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.
All in Big Data
Big Data Project Management: What Works? (Slides)
These are drafts of the slides I’ll be using at the Data Science DC Lightning Talks on September 29, 2015.
Big Data Project Management Research Report #2: What’s In & What’s Out?
“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.
Big Data Project Management Research Report #1: Setting the Stage
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.
"Big Data Project Management" Interview Questions Set #1
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.
You Need a Project Manager on Your Big Data Team
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.”
Needed: Better Integration of Project Management and Data Management
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.
Is Loss of Government Economic Survey Data Inevitable?
While it may be inevitable that all government data collection efforts have to tighten their belts, hopefully the process of making tough prioritization decisions will be done in light of rational factors such as the value of the data to users, the cost of collecting it, the availability of alternatives, and the manner in which data management processes are governed.
The Commerce Data Advisory Council's 2nd Meeting: Storytelling, Staff Recruiting, and Complex Processes
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.
Ten Questions for People Who Manage Large Data Intensive Projects
I’ve been researching how to manage projects where the goal is to make large amounts of data open, accessible, and useful.
10 Basic Suggestions for Planning and Managing Data Intensive Projects
It is also impossible for the project manager to ignore how data management and data governance are handled in the organization as a whole.
Problems and Opportunities with Big Data: a Project Management Perspective
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.
Data Program Governance and the Success of Shared Digital Services
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.
Risk, Uncertainty, and Managing Big Data Projects
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.
Interim Report on the Generalizability of the NOAA Big Data Project’s Management Model
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.
Moving to the Cloud: Business as Usual or Opportunity for Change?
Sometimes it makes sense to consider open data programs and cloud infrastructure transformation at the same time.
Do People Really Understand What “Open Data” Means?
Ultimately the most important issue has to come down to deciding what level of data literacy citizens need. As the production and consumption of goods and services become more data-dependent in both developed and developing countries, it is reasonable to ask how much understanding of data and data related decisions people really need. I’ve referred to this elsewhere as data management literacy. Maybe we also need to consider data consumption literacy. After all, if people don’t understand or appreciate the services we’re providing, no amount of standardization, interoperability, or transparency is going to make any difference.
NOAA’s Big Data Project Comes Into Focus
What’s different about this newly announced NOAA program is not just the potential “big data” scope of the program but the way in which private sector cloud vendors are involved as intermediaries not only to the public but also to potential data vendors and resellers.
Will NOAA’s “Big Data Partnership” be a Model for Other Government Agencies?
One thing that’s different with the NOAA data partnership program is not just its focus on making data available for access and exploitation via the cloud but the changing market environment in which it will be operating.
Still, knowing that data exist – which is what the inventories will tell us — is not the same as accessing and interpreting the data. Even assuming the public eventually gains access to the inventoried data, we’ll still need contextual information about the programs described by the data and measurement of the impacts these programs have.