Business intelligence and data analytics: you’ve heard the terms thrown around. But is there a difference? And if so, what is it? Read on to find out.
You probably know that business intelligence and data analytics are vital to the running of modern business. Confusingly, though, the terms are often used synonymously, begging the question: are they the same thing? While the short answer is no, they share many similarities, which is where the confusion lies.
In this post, we introduce the concepts of business intelligence and data analytics before diving into their differences. We’ll start with a quick definition of each and then explore their distinct features.
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- What is business intelligence?
- What is data analytics?
- What’s the difference between business intelligence and data analytics?
- Business intelligence vs. data analytics: FAQs
- Key takeaways
Ready to demystify business intelligence and data analytics? Let’s dive in.
1. What is business intelligence?
Business intelligence (BI) is a collection of methods, systems, and tools that convert unprocessed data into valuable insights. These insights help businesses make better strategic, tactical, and day-to-day decisions.
This is a good start, but let’s be honest: it’s a bit wordy, and if you’re not into all the buzzwords, it doesn’t get to the core of the matter! So let’s break it down.
In its broadest sense, business intelligence has two definitions. First, it describes the strategies, technologies, and tools that companies use to obtain (and present) business insights. Second, it describes the output of this process, i.e. the insights themselves.
This is a subtle but necessary difference. When talking about business intelligence, you should always be clear about whether you’re referring to the process or the outcome.
Returning to the first definition—the process of obtaining business insights—business intelligence includes tactics and tools such as:
- Real-time monitoring
- Dashboard development and reporting
- Implementation BI software, like Power BI
- Performance management
- Data and text mining
- Data analytics
While this list isn’t exhaustive, it highlights how varied the processes and tasks of business intelligence can be. And while data analytics is a single tool within BI (and an important one, for that matter) it’s ultimately just one piece of a much larger puzzle.
What is the purpose of business intelligence?
Most will tell you that the purpose of business intelligence is to improve an organization’s strategy and decision-making.
And this is true. However, business intelligence ultimately boils down to something else: profit. While some might disagree, we operate in a capitalist system, so there’s no escaping it—money talks! How this pursuit of profit looks, however, is varied. And it always depends on the individual case. For instance, a toy company might use BI to improve its Christmas sales strategy, whereas a social media company might be more interested in using it to find ways of increasing ad clicks.
Regardless of the industry, company, or objective, business intelligence ultimately focuses on increasing profit through improved operations. In general terms, this means it uses metrics that inform how an organization runs, from supply chain data to sales revenue, profit margins, staff attendance, and so on. The clue’s in the name: it’s all about business!
2. What is data analytics?
Data analytics is the process of collecting, cleaning, inspecting, transforming, storing, modeling, and querying data (along with several other related tasks). Its goal is to produce insights that inform decision-making—yes, in business—but in other domains, too, such as the sciences, government, or education.
Sound familiar? It’s no surprise that this seems similar to business intelligence—there’s a great deal of crossover between the two. However, in its purest sense, data analytics focuses on the nitty-gritty aspects of the analytics process. Although often used in a business context, it is not exclusively a business intelligence tool.
In addition, while data analytics often incorporates presentation features that are also common to BI (such as dashboards and custom reporting) many of these are not fundamental aspects of the process itself. It’s better to think of them as useful add-ons.
What are the different types of data analytics?
Considering data analytics as a technical discipline, we can divide it into four broad categories. In a nutshell, these are:
- Descriptive analytics, which provides an objective, fact-based description of what has happened in the past, i.e. ‘A’ occurred.
- Diagnostic analytics, which not only focuses on what happened in the past but aims to understand why, i.e. ‘A’ occurred because ‘B’.
- Predictive analytics, which uses past data to forecast trends, i.e. because ‘A’ occurred, we predict that ‘C’ will occur in the future.
- Prescriptive analytics, which aims to provide actionable steps towards a chosen goal, i.e. To achieve goal X, we must take action Y.
This is a quick overview, of course. For more detail, check out this guide: What Are the Different Types of Data Analysis?
In its rawest form, everything that data analysts do—from collecting and parsing data, to building databases and carrying out various analyses—is focused on achieving one of these four goals. Data analytics is all about turning raw data into useful insights and mastering the technical tools required to do this.
3. What’s the difference between business intelligence and data analytics?
As we’ve seen, data analytics and business intelligence seem pretty similar, right? It’s okay if things are a bit confused at this point. The internet is awash with people getting the terms mixed up, so you’re not alone!
To help matters, in this section, we highlight some broad differences between business intelligence and data analytics. Let’s jump right in:
Using insights vs. creating insights
- Business intelligence’s primary purpose is to support decision-making using actionable insights obtained through data analytics.
- Data analytics’ primary purpose is to convert and clean raw data into actionable insights, used for many purposes, including BI.
Backward-looking vs. Forward-looking
- Business intelligence is primarily concerned with looking back to see what has already occurred, using this information to inform future strategy.
- While data analytics also identifies past patterns, it often uses these data to forecast what might occur in the future (see ‘predictive analytics’ in section 2).
Structured vs. unstructured data
- Business intelligence utilizes structured data, i.e. data stored in warehouses, tabulated databases, or other systems. These data are used to produce dashboards and reports.
- Data analytics also uses structured data, but usually begins with unstructured, real-time data. One task of data analysts is to clean and order these data, before storing them for future analysis.
Want to learn more? Check out: Structured vs. Unstructured Data: What’s the Difference?
Non-technical users vs. Technical users
- Business intelligence is primarily used by leadership teams and non-technical personnel, such as chief executives, financial directors, or chief information officers.
- Data analytics is usually the preserve of analysts, data scientists, and computer programmers who have a more technical focus.
Big picture vs. narrower focus
- Business intelligence usually thinks in ‘blue sky’ terms, asking high-level strategic questions about an organization’s overall direction.
- Data analytics tends to focus on a single issue or question, e.g. ‘Why are sales on product A dropping, despite positive reviews?’
Tidy(ish!) vs. messy
- Business intelligence relies on clear dashboards, reporting, and other monitoring techniques to relay insights in a clear, easily consumable way.
- To obtain insights, data analytics gets ‘under the hood’ with data, carrying out tasks like data mining, algorithm development, modeling, and simulations.
As you can see from this list, there are some clear differences between business intelligence and data analytics. However, we should highlight that these are not hard and fast rules, but general guidelines.
For instance, data analytics doesn’t always focus solely on making predictions, and business intelligence might also involve tasks such as data mining. These blurred lines go some way toward explaining why the terms are so often used interchangeably.
One key takeaway
If you take nothing else away from this article, remember this: business needs data analytics, but data analytics does not need business. Okay, so what exactly do we mean by this?
In short, business intelligence relies heavily on data analytics. It cannot function without it. Conversely, data analytics—while heavily used in business—functions quite well without business data. It’s simply a useful tool that businesses have adopted. While BI is now one of the most dominant ways in which data analytics is used, it’s applicable in many other fields, too.
4. Business intelligence vs. data analytics: FAQs
Next up, let’s answer some of your burning questions:
Is data analytics the same thing as business intelligence?
Data analytics is a necessary tool for business intelligence. However, it is not the same thing as business intelligence. The latter uses a range of strategies and tools, of which data analytics is just one. In fact, data analytics is likely the most fundamental business intelligence tool there is.
On its own, however, data analytics aims to deal dispassionately with data. It only concerns itself with answering the specific question at hand. While this might mean driving profit (the main objective of business intelligence) when applied in this context data analytics is also used in other, non-business-related fields (such as the sciences or in software development).
What’s the difference between a data analyst and a business intelligence analyst?
A data analyst’s job is to uncover patterns in data and to produce actionable insights. When used as a business intelligence tool, it naturally follows that these insights are business-related.
However, this is simply a by-product of data analytics’ usefulness—data analysts are not necessarily business experts by nature (although they can be). Instead, they’re primarily technical and mathematical specialists, capable of coding algorithms, conducting statistical analyses, and coding with programming languages like Python.
Meanwhile, business intelligence analysts primarily focus on the operational side of things. While they often have technical know-how, their primary skills lie in areas like strategy management, persuasion and communication, leadership, commercial awareness, and other business-domain-related areas.
What skills do I need to become a data analyst?
At a minimum, data analysts usually require an undergraduate degree, ideally in a field related to data, statistics, computing, or similar. Failing this, it’s also possible to land a job with an undergraduate degree in any subject, so long as it’s complemented by a data analytics certification (there are many options available, check out some bootcamps!)
Entry-level data analysts tend to require the following broad skillset:
- Strong mathematical knowledge, especially in probability and statistics
- Good time management and communication skills
- Problem-solving and critical thinking
- Data collection, data warehousing, and data cleaning skills
- Knowledge of relational databases and other common IT systems
- Basic knowledge of SQL, Python, and MS Excel
- Awareness of business intelligence platforms such as Power BI, Tableau, or Qlik
What skills do I need to work in business intelligence?
Business intelligence analysts require many of the same technical skills as data analysts. However, with their more strategic focus, they also need a high degree of business acumen. As such, their skills should include:
- BA or MA in business management or similar
- Data analytics (including an awareness of the skills described above)
- Knowledge of a wide range of BI platforms and tools
- Financial management expertise, e.g. budgets and accounting
- Project management expertise, e.g. PRINCE2
- Knowledge of software development methodologies, e.g. Agile
- Exceptional communication and interpersonal skills
- Delegation and time management
- Database management and IT security
5. Key takeaways
In this post, we’ve tried to demystify the complex differences between data analytics and business intelligence.
Although we’ve focused on what differentiates them, in reality, you’ll find that the distinction is not always clear-cut—both are evolving fields. Many data analysts have a fascination for how businesses operate and are carving themselves a niche in this area. Likewise, many business leaders now appreciate how data analytics can help shape their long-term operational strategies.
In this post, we’ve learned that:
- Business intelligence describes the strategies, technologies, and tools that companies use to obtain business information. It also describes the information itself.
- Data analytics is the technical process through which we obtain actionable insights from raw data. It is one tool within BI.
- Business intelligence takes a ‘big picture’ approach, utilizing existing insights to explore what has happened in the past to inform future decision-making. It primarily relies on structured data that is digestible for non-technical users.
- Data analytics takes a narrower focus, using data to solve specific problems. It’s commonly used to predict future trends and usually begins with unstructured data that needs careful parsing and cleaning before it can be stored and analyzed.
- The lines between business intelligence and data analytics are constantly shifting and blurring. This goes a long way to explaining why the terms are so often used interchangeably.
So there you have it: business intelligence vs. data analytics. Has it helped you decide on a discipline that interests you? If you’re ready to learn more, dive deeper into data analytics with this free, 5-day data analytics short course, or read the following introductory articles: