NIST Big Data Interoperability Framework: Volume 9, Adoption and Modernization

The potential for organizations to capture value from Big Data improves every day as the pace of the Big Data revolution continues to increase, but the level of value captured by companies deploying Big Data initiatives has not been equivalent across all industries. Most companies are struggling to capture a small fraction of the available potential in Big Data initiatives. The healthcare and manufacturing industries, for example, have so far been less successful at taking advantage of data and analytics than other industries such as logistics and retail. Effective capture of value will likely require organizational investment in change management strategies that support transformation of the culture, and redesign of legacy processes. In some cases, the less-than-satisfying impacts of Big Data projects are not for lack of significant financial investments in new technology. It is common to find reports pointing to a shortage of technical talent as one of the largest barriers to undertaking projects, and this issue is expected to persist into the future. This volume explores the adoption of Big Data systems and barriers to adoption; factors in maturity of Big Data projects, organizations implementing those projects, and the Big Data technology market; and considerations for implementation and modernization of Big Data systems.

[1]  Dale Neef,et al.  Digital Exhaust: What Everyone Should Know About Big Data, Digitization and Digitally Driven Innovation , 2014 .

[2]  Geoffrey C. Fox,et al.  NIST Big Data Interoperability Framework: volume 3, use cases and general requirements , 2018 .

[3]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: volume 1, definitions, version 2 , 2018 .

[4]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 4, Security and Privacy , 2015 .

[5]  Louisa Tomar,et al.  Big Data in the Public Sector , 2016 .

[6]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 9, Adoption and Modernization , 2019 .

[7]  Claire C. Austin,et al.  A Path to Big Data Readiness , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[8]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 7, Standards Roadmap , 2019 .

[9]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 5, Architectures White Paper Survey , 2015 .

[10]  Kara H. Woo,et al.  Data Organization in Spreadsheets , 2018 .

[11]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[12]  C. Johnman,et al.  Big data! Big deal? , 2015, Public health.

[13]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[14]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: , 2019 .

[15]  Taxonomies Subgroup. NIST Big Data Interoperability Framework:: volume 8, reference architecture interfaces version 3 , 2019 .

[16]  Ning Jiang,et al.  Our path to better science in less time using open data science tools , 2017, Nature Ecology &Evolution.

[17]  P. Harker,et al.  Innovation in Retail Banking , 1997 .

[18]  Wo L. Chang,et al.  NIST Big Data Interoperability Framework: Volume 2, Big Data Taxonomies , 2015 .