What is the difference between business intelligence and data analytics?

In conversations about big data and how it can inform your business practices, two terms are commonly confused. These are business intelligence (BI) and data analytics. Let’s look at each of these terms and offer definitions, and hopefully arrive at a description of how they relate to one another.

Business Intelligence: Informing Brand Direction and Decision-Making

Business intelligence is an umbrella term for the strategies and methods applied to marshal business data and sales information to inform data analysis. A business intelligence consultancy might offer a range of strategies, including AI, machine learning, sales performance benchmarking, CMS analysis, competitor analysis, and other methods.

These strategies aim to gain a competitive advantage by applying science to what would otherwise be educated guesswork. It’s akin to modern warfare, where AI systems and drones monitor possible hot zones, and risk analysis is performed. Nobody pushes a red button without a host of information about how likely the strategy is to succeed.

Data Analytics: Deriving Concrete Insights from Undifferentiated Data

In 2022, even the least technologically advanced company has access to vast pools of data, even if it’s only credit card data, sales receipts, product inventories and website hits.

Data Analytics is a scientific field devoted to dredging these vast reservoirs of data to derive actionable insights from them. Put another way, it’s a sub-field of business intelligence or one strategy amongst many to obtain a competitive advantage.

Data analytics uses algorithmic processes to detect patterns in data which can demonstrate trends, problems, and opportunities. A data analytics consultancy may use machine learning to aid this process by first training an AI on data which has already been analysed, then setting it loose on a much bigger data pool to perform pattern recognition.

Before data analytics can be used, the data usually has to be cleaned, structured, and prepared for analysis. Fortunately, algorithmic methods exist to do just that, reducing the person-hours required to deal with millions or even billions of bits of data.

How Data Analytics contributes to Business Intelligence

As we’ve seen, data analytics is one key method among several that can contribute to business intelligence. It’s important to add that, even when recurring patterns are spotted within the data, it can still be challenging to figure out what these patterns mean.

Business intelligence can cross-reference information from various sources to turn patterns into insights. For instance, let’s say you identify a pattern of customer drop-off for a digital product. Customers use it assiduously for a couple of weeks; then, their interest tails off. The data might demonstrate this incontrovertibly, but you still need to know why.

Techniques including customer surveys, data scraping consumer review sites, competitor analysis and focus group feedback might be used to gain deeper insight. Using all the business intelligence tools at your disposal, you’ll come to understand what’s turning customers off, and then derive strategies to address the problem.

For more insights into the complementary fields of data analytics and business intelligence, why not browse the other articles on our site?