This is still the #1 question I get from many data warehouse and business intelligence folks. I use to show Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each) and Figure 2 (Business Intelligence vs. Data Science, which shows the different types of questions that each tries to address) in response to this question. However, these slides lack the context required to satisfactorily answer the question – I'm never sure the audience really understands the inherent differences between what a BI analyst does and what a data scientist does. The key is to understand the differences between the BI analyst's and data scientist's goals, tools, techniques and approaches. Figure 3 outlines the high-level analytic process that a typical BI Analyst uses when engaging with the business users.

Lendi is Australia's fastest-growing Fintech business and we're building a technology-enabled platform to take the hard work out of home loans. We're passionate about how technology can revolutionise our industry and solve a key pain point in peoples' lives. In an ever-changing regulatory environment, Lendi Group is leading the charge in developing industry-first technology designed to offer Australian homeowners transparency, simplicity and convenience in their home loan experience. Engineering is forefront in this charge as we look to automate and build out scalable and reliable systems to support our customers and counterparty banks. Lendi is looking for an experienced BI Analyst to join our BI team for an initial 6 month contract.

Today, organizations that are using data to uncover opportunities and are applying that knowledge to differentiate themselves are the ones leading into the future. Whether looking for patterns in financial transactions to detect fraud, using recommendation engines to drive conversion, mining, social media posts for customer voice or brands personalizing their offers based on customer behavior analysis, business leaders realized that data holds the key to competitive advantage. To get value from data, you need a vast number of skill sets and people playing different roles. In this article, we're going to look at the role, BI analysts play in helping organizations tap into vast amounts of data and turn them into actionable insights. It all starts with a data engineer.

The business intelligence (BI) landscape is changing. Traditionally, BI analysts used dashboarding and analytics tools like Microsoft Excel, Microsoft Power BI and Tableau. However, analytics tools are rapidly evolving, and BI analysts are expected to evolve alongside industry advancements. In particular, predictive analytics used to be in the domain of more technical employees, but today, no-code automated machine learning (AutoML) tools mean anyone can deploy AI. Looking at BI Analyst job offerings, we can see that many now ask for AI skills because they want someone who can deploy predictive models that actively impact the company, instead of passive analytics like dashboards.

I'm reposting this blog (with updated graphics) because I still get many questions about the difference between Business Intelligence and Data Science. I recently had a client ask me to explain to his management team the difference between a Business Intelligence (BI) Analyst and a Data Scientist. I frequently hear this question, and typically resort to showing Figure 1 (BI Analyst vs. Data Scientist Characteristics chart, which shows the different attitudinal approaches for each)... But these slides lack the context required to satisfactorily answer the question – I'm never sure the audience really understands the inherent differences between what a BI analyst does and what a data scientist does. The key is to understand the differences between the BI analyst's and data scientist's goals, tools, techniques and approaches.