In the real world things move much quicker than your data, but the business needs answers now. How do you use incomplete information to deliver actual business value?
Join hundreds of practitioners and leaders like you with episode insights straight in your inbox.
Checkout our brands or sponsors page to see if you are a match. We publish conversations with industry leaders to help data practitioners maximise the impact of their work.
Your ideas help us create useful and relevant content. Send a private message or rate the show on Apple Podcast or Spotify!
Today I speak with Francesco Scoto a senior Business Analyst with a decade of experience across industries including The Iconic here in Sydney.
We start from the different types of data analysis and dive into a typical process/workflow. Business analysts start from raw data and create models based on their understanding of the link between numbers and the physical world. Great analysts connect with different parts of the business to gain as much domain knowledge as possible, which in turn allows them to make more accurate assumptions and be faster and more accurate. Results are perceived differently depending on whether they confirm or challenge a prior belief. This is largely due to confirmation bias, one of the cognitive weaknesses that we all share. This is healthy when it brings about dispassionate scepticism, but it can be frustrating and indeed toxic when it is dogmatic.
We find that data access is one of the most frustrating parts of analytics. Teams often underestimate the cost of moving data around, and requests from business analysts are often perceived as a tax on engineering time, already busy building features and managing databases. One of the jobs of a data analyst is to ensure that data is correct, and make reasonable assumptions to fill the gaps when data is missing or incomplete. We talk about the need to reconcile what happens in Excel with a curated source of truth, data tiers, and systems that could make it easier to understand how much we can trust the data we are looking at.
Most data users want the right information at the right time and are not interested in how a metric is defined. But an increasing number of data visualisation tools allow to define, use, and share metrics easily. This empowers people to take initiatives, but it can lead to duplication and inconsistencies, especially in the absence of an effective data management strategy.
Analytics can be frustrating but it can be a really satisfying job. It's important to have the right resources and the right people in the room, as well as identify owners to monitor the project alignment with the business goal.
You can follow Francesco on LinkedIn.