Written by Roman Pjajka |
In today’s data-driven world, the terms business intelligence (BI) and data analytics have become nearly synonymous, and while that might be the case, the two are not exactly the same. Although overlaps between BI and data analytics certainly exist, nuanced differences also set them apart. Be sure to consider these details when deploying and maintaining a successful data strategy.
Keep reading to learn the definitions of each term, as well as their similarities, their differences, and why they can be — and often are — used interchangeably.
What Is Business Intelligence?
BI is a comprehensive collection of processes that aim to improve aspects of your organization by leveraging and utilizing data created through daily operations. These uses include benchmarking, spotting market trends, and more. Conducted in a structured and periodical manner, BI is delivered to the average business user in the form of easy-to-understand business reports.
What Is Data Analytics?
Data analytics is a broader concept for extracting insights from raw, unstructured data. Data analytics can refer to any form of data analysis — via spreadsheets, databases, or apps — to identify past trends and to predict future occurrences, thus focusing on more ad-hoc and investigative tasks. Data analytics processes fall under one of four categories:
- Descriptive: Descriptive analytics focuses on summarizing raw data in a meaningful way as well as transforming the data to prepare it for deeper analysis later. Descriptive analytics also converts the raw data into a form readily understandable by standard business users. This includes traditional business indicators, created to be observed regularly.
- Diagnostic: Diagnostic analytics comprises the data that was transformed via descriptive analytics and then further analyzed with the purpose of discovering why performance has increased or decreased. This type of analytics helps with analyzing past trends in data to clearly identify their causes.
- Predictive: Predictive analytics centers on forecasting the future based on trends within past data. These predictive models are more successful when a large quantity of data is available.
- Prescriptive: Prescriptive analytics is an advanced form of analytics that improves decision-making by analyzing possible future outcomes of current decisions. It answers questions such as “What should be done?” or “How do we make XYZ happen?”
The Similarities Between Business Intelligence and Data Analytics
With the above definitions in mind, here are a few factors that BI and data analytics have in common.
- Gathering valuable insights: For both BI and data analytics, the overarching goal is to generate valuable insights from data to benefit the organization. These benefits can be realized in various ways, such as identifying pain points within the current business strategy or improving future business strategies.
- Following a similar process: BI and data analytics techniques go through a similar order of processes to reach their shared goal. For example, a crucial step within both the data analytics and BI procedures is data collection. Data collection is a particularly vital step as low-quality data can yield low-grade insights, thus resulting in falsely informed decision-making and lost opportunities.
- Providing actionable insights: BI and data analytics both result in the generation of information and insights. Furthermore, these insights must be reported to various internal and external users. After going through varying forms of analysis, the generated insights have to be presented comprehensively to enhance decision-making and to address existing weak points within the organization’s activity.
The Differences Between Business Intelligence and Data Analytics
Now, let’s look at the key differences between BI and data analytics.
Data analytics can refer to various analytics processes that start specifically with raw, dirty data. On the other hand, BI encompasses only those that involve already refined and structured data coming from data warehouses, and that are specifically related to improving business operations on a day-to-day basis.
These distinctions also impact which personas are given access. While BI is specifically targeted at a business audience, data analytics is more technical and requires a higher level of mathematical expertise in coding and algorithms to search for insight in enormous, raw datasets.
Another significant difference between data analytics and BI, specifically related to business application, is determined by the position of the targeted data consumer.
BI focuses primarily on internal team consumption. In other words, internal business users — with the goal of process optimization and improved decision-making — observe data that is generated by daily operations. Though the distinction suggests that BI does not directly generate revenue for the business, it still can significantly enhance overall output.
In contrast, data analytics is often used as a revenue-generating tool provided for external users. Data analytics can be added to an already existing product (or service) to encourage adoption and to increase the product's value. As a result, data analytics also can directly generate revenue for the organization who had purchased the analytics solution and implemented it within their product; these use cases are referred to as embedded or customer-facing analytics.
Business Intelligence and Data Analytics as One
Rather than looking at BI and data analytics as competing concepts, they should be used simultaneously — both to enhance day-to-day operations and to inform long-term business strategy. Understanding the similarities and differences between BI and data analytics will help you leverage your resources in the most effective and impactful way.
Written by Roman Pjajka |
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