Organizations generate a mountain of data day after day. To make intelligent decisions, tackle challenges, and stay profitable, specific tools and strategies are needed to turn that data into actionable insights. That's where business intelligence (BI) and its close relation, business analytics (BA), come into play. These are methods for managing and interpreting data to uncover valuable insights. But how do they differ, and which is best used in different scenarios?
The lines between BI, BA, and data analytics can be blurry, and to make it more confusing, people often use these terms interchangeably. So, let's break it down.
What is business intelligence (BI)?
Business intelligence is a technology-based process for gathering, storing, and analyzing an organization's data. It aims to turn raw information into meaningful insights that inform decision-making. BI can be used to set performance benchmarks, spot market trends, enhance compliance, and improve operations across multiple areas of an organization. BI platforms are typically used at the enterprise level and operate as external software rather than embedded into an application, like embedded analytics would be.
In short, BI isn't just about managing data - it's about using that data to guide your organization in the right direction.
What is business analytics (BA)?
Business analytics focuses more on statistical techniques to predict future trends and develop growth strategies. While BI might tell an organization who its current customers are, business analytics goes a step further, offering insights into the behavior of those future customers.
Business analytics provides a full toolbox of predictive tools, such as regression analysis, forecasting, text mining, and more. However, these methods often require skilled data scientists to utilize them fully.
What is data analytics?
When looking at business intelligence and business analytics, data analytics is often thrown in the mix. So, as a brief overview, data analytics deals with the nuts and bolts of data management - extracting, cleaning, and transforming raw data into something usable.
At a glance: Business intelligence vs. business analytics
| Business intelligence | Business analytics | |
|---|---|---|
| Definition | Relies on past data to analyze events in a company. | Utilizes data to analyze the reasons behind past events to forecast future outcomes. |
| Usage | Generally used to gain insights into business operations, assessing how previous processes impact key performance indicators (KPIs). | Analyzes the reasons behind how processes influenced the KPIs and assists in creating models to forecast the impact of future changes on them. |
| Application | Typically requires a blend of analytical, technical, and business expertise. | Generally more mathematically advanced and require a specific set a skills. |
Breaking down the key difference between BI and BA
Here are a few key ways BI and business analytics differ to help:
Present vs. future focus: "Descriptive" and "predictive"
As mentioned, the key difference between business intelligence and business analytics lies in their focus. BI zeroes in on current and past events captured in the data, while BA focuses on what's likely to happen in the future. Though both rely on the same data, they apply it to different timelines and purposes. Looking at the past and present can be described as a "descriptive" and "predictive" approach.
You can think of it this way: business intelligence helps tackle pain points in your organization’s internal processes - streamlining workflows, meeting goals, and boosting efficiency. On the other hand, business analytics focuses on the bigger picture, offering insights that drive product innovation and help you stay competitive in the global market.
Structured vs. unstructured data
BI tools are great for structured data, like financial reports or ERP system outputs. Business analytics shines with unstructured data, helping to make sense of things like social media posts or customer reviews through predictive models.
Who uses it?
Managers, marketers, and non-technical teams often rely on BI tools to make informed decisions without needing a data scientist's help. Meanwhile, business analytics usually requires more technical expertise, like machine learning knowledge, to turn data into predictive insights.
BI, business analytics, both, or embedded analytics?
When considering the best option, the real question isn't "BI or business analytics?" but "What do we need from our data systems, and who will be using them?"
While there are many tools and systems to choose from, we believe embedded analytics stands out as a superior option. Unlike traditional BI platforms that operate as separate tools outside your everyday business applications, embedded analytics integrates directly into the software your team already uses. This seamless integration allows users to access real-time insights right within their workflows, eliminating the need to juggle multiple programs or tools.
It’s user-friendly, improves efficiency, and encourages higher adoption across your organization—all while empowering your team to make data-driven decisions quickly and effortlessly. For us, it’s the perfect blend of functionality and simplicity.
Questions to consider
However, when figuring out what's best for your organization, you should be asking yourself the following:
- Who will use the system, and what are their needs?
- Which departments will rely on it, and what metrics should be tracked?
- What kind of support do users need to maximize the system's value?
- What is the skill level of my users like? How technical are they?
- Will I benefit from embedding or using a separate piece of tech?
By answering these questions, you can craft a strategy that uses business intelligence, business analytics, or embedded analytics in a way that supports your goals and empowers your team to make better, faster decisions.
