UK Data Service organised a #DataImpact event with key data innovators in Scotland who shared their knowledge and vision around data, the future of data and how data can and will impact our lives and our socio-economic and political environments in the future.
At UrbanTide we are all about data, specifically we are about open data, thus we were delighted to listen to and mingle with data and open data enthusiasts during the Data Impact event.
Measuring the impact of data is challenging; the definition of impact is demonstrable contribution to society and economy. Impact is multilayered with instrumental impact, conceptual impact, capacity building and skill development.
#1 Step up so that the re-use of data is built into the data collection process from the beginning.
#2 Start looking at predictive analysis and start linking data together across data services - free the data from the silos!
#3 Maximise data's impact:
Data needs to be accessible, shareable and of good quality
People need to trust the way it is handled
We need skills to use the data
Public sector organisations need to adopt more data-driven business processes and go from overly cautious to securely sharing
#4 Having the right skills is key in bringing insights to the community and unlocking the potential of data.
#5 FoI should not be about the right to ask but the right to know, however the open data journey is often hindered by a hugely complicated legislative framework.
#6 Often when public sector leaders ask what the right data is to publish, they don’t understand that it is not about tech it is about business.
#7 Your data will never be perfect, so just start where you are.
#8 Soon data will be moved out of the silos and different data will be brought together to produce refined data structures.
#9 The data industry is still a cottage industry. Generally, highly skilled analysts spend 80% of their time gathering data. The data value chain needs to be industrialised and moved towards automation and standardisation with upstream processes improved.
#10 Main challenges of data:
Validity of data
Biases, propagation and error
Data quality issues of the future