Data science has been a stardust activity since around 2014 when interest in the term started to climb from a ten-year flatline (scoring six or seven on Google Trend’s interest over time index) to reach a maximum 100 rating in September of 2019. For those involved and able to appropriate the term to their skills set, that stardust has been golden with salaries reflecting rising demand and limited supply.
The last five years have also given data scientists a golden period during which to hone those skills and demonstrate their value. Optimisation has been the watchword for this function, helping brands to do more, faster and better by leveraging big data to find predictive patterns.
All of this is about to change as we head into a down economy - in the UK due to Brexit, in the US because of its trade wars and in China because of the slowdown in demand from the UK and US, for example. What works in a growth economy seldom has traction when consumer behaviours switch from discretionary spending to defensive saving.
In the business realm, the next five years will be all about rationalisation - working out how to achieve the same with less. Some of this can look the same as optimisation, such as when stretching a marketing budget through constant incremental gains, and there are areas of activity that have already been under the data science lens for some time. Supply chain management and logistics are a good example, such as where marginal savings from optimised delivery routes can add up to million pound savings.
But rationalisation also means cutting resources - in particular the human kind. The core data science areas of automation and artificial intelligence are essentially about replacing tasks carried out by people with ones carried out by machines. With the sparkle of growth and progress on them, these have looked to date like building the future.
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