If you’ve spent any time in the market research business you know how cost competitive data services can become. Pressures to maintain data quality mount while customers seem to always want more while paying less.
All in Big Data
Using Internet Related Economic Development Goals to Drive Open Data Strategy
While there may be significant capacity issues related to Internet and data access in developing countries, it’s impossible to ignore the disruptive and competitive landscape changes the growing Internet use offers wherever existing industries are adopting — or are being threatened by — web based developments. It make sense for at least some of open data program planning to reflect those realities.
“Mobile First” Is An Obsolete Strategy
Government “mobile strategy” must evolve beyond a device focus to a user focus. What we refer to as “mobile devices” today are just the tip of the iceberg as we contemplate our movement through an increasingly interconnected world.
Challenges of Public-Private Interfaces in Open Data and Big Data Partnerships
I would not be surprised if government agencies find themselves experimenting with different organizational and governance structures as they begin to take open data programs more seriously.
Don’t Let Tools Drive Enterprise Data Strategy
Isaac Sacolick’s Friend or Foe? How Microsoft Excel 2013 Creates New Data Governance Challenges is a refreshing look at the challenges organizations face as the capabilities of data management tools outstrip the policies and processes for governing how such tools are used.
Data Transparency Needs Data Strategy!
We want systems and processes to be more effective and transparent, we want to be able to take advantage of improved standards and technologies when they make sense — but we also need to balance the cost benefits of change in a fiscally austere and change resistant environment.
Getting Real About "Open Data”
If the “geeks” on your team understand all this that’s great. If not, you need to seek out and involve other areas of expertise, starting with people who understand what enhanced data services can do for your target users and the problems you are trying to solve.
Necessity is the “mother of invention” and a place like Detroit needs all the invention it can get. Perhaps better and more disruptive access to basic data that drives service improvement could be an important element in Detroit’s recovery.
Making Open Data Accessible Inside the Firewall
Balance between control and autonomy will be critical and will vary by program size, complexity, structure, and maturity. Also critical will be how external and internal users of related data resources are supported when important distinctions related to privacy and security need to be maintained.
The Changing Culture of Big Data Management
In some ways managing “big data” tools and processes is no different than figuring out how to manage any other type of technological innovation. The technology is introduced, experts emerge and help control and shape evolving practical applications, and management eventually figures out what is worth keeping and what can be discarded.
How To Make Datathons and Hackathons Sustainable
What’s a “datathon”? Here’s one suggested definition from NYU’s Institute for Public Knowledge:
Planning for Big Data: Lessons Learned from Large Energy Utility Projects
What can we learn from working with large energy utilities and their data that will help others plan and manage “big data” projects?
Understanding How Open Data Reaches the Public
In the long run, traditional top-down efforts at management control may be insufficient to ensure that both agency goals and the potential benefits of open data access — including unanticipated cponsequences — are realized in an efficient and cost effective manner. While leadership by the Federal government’s IT infrastructure will be necessary, it will also be necessary to ensure that ongoing efforts to advance open data access are managed efficiently both across agencies and in accordance with individual agency and program priorities; IT staff cannot do this on their own and will have to work closely with agency management.
When Does a Public Data Good Become a Private Data Resource?
It may be that the greatest challenge facing private entrepreneurs in developing new and valuable information products and services based at least partially on public data will be public resistance to paying for information, no matter how new, innovative, or unique these producrts or services are.
The State of Government Data Transparency, 2013
What I found most interesting and useful about the Data Transparency 2013 conference held September 10 in Washington DC was the diversity. The ability of Hudson Hollister’s Data Transparency Coalition to bring together such a high-quality group of speakers and attendees representing so many different perspectives was quite an accomplishment.
How Our Increasing Digital Connectedness Improves Government Program Evaluation
There will always be a need to conduct formal evaluations of how well government programs perform. Such evaluations must take into account the complexity of programs and the need to distinguish among short term and long term impacts and the intervening conditions that also impact program effectiveness.
Robert Scoble’s explanation of why his employer is paying him to fly around promoting Google Glass makes perfect sense, especially this point:
At issue is whether the public will have the tools, skills, wherewithal, and resources to locate, understand, and make use of the data. If not, the division between the “haves” and “have nots” will be exacerbated, no matter how many data files are offered for download.
On Defining the "Maturity" of Open Data Programs
Making government program data more “open” should be the responsibility of the program that is responsible for generating the data. The cost of doing so should also be part of the budget of that program.
Learning from the World Bank’s “Big Data” Exploration Weekend
If you’re serious about data analysis there’s probably no substitute for getting “down and dirty” with real, live, messy data. Sometimes you just have to sift through the numbers with your “bare hands” if you really want to extract meaning from descriptive statistics, predictive models, and fancy visualizations.