You’ve probably heard the terms business intelligence and data analytics used interchangeably. Is there a difference, though? If yes, what exactly is it? Continue reading to learn more.

You are undoubtedly aware of how important data analytics and business intelligence are to the operation of today’s businesses. However, the names are confusingly frequently used interchangeably, raising the question: are they the same thing? The misconception stems from the fact that they are so similar, even though the short answer is no.

Before delving into their distinctions, we first present the ideas of business intelligence and data analytics in this piece. We’ll begin with a brief explanation of each before examining their unique characteristics.

Introduction

What is Business Intelligence?

The term “business intelligence” (BI) refers to a group of techniques, frameworks, and instruments that transform raw data into insightful knowledge. Businesses can use this information to inform their daily, tactical, and strategic decision-making.

This is a good beginning, but to be honest, it’s a little wordy and misses the main point if you’re not into all the jargon! Let’s dissect it now.

There are two meanings of business intelligence in its broadest sense. It begins by outlining the methods, equipment, and technologies that businesses employ in order to gather and communicate business insights. Secondly, it explains the results of this procedure, or the insights in and of themselves.

This is a minor but important distinction. Always be clear about whether you’re discussing the process or the result when discussing business intelligence.

Going back to the original meaning, which refers to the process of gaining business insights, business intelligence includes strategies and resources like: 

• Real-time monitoring
• Dashboard development and reporting
• Benchmarking
• Implementation BI software, like Power BI
• Performance management
• Data and text mining
• Data analytics

This is not a full list, but it does demonstrate the variety of business intelligence procedures and duties. Furthermore, even while data analytics is one BI tool—and a very useful one at that—it is ultimately just one component of a much bigger picture.

The Purpose of Business Intelligence

Most will tell you that enhancing an organization’s strategy and decision-making is the goal of business intelligence.

This is accurate as well. But in the end, business intelligence comes down to one thing: money. We live in a capitalist society, and although some may not agree, there’s no denying that money talks! There is variation in the way this profit-driven endeavour appears. Additionally, each situation is unique. While a social media firm could be more interested in using BI to uncover ways to increase ad clicks, a toy company might use it to better its Christmas sales plan.

Business intelligence ultimately focuses on boosting profit through enhanced operations, regardless of the industry, company, or goal. This basically means that it makes use of metrics, such as supply chain information, sales income, profit margins, employee attendance, and so forth, to determine how an organisation operates. The name alone gives the game away: business is everything.

What is Data Analytics?

The process of gathering, organising, examining, modifying, storing, modelling, and querying data—as well as a number of other associated tasks—is known as data analytics. Its objective is to generate insights that guide decision-making not only in business but also in other fields including education, government, and the sciences.

Do you recognise this? Given how much these two fields overlap, it should come as no surprise that this appears to be similar to business intelligence. But data analytics in its purest form is concerned with the details of the analytics procedure. It is not only a business intelligence tool, despite being used frequently in a commercial setting. 

Furthermore, even while dashboards and bespoke reporting—presentation features that are also common to business intelligence (BI)—are frequently incorporated into data analytics, many of these elements are not core to the process itself. It is preferable to consider them as helpful extras.

Different Types of Data Analytics

We may categorise data analytics into four main groups when looking at it as a technical field. To put it briefly, these are: 

• Using descriptive analytics, one can objectively and factually describe historical events, such as when “A” occurred.
  
• Diagnostic analytics seeks to explain why something happened, i.e., ‘A’ happened because ‘B,’ rather than just concentrating on what happened in the past.
  
• With predictive analytics, we may forecast patterns by using historical data; for example, we can say that since ‘A’ occurred, ‘C’ is likely to occur in the future.
  
• Prescriptive analytics seeks to offer concrete steps towards a desired outcome, such as “We need to take action Y in order to achieve goal X.”

In its most basic form, everything of the work done by data analysts—from gathering and processing data to creating databases and doing different analyses—is directed towards accomplishing one of these four objectives. Gaining technical tool proficiency and converting unstructured data into actionable insights are the main goals of data analytics.

Difference between Business Intelligence and Data Analytics?

Data analytics and business intelligence appear to be rather similar based on what we’ve seen. If you’re a little bewildered right now, that’s okay. You’re not alone if you confuse the terms—many individuals on the internet do the same! 

To make things easier, we’ve outlined some major distinctions between data analytics and business intelligence in this section. Now let’s get started:

Using Insights vs. Creating Insights

The main goal of business intelligence is to facilitate decision-making by providing useful information gleaned from data analytics. 

Data analytics is primarily used to transform and clean raw data into actionable insights that may be utilised for business intelligence (BI) and other uses.

Backward Looking vs. Forward Looking

The main goal of business intelligence is to look back and observe what has already happened, then use that knowledge to guide future planning.

Although data analytics also finds historical trends, it frequently makes predictions about the future using these data.

Structures vs. Unstructured Data

Data kept in warehouses, tabular databases, or other systems is known as structured data and is used in business intelligence. To create dashboards and reports, these data are used.

Although it normally starts with unstructured, real-time data, data analytics also incorporates structured data. Cleaning and organising this data before saving it for later study is one of the responsibilities of data analysts.

Non Technical Users vs. Technical Users

Leadership teams and non-technical staff members including chief information officers, financial directors, and executives are the main users of business intelligence.

More technically oriented professionals like analysts, data scientists, and computer programmers tend to specialise in data analytics.

Big Picture vs. Narrow Focus

Business intelligence typically asks high-level strategic questions concerning the general direction of an organisation, thinking in “blue sky” terms.

Data analytics typically focuses on a specific problem or query, such as “Why are product A sales declining despite positive reviews?”

Tidy(ish) vs. Messy

Clear dashboards, reports, and other monitoring methods are essential components of business intelligence since they communicate findings in a clear and understandable manner.

Data analytics “under the hood” with data, performing operations like data mining, algorithm building, modelling, and simulations, in order to get insights.

There are some obvious distinctions between business intelligence and data analytics, as this list makes evident. It is important to emphasise that these are only broad recommendations rather than strict guidelines.

For example, business intelligence may also include activities like data mining, and data analytics may not always concentrate only on forecasting. These hazy distinctions help to partially explain why the names are so frequently used synonymously. 

One Key Takeaway

Remember this if you learn nothing else from this article: data analytics is not dependent on business; business is dependent on data analytics. Alright, so what does this actually mean?

In other words, data analytics plays a major role in business intelligence. Without it, it cannot operate. Think of it this way: businesses used to operate by intuition and gut feeling. Now, data is the compass that guides them. Without data analytics, businesses would be sailing blind in a sea of information, unable to make informed decisions or optimize their operations.On the other hand, even if business data is widely utilised in data analytics, it still works pretty effectively without it. It’s just a practical tool that companies have started using. Even while BI is currently one of the most popular applications for data analytics, it may also be utilised in a wide range of other domains. So in order to equip yourself with the required skills in this field, opt for a data analytics training course in Delhi, Patna, Ludhiana, Nashik, Bangalore and other cities of India.