.. 10 ways that data analytics will evolve in the next 10 years … Data Analytics help us know more about “where we are now,” trends/factors getting here and alternative future scenarios. Predictive analytical tools help management examine change — what will change where, by how much, and when. Cause and effect models can tell us how change might impact you/us; they help us examine options that might alter outcomes. Geographic Information Systems tools help us knit together disparate geographic, demographic, economic and business data and better understand patterns using visual and geospatial methods.
Data Analytics Origins and Evolvement
Data Analytics had its origins in the 1960s/1970s with the availability of mainframe computing. Statistical packages, such as SAS and SPSS, enabled access to a range of Data Analytics tools by a wider array of potential users. Programming tools became more widely available creating customizable Data Analytics tools. Large scale machine-readable data became both available and processable for the first time.
Data Analytics growth has been hastened by more accurate and detailed digital geographic data and geographic-based data. Data Analytics expanded with PCs/microcomputers growth. The Internet has created new ways to perform Data Analytics applications on-the-fly and reachable by a wider set of users. Data collection tools/methods have expanded. Advances in secondary data development has improved the ability to use cause and effect modeling, forecasting and impact analysis.
Data Analytics Trends During the Next 10 Years
1. Organizations using Data Analytics effectively will experience improved growth opportunities relative to those not using these methods.
2. Data Analytics will continue to evolve as a set of integrated techniques and methods.
3. As Data Analytics use grows, a wider area of STEM-related occupations will expand in business and government.
4. TIGER/Line geographic data and digital map databases will improve in quality and scope enabling better location-based analyses using Data Analytics.
6. Cause and effect, stochastic, simultaneous equation modeling will become more widely used as a core element of Data Analytics.
7. Annual time-series demographic-economic data will become available from the American Community Survey for use in Data Analytics modeling.
8. The role of Geographic Information Systems (GIS) tools, geospatial analysis and visual analysis will become increasingly centric to Data Analytics.
10. Several challenges may impede Data Analytics evolvement including data linking (geocodes and file structuring) and data distribution architecture that makes data difficult to consume.
About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data.