Making Decisions with Data Analytics

🧠 Decision Making 🧠

Data analytics aids in making data-driven decisions by revealing trends and patterns hidden in raw data. Think of it as having a crystal ball that shows you where your business should head next.

For instance, during the 2016 US Presidential Election, data analytics played a crucial role in campaign strategies. By analysing voter data, campaigns could target specific demographics more effectively, ultimately influencing election outcomes.

But it’s also easy to see what we think is a relationship and assume that it will always be true.

Deeper analysis is typically required as well as domain knowledge and understanding.

Which comes first – analytical skills or domain knowledge?

For me it was analytical inquisitiveness – wanting to solve problems and make things better. Then I would throw myself into the domain and gain a deep understanding. This was born through research and asking questions from domain experts: people who also had an analytical and data driven approach but also knew the subject matter very well. Specialising in these domains then led me to gain better insights.

I see different approaches in the companies that I work with.

For example, a Center of Excellence strives to enable domain experts with best practices and the skills to use tools fluently themselves, whereas a centralised BI department is a development team who works to understand the business domains well enough to create data products that can be used by the subject matter experts.

There are many factors that influence the deployment approach but what is essential for success, is the ability for organisations to be both reactive and proactive fluently, to drive better decision making, quickly.

And this often requires a balance between advanced dashboards for complex analysis and quick, ad-hoc solutions that enable root cause analysis, that has not been predetermined, easy to produce by or for the domain experts.

💡 “The best decisions are data-informed.”

🌟 What tools, techniques and approach do you rely on for data-driven decision-making?