[A guest post from David Johnson, Data Science Campus Start-up Manager]
“[Better use of data] has the potential to transform the provision of economic statistics, ONS will need to build up its capability to handle such data. This will take some time and will require not only recruitment of a cadre of data scientists but also active learning and experimentation. That can be facilitated through collaboration with relevant partners – in academia, the private and public sectors, and internationally.”
– Independent Review of Economic Statistics, Professor Sir Charles Bean, 2016, p.11
In 1971, the American psychologists Richard E. Nisbett and Edward E. Jones published their ‘Actor-Observer Bias’ theory. They proposed that we assign different causes to actions depending on whether those actions are carried out by ourselves, or by other people. For example, when we act in a certain way, we may explain our actions as being reactions to things that happen to us (I’m grumpy today because I missed my train and was late getting to work, but I’m actually a really happy person), but when we observe those same actions in others we assume them to be inherent traits (you are grumpy today because you are a very grumpy person).
I’m a big fan of the website and my own train journeys are often filled with their podcasts. I heard a recent interview with one of their data journalists, Harry Enten, who when asked about his biggest mistake said it occurred when data didn’t fit his model and he chose to ignore it, unable to overcome his own bias.
If data professionals like Enten can fall foul of his own biases, how can the rest of us overcome this tendency and other challenges when it comes to the use and understanding of data? I recently had the opportunity to Professor Richard Nisbett speak about some of these challenges at the London School of Economics. He argued that we misunderstand data because we are not equipped with the right tools to understand it, joking that the way in which statistics is currently taught is designed “to prevent its extension to everyday life”.
The Bean Review concluded that measuring the modern economy requires new approaches and new tools. Across ONS a number of initiatives are underway to develop these, and the one that I’m spending all my time on (when not missing my train or listening to podcasts) is the new Data Science Campus at our headquarters in Newport. Launching later this year the Data Science Campus will establish a centre for Data Science and Data Engineering, bringing together Analysts, Data Scientists and Technologists from across the UK and the wider international community.
It will act as a hub for the whole of the UK public and private sectors to gain practical advantage from increased investment in data science research and help cement the UK’s reputation as an international leader in this field. By partnering with academia, industry and other areas of government, ONS will develop a greatly enhanced range of measures of the economy and society, so that emerging issues and trends can be spotted more quickly, understood in greater detail, and so that decision making can be better informed.
The goal of the centre will be to build a new generation of tools and technologies to exploit the growth and availability of innovative data sources and to provide rich informed measurement and analyses on the economy, the global environment and wider society.
I’m delighted to be a part of this journey. But am I happy because of the journey, or am I basically just a very happy person?
I think we need more data points to answer that.