What’s the first thing that comes to mind when you hear the term “project management”? Most people throw out words like timelines, milestones, man-hours, staffing, budgeting, and invoicing. However, if we distilled all this, what’s really being described is managing the two most valuable resources — people and money. Project managers focus the majority of their time on these resources not only because they’re fundamental but also because they’re quantifiable; they can be measured, analyzed, visualized, and charted in many different apps in various ways.
Yet, there’s a resource even more fundamental than these on which decisions about time and finances are made — one that needs to be recognized and effectively managed before an appropriate framework for project management can be created. That resource is raw data, also known as project data. In real-world practice, raw data is often stored in a disorganized manner on a local drive, shared drives online, or in an email chain. Furthermore, the data itself can be found spread across several spreadsheets, categorized, and labelled so that it makes sense to no one but the current project manager, leaving it inaccessible to other team members or future staff. Raw data is collected and stored with little thought to how it will be accessed later.
Data is the first step and the last step in every business activity.
Any venture starts with data before any planning can get underway. It is usually derived from information collected from previous projects, researched data, or project post-mortems. All of these must be reviewed in order to make informed decisions and to plan for future projects.
Questions during this stage revolve around the feasibility of a proposed project. But even more fundamental questions — such as the project’s priority among other company objectives, or the ability to define an appropriate scope for the project to justify the investment, require pre-existing data in order to arrive at clear answers. These answers then provide the foundation for all your assumptions after that. So, if your raw data isn’t adequately housed, organized, and collated on one central platform initially, this could cause major headaches down the road. You may be missing essential information, or there may be inaccuracies in your data, both of which may significantly affect the progress or outcomes of your project.
Most companies don’t see business data as a vital resource, nor do they see its management as a high priority before starting a project. Why is this? Well, because picking the right tools to capture project data adequately isn’t something directly tied to anyone’s timesheets, nor can it be billed to a client. It’s already assumed to have been correctly done when the project is in progress, but it’s easy to overlook without a system in place.
When inaccurate data starts to cause issues, managers must spend enormous amounts of time and effort to locate and correct these errors. During this ordeal, they may discover another significant challenge — data access. The ability to quickly and easily find specific information needed at a given moment can make a huge difference in everyone’s stress levels and daily workload. If your company hasn’t recognized the need for a proper data management system, the seemingly simple act of accessing data can be incredibly laborious.
When a manager is responsible for overseeing a single project, then issues around data are usually manageable. Data collection and capture can be treated as an organic part of project management. When a project manager’s portfolio expands to, say, 50 projects, then it’s imperative for data management to be treated as a separate thing; its infrastructure needs to be established before moving forward. The more projects you have, the higher a priority data management should be.But, when managing multiple projects, not only does the amount of data increase, but the relationship between data sets can also vary depending on the nature of the work and how managers decide to organize their resources. Consider these two different scenarios:
Tomas is a manager overseeing seven projects. These projects are unrelated to each other. In addition, teams of four to five staff will be responsible for bringing all these projects to completion. In terms of data management needs, Tomas should have separate, secure locations to store project data for each project. So, he creates seven different folders, each containing each unique project’s data.
Debra is managing the development of ten rental properties. She’s running a single budget for all those projects, as well as a single team to work on all the projects concurrently. Debra needs a system where everyone can access any properties’ information at any time. In this case, she creates a spreadsheet (maybe even an online one) with multiple tabs, each representing a property.
This is why many consultants struggle to produce effective outcomes for their clients. They operate on the assumption that data management can be rolled into project management, but they inevitably discover that the solutions proposed can’t handle the combined demands of both because the prospect becomes too unwieldy. Data management is about collecting information, storing it, cataloguing it, and making it easily accessible for later use. Project management is managing schedules, deliverables, resources, and teams of people.
Before project managers make any decisions on allocating resources, they’ll have to spend a ton of time organizing their project data to make those decisions as informed as possible. The process of turning raw data into useful information that can be used during a project involves several steps. Raw data is collected in the field during the preliminary stage; data capture — the process of extracting information from a document and converting it into data readable by a computer — is an important next step that can take a considerable amount of time. With a lack of preparation for this step, important raw data may be lost, forgotten, or misplaced. The data that is retained often isn’t well-documented or categorized properly. Lastly, project managers may encounter the problem of having too much data, making the process of sorting through all that information more time-consuming than it needs to be.
In response, managers create their own customized, ad-hoc systems for the information, which may very well work well for their needs. However, in companies where several project managers are employed, this compounds the organizations’ siloed data issues. Each project manager houses, organizes, and categorizes their project data differently, making it often inaccessible if not incomprehensible when there are changes in staff.
Organizing project data is like organizing your kitchen or garage. Everything you want to do afterwards becomes easier. Not only is your team’s productivity increased, but making decisions becomes less fraught with anxiety.
Good data management allows you to base your projections on facts. Mistakes can be avoided early, and getting needed information becomes painless. Aggregate results make it possible to analyze past performance accurately.
It’s incredible how much work needs to be repeated due to common human error when people are forced to rush through the data access process. Information that’s easy to parse and filter prevents errors that otherwise result from multiple versions or storage locations — productivity and morale increase.
Whether it’s changing offices or handing a project off to another manager, change is much more manageable when companies have a comprehensive data management system that all project managers can use. Over time, well-kept records from each project allow you to repeat past successes. Each completed project becomes a template that you can use for next time. This may be in the form of a collection of repeatable tasks, a roster of dependable contractors you can reach, or a list of contacts to call ahead of time for anything that would otherwise cause a delay.