Using Data To Make Decisions With Frontier Economics

We discuss the process behind data driven decision making with Rob Woodhard from Frontier Economics.

In the sixth event of Data Driven Guernsey Week, we heard from Rob Woodhard, Consultant at Frontier Economics about how to make evidence-based decisions using data. His session offered guidance on how to generate insight from datasets, including short introductions to different approaches for analysing and visualising data. The full webinar including the Q&A can be viewed at the bottom of this article.

How do you use data to generate insight that can then form part of an evidence-based and informed decision-making process? Organisations can gain so much value by collecting and analysing data, to shape their core business models and improve decision-making. Multitudes of data can be collected and used to make decisions in all facets of society, from governmental decisions to help the population and upskilling their staff, to businesses planning their business journey, regulators, and even consumers using data to price check items and make decisions on when and where and who they want to spend money with. 

At Frontier Economics they look to deliver insight in two ways, by identifying and visualising data to detect real-world patterns in the business sectors and the economy, explaining this data and making predictions using statistical analysis to understand the cause of events. Frontier Economic help businesses understand the real drivers of outcomes that matter using econometrics, machine learning and optimisation modelling. This kind of decision-making process is vital to innovation within businesses, from founding to scaling and beyond, but how do you take advantage of and then wade through the sea of data to be collected and pinpoint vital information without falling down many, many rabbit holes? Rob suggests, keeping your analysis as simple as possible with a well thought out framework and timeline. By questioning the information you want to gather and narrowing that search you are more likely to find the answers that will be most useful.

Watch the webinar below for some great visual aids and examples expanding on best practices for displaying data.

"Demand for data scientists in the UK over the last 5 years has increased by 231%."

Rob Woodhard, Consultant at Frontier Economics

Rob's tips for managing data work well,

1. At the start, think about the wider framework you want to follow and the questions you want answers to, then try to break that aim/ question down into more detailed questions or hypothesis to test using that conceptual framework. This will help you to streamline your analysis and keep it focused on the important things and manage the scope of the project if you are working to a deadline.

2. Keep your analysis as clear and simple as possible, the insights will be a lot more helpful if they are easy to understand and explain. Think about the insight you want to get, and let that drive your analytical approach.

3. Pick a suitable program for analysis!

4. Spend enough time interrogating, cleaning, matching data. If there is a mistake in the raw data that then flows through the analysis stage into your results, and you reach a wrong conclusion due to this simple mistake it can be very time-challenging and intense to go back and unwind the entire process and start again.

5. Interpret results at all stages, build in cross-checks throughout your process to pick up any errors using internal and external validity.

Watch the webinar in full below

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