All in Project Management
As hard as it sometimes for those of us who are technically inclined to admit, management doesn’t necessarily care what tools we’re using. Nor does management care how we manage our projects.
These are drafts of the slides I’ll be using at the Data Science DC Lightning Talks on September 29, 2015.
“Boiling the ocean” by attempting to make too many changes early on is tempting. Incremental steps in delivering analytics-based value may actually accelerate change in the long run.
Some of what I’ve learned so far does seem to be unique to data intensive projects. At the same time, many of the challenges posed by “big data” projects will be recognizable to project managers based on previous experience with other large or complex projects.
I’m researching how people plan and manage data-intensive projects. I’m calling this my “big data project management survey.” I’ll publish the research results here. Let me know if you would like to participate or if you are interested in the results; my contact info is at the end of this article.
The firewalled article by Ian Thomas, The seven people you need on your Big Data team, is an entertaining and insightful overview of needed technical skills if you are tasked with developing a team that “… takes data from various sources … and turns it into valuable insights that can be shared broadly across the organization.”
These days, still, when you read about big data or if you attend conferences or webinars you’re much more likely to read about products and tools. You don’t hear as much about “back room” management issues you need to address to make sure all the members of the project team are sharing information and marching in the same direction.
There’s an understanding represented by this group that the data resources being stewarded by Commerce programs both reflect and are critical inputs to U.S. technical and industrial competitiveness. Hopefully this group will be able to facilitate an exchange of useful “lessons learned” and resources across the varied Commerce programs.
I’ve been researching how to manage projects where the goal is to make large amounts of data open, accessible, and useful.
It is also impossible for the project manager to ignore how data management and data governance are handled in the organization as a whole.
Being able to make wise investments in “big data” capabilities may require more collaborative approaches to project management and decisionmaking than are currently being practiced.
I’ve created this special compendium of posts that are relevant to planning and managing data related programs and projects. There are four groups:
I heard David Lebryk of the U.S. Department of Treasury speak on Data Act implementation this morning at the Johns Hopkins/REI Systems Government Analytics Breakfast Forum in Washington DC.
If the tip of the data management “spear” is analyzing data in new and interesting ways to help solve important problems, someone still has to manufacture the spear, carry it, and train throwers to hit the target.
Such challenges are not unique to the Federal Government. All large organizations desiring to take a more strategic position in how data — the lifeblood of organization processes — are managed and released will have to address such governance issues.
A key feature of the Project Open Data effort being managed by OMB and OSTP is that so much of it is being conducted in the open using accessible resources such as shared documentation, a defined metadata schema, and use of GitHub for capturing comments and issues. Agencies that want to involve private sector vendors in their open date efforts should consider the use and management of such tools as a required part of program governance and oversight (as long as sufficient staff and resources are provided to manage such efforts, of course).
Anyone who practices project management for a living will recognize this list. It’s certainly not unique to big data analytics project. It is however reasonable to ask whether “big data” projects are unique in some way that exacerbates the probability of failure.
It’s good to see the Federal government and private sector working together to create value from data that might not be realized were its use restricted only to specifically funded and legislated programs.
Sometimes it makes sense to consider open data programs and cloud infrastructure transformation at the same time.
Realistically it’s also impossible to control what people say online about companies. Just type the name of any large company into the Google search engine followed by the word “sucks” and you’ll see what I mean.