As a project manager, you quickly learn no two projects are ever the same. Whether it’s the client, team, or content itself, there’s always some level of unpredictability. Over the past year, many of Intridea’s projects have revolved around data visualization, with large data tables or many datasets.
With their enormous levels of data and unknown variables, these projects can easily be intimidating and very overwhelming. However, don’t be discouraged.
In the next few weeks, I’ll be giving you a few tips and tricks on managing successful data projects. Tools to make you a success in any data project, and maybe even turn that intimidation factor into gasp excitement.
Define Data Update Responsibility Early On
Whenever possible, get data responsibility in writing! Depending on the project, data changes or unexpected additions to the data can (read: WILL) be time consuming! Having clearly defined responsibility in case of x, y, or z, from the start, can help eliminate costly delays and prevent confusion or unexpected costs for your client.
In addition, it’s important to discuss and fully disclose the volatility of data and the nature of data visualization development. As data discovery happens, and you learn how the data relates to your front end code, you may have to reset expectations for a particular deliverable. Flexibility is key, and if your client’s expectations are already set to “expect the unexpected” you won’t lose any credibility in their eyes.
Keep in mind, you may need to define different roles for responsibility – ranging from table updates and query management, to data normalization. Every project is bound to have various tweaks, manipulations, or edits; having a plan laid out for these scenarios will counteract any potential scrambling or wheel spinning.
Establish Data Deadlines and Stick to Them
Working with a changing dataset is like hitting a hummingbird with a nail gun; when you pull it off, it’s spectacular, but has less to do with nail gun prowess and much more with luck. Establishing data deadlines for both you and the client is one way to increase those chances.
In a perfect world, full data sets will be available at project kickoff, with no need for changes during development. In reality though, this is rarely the case. Thus, to keep your developers from chasing a moving target, establish early “stop dates” for data changes. Be clear with the client; specifying specific time frames for changes, and that any changes resulting from those updates will fall outside the original scope of work.
These guidelines make it much easier to box-in and track problems from changing the data, and introduce new edge cases during development. And most importantly, it enables you to track unexpected hours spent from said data changes.
Got any ideas, tips, or tricks for managing big data projects? Let us know!
Want to learn more? Check out the entire Successful Data Project series below!
- Successful Data Projects, Part II: Work that Data
- Successful Data Projects, Part III: Expect the Unexpected
