Using information technology to make data useful is as old as the Information Age. The difference today is that the volume and variety of available data has grown enormously. Big data gets almost all of the attention, but there’s also cryptic data. Both are difficult to harness using basic tools and require new technology to help organizations glean actionable information from the large and chaotic mass of data. “Big data” refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially those related to human behavior and interaction. The challenges in dealing with big data include having the computational power that can scale to the processing requirements for the volumes involved; analytical tools to work with the large data sets; and governance necessary to manage the large data sets to ensure that the results of the analysis are accurate and meaningful. But that’s not all organizations have to deal with now. I’ve coined the term “cryptic data” to focus on a different, less well known sort of data challenge that many companies and individuals face.
Topics: Big Data, data science, Planning, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, forecasting, FP&A, Human Capital, Marketing, Office of Finance, Operational Performance Management (OPM), Budgeting, Connotate, cryptic, equity research, Finance Analytics, Kofax, Statistics, Operational Performance, Analytics, Business Analytics, Business Performance, Financial Performance, Business Performance Management (BPM), Datawatch, Financial Performance Management (FPM), Kapow, Sales Performance Management (SPM), import.io