All in Data Program Management
Two risks are apparent for GE’s approach which may also be relevant to other industries where cloud-based analytical platforms are being developed and marketed. (I’m thinking here, for example, of medical devices, prescription medicine, and treatment costs.)
Government regulators need to be sensitive to the costs of complying with and overseeing the regulations they impose on data management, as do the organizations that are regulated. Costs related to data oversight, quality control, standardization, security, and privacy all need to be considered in comparison with the quantitative and qualitative benefits that will be generated.
If you’re doing exploratory data analysis to help you decide how much data prep might be needed to make your data public, that’s one thing.
Given the wide variations that currently exist in most organizations in understanding the ins and outs of managing current data governance and analytics practices, it’s not surprising that bringing in potentially “disruptive” technologies will be even more of a challenge.
There’s a lot more to collaboration than just providing a technology platform.
One of the benefits of focusing on behavioral outcomes as a way of assessing the effectiveness or usefulness of improved data analytics is that behavioral outcomes are potentially measurable.
A slide deck summarizing where I am on researching Big Data Project Management.
I began researching “big data project management” when I started seeing publications and online discussions concerning big data project “failures” being attributed to the classic reasons for project failure such as scope creep, poor stakeholder engagement, and inadequately understood requirements.
“If data analysis is Big Data’s “tip of the spear” when it comes to delivering data-dependent value to customers or clients, we also must address how that spear is shaped, sharpened, aimed, and thrown – and, of course, whether or not it hits its intended target. We also want the processes associated with throwing that spear to be both effective and efficient.”
If what Nate Silver said in a recent presentation is true – “Big Data has Peaked, and that’s a Good Thing” – perceptions about big data are maturing.
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.”
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: