Final answer:
More than five years ago, analytics relied on relational databases, BI tools, and basic data visualization software, hindered by technological limitations in data processing speed and volume. Tools like Excel, SPSS, and SAS were common for analyzing archival, economic, and demographic data. Despite technological advancements, the core principles of analyzing data for decision-making have remained constant.
Step-by-step explanation:
The field of analytics more than five years ago was marked by the advent of big data and an increasing reliance on technology to gather, store, and analyze vast amounts of data. During this historical period, the technology available and commonly used for analytics included relational databases, business intelligence (BI) tools, and basic data visualization software. These tools allowed businesses and researchers to organize and interpret data efficiently, revealing trends and patterns essential for decision-making. However, technological limitations often capped the quantity and speed of data processing, with cloud computing and machine learning only beginning to emerge as mainstream solutions.
At that time, analytic approaches like archival research, cross-sectional and longitudinal research, and surveys were applied using tools such as Excel, SPSS, and SAS. These tools drove the analysis of financial, demographic, employment, and economic data. While the scope of data has expanded significantly with the evolution of technology, the fundamental ability to perform balanced analysis of policies, opinions, cause-and-effect relationships, and to articulate descriptions of change over time has remained a crucial skill in the field of analytics.