More data is collected in one day now than existed in the world just a few years ago. Unfortunately, this speaks only to our ability to capture data, rather than to its inherent utility. This dramatic surge in data is essentially caused as the number of connections that can be made is increasing geometrically between content, users, apps and activities. And in many ways, this is just the beginning. As we advance further into the Internet of Things we will see an explosion of data availability, with data being broadcast from every addressable entity – humans, nature, vehicles, machines, factories, drones, sensors, ad infinitum. Collecting any of these will soon be trivial – just an attachable service  – but the consequences of bringing the data into the enterprise will be costly and not necessarily useful…

enterprises start collecting data because they can. Data pours in, collected in increasing quantities. Data analytics machines commence work, create graphs, tables, correlation and dashboards, and yet the outcomes remain uncertain. We crunch data streams but miss out on assessing the value of each data stream. We need to be more thoughtful, analytical and definitive going in, before we enter into large scale data collection. This requires assessment of the underlying data structures of the enterprise and establishing models and perspectives on what outcomes they could yield, before big data collection begins.

Five Rules For Big Data

1)Understand your enterprise

2)Model your enterprise

3)Model your actions

4)Implement the data analytics plan

5)Roll back collection of useless data

You can read the full article on Information Management at Big Data, Little Happiness

Honestly, reading that article made me think about some of the problems that records managers face.  We may have high quantities of records, but still little happiness as over-retained records can lead to high risk of indictment.  The rules for big data also correlate to the rules for records management.  These are my five rules for records management

1)Understand your enterprise

2)Audit your information flow

3)Model your plan for retention and disposition

4)Implement the records retention schedule

5)Roll back collection of non-records