By Dennis D. McDonald

The following are links to and excerpts from a selection of this web site's articles related to the topic "data program management." 

An Introduction to Data Program Management (DPM)

"Data Program Management (DPM) is the intelligent application of data management tools, technologies, and processes to improve the usefulness of an organization’s data."

Improving Data Program Management: Where to Start?

"A good starting place is to examine why you got started with upgrading your data handling and analysis efforts in the first place."

10 Basic Suggestions for Planning and Managing Data Intensive Projects

"When you are faced with planning or managing a “data intensive project,” is there anything in particular you need to keep in mind about this type of project that might help make your project a success? What follows was written from the perspective of a project manager with experience where a primary project focus has usually been on generating, managing, transforming, or presenting data of various kinds."

A Framework for Transparency Program Planning and Assessment

Provides a model outlining the elements needed to plan an effective program for making data open, accessible, and aligned with program goals.

Big Data Project Management: What Works? (Slides)

Insights on how to effectively manage “big data” projects based on interviews with data scientists, project and program managers, and government officials.

Building a Realistic and Effective Data Program Governance Strategy

Planning an effective data analytics program requires a governance process that focuses on developing and supporting useful data-based services aligned with program goals.

Dashboarding Open Data Program Governance

Discusses both intermediate progress reporting metrics as well as the governance challenges associated with data programs that incorporate partnership with private sector organizations (such as the NOAA open data program).

Developing a Basic Model for Data Analytics Project Selection

Eventually priorities must be set for making use of data. This article describes the factors to consider in project selection.

How Much "Data Science" Do You Really Need?

"Is it really true that "Nearly two-thirds of big data projects will fail to get beyond the pilot and experimentation phase in the next two years, and will end up being abandoned," as suggested by Steve Ranger last year? My take: to be successful you need a collaborative team with multiple skills, effective leadership, good communication -- and a plan. In other words, don't put the cart before the horse by starting with a technical solution before you understand what problems you'll be trying to solve."

Improved Data Access Requires More than Analytics and Technology

“… we need to do a better job of making sure that people can understand and use their data. Not everyone is a data scientist or statistician. Even reasonably intelligent people can be flummoxed by the intricacies of even a moderately sophisticated spreadsheet. Plus, the details of an individual’s financial or health records may require expert knowledge to interpret.”

Interim Report on the Generalizability of the NOAA Big Data Project’s Management Model

NOAA’s Big Data Project is an innovative effort to provide public access to large amounts of environmental and weather data through innovative partnership with private sector cloud vendors. This article is a progress report on the issues that the program needs to address including  transparency, risk averse management, and the measurement of success.

Learning from General Electric’s Big Data Challenges

Reviews a case study of how an industrial giant takes advantage of big data, software as a service, and infrastructure development to build a new business that transforms an old business. Good view of the “industrial internet.”

On Managing Health Data Programs: Some Thoughts After the Health Datapalooza Conference

A snapshot view of how health related data are providing the foundation for a wide range of new data-reliant products and services:
“From a program design standpoint this means government health programs that generate useful data need to incorporate systems and processes not only to makes sure program data are used internally in an intelligent and secure fashion to support planning and management but also to make sure appropriately anonymized data are discoverable by and accessible to innovators, developers, analysts, researchers, and the public.”

Planning for Big Data: Lessons Learned from Large Energy Utility Projects

Lesson learned from energy utility “big data” projects.

Problems and Opportunities with Big Data: a Project Management Perspective

Adopting big data tools and process changes may be associated with a range of organizational changes.

The Changing Culture of Big Data Management

“Yet, big data does offer challenges that many analysts and managers are going to have difficulty reconciling. Analysts really do need to understand more about business and business strategy than might have been the case in the more compartmentalized past.  At the same time, managers who don’t understand and appreciate how data analysts work and how trends, modeling, and error are handled will be at a disadvantage. The two groups need to work together to make “big data” work.”

Thinking About “Data Program Governance”

“Data governance organizational structures have to be sustainable. They must support and facilitate needed data related services. While short-term “skunk work” tactics might best be served by a separate organization, in the long run a more federated or collaborative approach empowered to work through existing lines of authority might also make sense.”

Understanding the Challenges of Big Data Project Management: “The Data Must Flow”

“Managing data and metadata at an enterprise level to facilitate efficient tool use can be a complex undertaking. This is especially true when corporate actions such as transitioning IT resources to the cloud, constantly upgrading technologies, and increasing attention to privacy and security must also be considered. Such complexity should not be a cause for discouragement but should help drive the organization to become more disciplined in how it generates value from its data.”

Understanding the Challenges of Big Data Project Management: The Business Case

“The first challenge – – knowing how to develop a convincing business case – – is something that can be taught or purchased. The second challenge – – knowing in advance what the outcome will be of a new data analysis or predictive modeling effort – – is more difficult to address and may be especially acute where management is not analytically oriented. One way to address this second challenge is to start with something simple and not attempt a program- or enterprise-level change requiring modifications to the organization’s culture.”

What Kind of Management Structure Is Needed to Govern a Data Analytics Program?

Discusses organizational models when “data analytics” is the focus.

Why Measure the Value of an Organization's Information?

“Perhaps the most problematic aspect of measuring the value of information is how we deal with uncertainty. In deciding how to plan an improvement in how an organization manages and analyzes its information assets, it's not unusual to have to answer the question, ‘Why should I spend money on this system/project/program/tool if I don't know with certainty how useful the results of this new analytical capability will be?’”