Both “big data” and “analytics” are popular keywords in the Information Management domain and many organisations are trying to work out how to harness the increasing volumes, velocity and complexity of data in a world of constant change and disruptive technologies.
My three takeaways from his presentation:
1. Data is becoming a service or a product
Data is not the same as information. This resonated with me. At Altis we refer to the information value chain of data -> information -> insight. A decision is made on information, not the data alone. By first identifying what data is available and can be combined, informed decisions can then be made about improving operations, in turn enabling services to be more efficient and harnessing the power of data, information and insight to design and deliver services in new ways.
2. Small, simple data can still be useful
Simple data sets can still provide interesting and valuable insights. A sensor capturing on/off may seem like a very simple metric but when examined in terms of time, location, frequency – patterns may emerge that provide different understandings and can be used to influence behaviours or optimise service delivery. A number of governments are now establishing Data Analytics centres of excellence and embracing analytics – such as the NSW Data Analytics Centre and New Zealand’s Data Analytics agency. Case studies presented by Opperman included transport optimisation to improving fire and rescue responses to targeting resources where they are needed most at the right place and the right time.
3. You need to ask the right questions
With the proliferation of data available it is tempting to capture as much as possible, just in case it proves useful or valuable in the future. Without a doubt, the ability to digitize, link and model data in enabling powerful predictive model and delivering optimisation of service delivery – but fundamentally we still need to find the right questions to ask. Have a clear purpose in mind and use an iterative approach to progressively refine the analysis rather than being driven by the data available first.