responsibility. Another sign is if your project has multiple data input sources like paper notes, photos, forms, reports, etc. In that case, it’ll be particularly challenging to maintain an 80/20 ratio in your work. Remember, as a project manager, your job is to keep track of progress, not hunt data to move the project forward.
If you can manage both projects and data without a data management system, growth and capacity will be a challenge. Just because you’re in control today, you may slip into chaos when you’re beyond capacity. At that point, the most common solution is to hire more project managers. It’s an easy fix, but it won’t work and will probably make things worse. Going back to my analogy of a dirty and messy house: hiring more cleaners won't help clean your house until things are organised.
Principle 2: Self-awareness
The second principle is a more philosophical one. It’s the principle of self-awareness, in particular your subjective feeling of being overwhelmed. It’s common for project managers to hide their signs of stress for others to see. However, every project manager will experience burnout at one point in their career. I’m not saying that data management is the solution to that stress, but what I am saying is that data management shouldn’t be a significant burden for a project manager. Essentially, without a proper data system in place, a project manager is performing two roles simultaneously. The tragedy is the aforementioned misconception that project management includes data management. This belief is a primary crux to many organisations. My only explanation to why companies suffer from this is a mixture of the year after year growth, resistance to change, and being unaware the problem even exists.
That’s why I wrote this article. I’m here to tell you that change shouldn’t be avoided and that data management requires more attention. If you can get it right in time, then the year after year growth becomes a battle to conquer instead of internal fire to smother.
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