Written by Business intelligence and data analytics have a lot in common, but ultimately, they are separate disciplines. Read the article to find out the difference.
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“Business intelligence” and “data analytics” are often used as if they’re the same job description, but there is a difference. Getting the distinction straight is the first step to recognising which one you need, and to hire and brief for the right one.
Intelligence vs analytics
Let’s start with looking at the original meanings of intelligence and analytics. These map onto the business terms more directly than you’d expect.
Intelligence has two senses that both matter here. The general one is “the capacity of mind… to understand principles, truths, facts or meanings, acquire knowledge, and apply it to practice.” The narrower one, borrowed from military and political usage, is “information, often secret, about an enemy or about hostile activities” gathered by a dedicated agency or unit. Strip out “secret” and “enemy” (maybe lose “hostile”) and replace them with “your market” and “your competitors,” and you’ve got the shape of business intelligence: a function whose job is to gather what’s happening and turn it into something a decision-maker can use.
Analytics traces back to Ancient Greek analutiká, “science of analysis,” and is defined as “the discovery, interpretation, and communication of meaningful patterns in data.” Its root, analysis, comes from the Greek for “to unravel, investigate”: decomposing something complex into its components in order to study it.
The etymology is already telling. Intelligence is about gathering and applying knowledge to a decision. Analytics is about unravelling information to find patterns inside it. One is oriented toward action; the other toward discovery. And not so surprisingly, it is the actual division of labour between the two disciplines in a modern business.
What is business intelligence
Business intelligence (BI) is the set of technologies, processes, and dashboards that turn a company’s operational data into reports leadership can read and act on. It is something we regularly deliver when building systems to decision makers, for example our sales and budget reporting suite for Hatch Mansfield (a UK wine distributor), or subscription growth and engagement monitoring dashboards for Kinship (a Mars Inc. affiliate). These are built on structured data pulled from systems that are already in place (CRM, sales database, finance system), and it’s built for people who don’t need to know SQL.
Example. Our client Hatch Mansfield needs to comply with government regulations (Extended Producer Responsibility in specific) around wine packaging. If they fail to provide the relevant information they get taxed in a higher bracket. We build a dashboard that monitors and highlights missing information per product and categorises missing parameters according to legal requirement levels. The product manager can open the dashboard and immediately see which products are outstanding divided by urgency. He saves a lot of time and effort, because he can immediately chase up suppliers to obtain the most urgent information. He does not need to manually collect information and set up outstanding priorities. That’s the gist of BI: fast, grounded, and built for a non-technical reader making a call using available information.
What is data analytics
Data analytics is the technical work of extracting insight from data, often data that isn’t clean or structured yet, using statistics, modelling and code. It splits into four types, ascending in difficulty: descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what should we do about it). Unlike BI’s fixed dashboard, a data analytics project is usually built to answer a specific and often highly sophisticated question, and the people doing it are analysts and data scientists writing code.
Example. Another client of ours wanted to measure public sentiment towards specific public issues using national surveys. We set up a statistical model using sampling from earlier survey data and outcomes publicly available and tried to predict how people will react to future events of similar nature. In this case, it is predictive analytics because we are interested in what is likely to happen in the future based on past behaviour. Of course, national sentiment is quite large and overarching, but the same techniques can be used in predicting the success of an ad campaign or deciding which will be the most popular features of a new product (and many, many more of course).
So what is the difference
The two examples above share similar fundamentals (utilising data), but are still two different jobs.
Differences:
- Time orientation. BI is backward-looking: what happened, what’s happening now. Data analytics reaches forward too, into prediction and prescription, not just description.
- Data state. BI runs on structured data that’s already been modelled into a warehouse. Analytics often starts from messier, less structured data and does the modelling as part of the work.
- Audience. BI is built for a non-technical reader to self-serve. Analytics output goes to whoever needs the specific answer, and its frequency is also less stable (regular checks v. one-off answers).
- Skillset. BI leans on business acumen, data modelling and dashboarding tools (Power BI, Tableau, Looker). Analytics leans on statistics, coding and hypothesis-testing.
Similarities:
- Both exist to produce an actionable insight that changes a decision, not analysis for its own sake.
- Both draw on the same underlying data, but at different stages (an analytics model often feeds into a BI dashboard).
- Both increasingly use the same tooling; the line between a “BI tool” and an “analytics tool” has blurred as platforms add statistical and predictive features on top of dashboards.
- Neither is worth much without the other, which is the actual point of this article.
How data analytics helps business intelligence
Business needs analytics; analytics doesn’t strictly need business. Data analytics can run inside a university, a research lab, or a government agency with no commercial context at all. But when it’s aimed at a company’s operations, it quickly becomes a core pillar of BI and decision-making.
BI dashboards build on data and analytics. Consequently, they are only as good as the pipeline and the metric definitions behind it. Even seemingly small things like handling missing values benefit from data analytics and engineering work. Whilst you might not have to spend expensive resources on answering these questions, they can significantly improve outcomes and highlight any gaps or shortcomings in business processes.
Analytics also extends what BI can say. A dashboard built on clean and organised data can still only tell what happened so far and how things are. A predictive model, built with the analytics discipline can also tell you what is likely going to happen and will help decision makers to make preparations accordingly.
Why you should consider having an analytics team
If BI depends on analytics to be trustworthy, the practical conclusion is that you need an expert technical team to support the strategic decision makers (unless your business case is too simple or new to benefit from analytics meaningfully). A dashboard with no analytics behind it gives you little benefit. But the other way around, you also need technical experts that understand the business and are aligned on the goals and vision.
A professional team that covers both aspects is the best choice: engineers who are capable of building analytics pipelines and models, but also have the skills to manage the business stakeholders and cater to their needs without friction. That’s the difference between a regular data function and what FloreData offers.
We marry the technical know-how with creativity, superb communication and business acumen for our clients:
- Data engineering to get the pipeline and the metric definitions right
- Business intelligence and dashboards to get the right answer in front of the right person
- Analytics and AI capability to answer the harder, non-recurring questions underneath
If your reports and your predictions currently live in different, disconnected corners of the business, or you don’t get much out of your BI reports, give us a call. Be assured, despite being highly technical, we are a joy to work with!
Not sure whether you need a BI dashboard, an analytics project, or both? Start a conversation and we’ll help you figure out which one actually answers your question.
