Before Obama's election as President he promised many things to the citizens of America, one being that if elected, his government would;
‘harness technology to confront the biggest challenges that America faces’
If this was the message being portrayed back in 2009, we take a look at what the UK government do or can do to harness the same methodology in 2019 onwards.
To harness technology in decision making, this can be achieved through many different avenues. A household name is predictive analytics. HBR explains predictive analytics in a simplistic way-‘no one has the ability to capture and analyse data from the future. However there is a way to predict the future using data from the past. It’s called predictive analytics and organisations do it every day’. Predictive analytics uses historical data to predict future events, there are two types; classification and regression. Classification involves training a learning algorithm to correctly separate examples into predefined categories or classes, giving it the ability to correctly predict the categories of new examples. Whereas regression is a learning problem where the algorithm is interested in predicting a single real value number. We see these models used everywhere in the business world from a recommendation system to fraud detection and image recognition. But how can this be implemented in UK Government?
Predictive analytics involves understanding what your research goal is, the acquisition of data, the preparation of data and statistical analysis to allow data exploration and modelling to give you a better understanding of the world around you.
In UK Government, data is being collected for everything, from birth rates, traffic congestion and flooding to exporting, poverty and immigration. We are now seeing more investment into ensuring the correct data is being recorded and now the attention has turned to see how best we can utilise this data.
Within the police department, huge amounts of data are collected from 999 calls, using predictive analytics, the police department can assess different attributes that affect crime and can react accordingly. The process involves recording the call and then taking different attributes into consideration, for example seasonality, weather, time of day, city events and paydays to accurately predict criminal behaviour patterns. The police can react by patrolling specific areas more frequently or through public awareness.
In 2017, official figures released showed that 17,243 companies entered insolvency, this is a huge cost to an individual and the economy. Predictive analytics can be used to predict the insolvency risk. Using a model like a decision tree which takes particular attributes into consideration like credit rating, loan application, customer data, time series predictions and detection of insurance claim fraud, we can then predict financial distress.
Department for Work & Pensions
The cost of unemployment is high, with the UK government spending £264 billion on welfare in 2017. Predictive analysis is starting to be implemented to understand those who are likely to receive some form of welfare and how this could be reduced. This preventative method uses predictive analysis and evaluates attributes like marital status, socioeconomic status, lived outside UK, worked outside UK, education and criminal background in order to assess those more vulnerable, and strategies can then be used to target these individuals.
The use of predictive analytics in government has so many more possibilities and Anaeko believe that data should be the backbone of all decisions. We specialise in integrated analytics services to accelerate decision making. We deliver intelligent reports driven by machine learning and domain knowledge to enable you to make the right decisions. If you are curious about data or just want to learn more about how we can help your organisation, just talk to our team! Find out more around our analytics services and analytics as a service: Integrated Analytics
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Topics: Data Analytics, Public Sector, Data governance, Government, Machine learning, Predictive analytics, Analytics as a Service, Analytics Services