Report: Best practices for big data projects
Report: Realizing the Promise of Big Data, from the IBM Center for the Business of Government
Description: The 44-page report begins with a history of big data, cites differences between big data in the public and private sectors and includes some big data use cases in federal as well as state and local government.
Drawing on interviews with chief information officers from every level of government, it presents big data implementation steps, or best practices, grouped by the phases of a big data project: planning, execution and implementation.
The major insights from the CIOs focus on big data lifecycle questions related to design, execution, implementation, and post-implementation. Other findings are classified by the key area they are focused on — organization, personnel, business processes, technology and external environment.
Key points: Among the 10 findings:
CIOs succeed in big data projects by first tackling simple, yet visible, problems for the agency. Building maps of data elements is a valuable tool to uncover intricacies such as data dependencies, interactions among data elements and organizational factors. They also also help teams visualize the data space and identify areas for intervention.
A skilled task force is necessary to oversee a big data project. A staff with expertise in the technology, business and policy aspects of the project can help prevent any major surprises and ensure everything goes as planned.
The development of key performance indicators is critical to big data projects. Both process and outcome measures are essential to the project’s success. Performance measures are centered on improving efficiency, such as lowering the cost of operations. Outcome measures focus on how the customers perceive the service being delivered.
Bottom line: With the rise of big data in recent years, the data sets of government agencies have become critical resources. It is essential that CIOs and public sector IT managers take the proper steps to ensure successful big data projects.
Posted by Mike Cipriano on Feb 21, 2014 at 11:13 AM