All in Project Management
Whatever the environment, new data management and analysis technology may be important, but success and sustainability will also be driven by how we manage it and by how successful we are in putting data users and their priorities into the driver’s seat.
As an extension of my own research and consulting on big data project planning and management, I wanted to improve my understanding of how data governance and program management practices impact how medical and health data are used.
I’m fairly “old-school” when it comes to planning and building information systems. That is, first you decide what your requirements are and what services you need to provide, then you decide which technologies you need to adapt, develop, or purchase to help you meet those requirements.
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.
To appreciate some of the implications of the recent proposal by the International Committee of Medical Journal Editors (ICMJE) to make consideration of journal article acceptance contingent upon the author’s agreement to share de-identified clinical trial data with other researchers, as reported by NPR, some context is appropriate.
After years in consulting and project management, though, I now view politics and culture as factors to be anticipated and managed rather than ignored. This is true whether one is planning an individual project or an enterprise wide portfolio management strategy.
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.