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Data Analytics vs. Business Analytics

By now it’s trite to call data “the new oil,” but it's a hard metaphor to shake. Just like in the middle stages of the “black gold” boom, the data industry is beginning to change focus from extraction to refinement. No amount of raw data is worth anything without analysis, so the tools and mechanisms of analysis are undergoing specialization and fine-tuning.

This specialization can cause some terminological headaches, like when you need to define the difference between data analytics and business analytics. Both data analytics and business analytics are core parts of a modern data strategy, so to understand the terrain when working with data, you need a grasp of both components. Let’s dive in and explore the differences between these important concepts.

What is data analytics?

Data analytics is a broad umbrella that covers many different stages and processes of data intake, pre-processing, modeling, analysis, visualization, and reporting. This sequence of data management is referred to as the data analytics lifecycle: a pipeline of tools and statistical doctrines that are customized by a given company to deliver various types of analysis to teams that need them.

Often, the individual stages of a data analytics lifecycle are managed by different specialists; for example, data analysts are focused on making data transparent and legible. On the contrary, data engineers are experienced generalists with the knowledge and capability to take responsibility for the maintenance and function of the data pipeline.

What is business analytics?

Business analytics is one of the more focused subsets of the data analytics lifecycle. As the title suggests, business analytics is concerned with the data and insights that directly affect organizational decision-making. Typically, business analytics will be deployed across a company and has a more responsive role to play than other types of analytics: jumping into teams to identify pain points and potential solutions rather than trying to characterize wide-ranging sets of customer data.

As such, business analysts are typically experienced and knowledgeable about existing processes of logistics, staffing, and internal management. A business analyst must be able to coordinate between different teams and departments and understand their interconnections (or lack of connections) before being able to effectively analyze or report on business data. Therefore, business analysts do benefit from a background in statistics and some analytics experience, but — setting them apart from other roles within data analytics — business analysts benefit most from a strong understanding of the company itself and experience with management or organization-level decision-making.

Business analysts aren’t only reactive to internal problems. They can also be tasked to find areas to develop in terms of innovation, growth, or financial performance similar to a typical data analyst.

As with all data analysis, business analytics distinguishes between four types of analysis. When thinking about developing an analytics pipeline or a business analytics team, take into consideration which types of analysis you need — some have a higher skill threshold than others.

  • Descriptive: These are baseline analytics processes that help a company understand its own data. In the case of business analytics, this often comes up in the form of performance metrics, trend-making, or tracking deliverables.
  • Diagnostic: This tries to identify causal relationships between identified trends. This means research into the current practices of different teams: why isn’t the sales department hitting its goals? Did a policy change decrease employee churn? Why is this contractor more efficient than internal equivalents?
  • Predictive: Business analysis is purposed towards enabling decision-making, and therefore often deals in predicting the future. This often takes the form of financial modeling but can require a more comprehensive analysis, as when analyzing the profitability of adding or removing employees from the books.
  • Prescriptive: In the most complex cases of business analytics, the analyst will be tasked with generating solutions to identified problems. This means that the analyst is not only undertaking predictive analysis but also comparing multiple predictive models and recommending a decision to managers.

It is worth noting that business analysts often receive tasks from stakeholders that combine more than one of these forms of analysis. For example, one might be asked to investigate why an email campaign has a low impact on signups. This question covers:

  • Descriptive Analytics: How different is the signup rate for those who receive campaign emails and other users?
  • Diagnostic Analytics: Do the emails have low click-through rates or are they going to bad landing pages?
  • Prescriptive: Somewhat implied in that question is “what can we do to fix it?”

There is, therefore, a range of depth and difficulty within business analytics. It is important to reflect on the style of business analytics you can offer as an employee, or what you might be seeking as an employer.

What’s the difference between data analytics and business analytics?

Data analytics and business analytics are rarely two competing species in the taxonomy of data analysis. Rather, business analytics is largely a subset of data analytics, and therefore should be compared more in terms of scope than purpose.

Nevertheless, these two forms of analytics are often confused, especially because many workers transition between the two roles. Therefore, this section will break down some of the differences between data analytics and business analytics.

Business analytics is a subset of data analytics

Data sources

Data analytics is concerned with ingesting all available data. As part of a data analytics pipeline, data engineers should be delivering processed and standardized data to any teams that need it. Additionally, data analytics frequently synthesizes new data, which is also distributed in the course of the data analytics lifecycle.

On the other hand, business analytics is focused on specific types of data. Rarely does the business analyst have to source or ingest raw data — they rather rely on data analytics to supply relevant data such as leads, conversions, payment processing, or cancellation events. Of course, advanced business analytics can also create new models or metrics, but once they are created, their distribution is likely also the purview of a data engineer.

Analytical approaches

Data analytics is largely agnostic about what direction analysis should take. A variety of data is available at a company, and the data analytics team is tasked to prepare this data for any of those potential usages.

Business analysts are highly responsible for decision-making and impact: in many cases, business analytics is a kind of triage service that responds to acute business problems and needs to deliver relevant, confident advice quickly. This is a high-responsibility role and requires a more rigid, less exploratory approach to analytics.

Business analysis, therefore, is usually characterized by the development of hard metrics but not necessarily by ongoing observability, logging, or repeatability.

Deliverables

The ephemeral direction of data analytics means that full-spectrum data engineers require a broad vision and a good depth of knowledge of the data itself. They are responsible for delivering to anyone in the company, but like a librarian finding books for researchers, this implies more knowledge about the way the library is organized than the content of the books.

Business analytics is usually attached to an existing team or process to deliver specific results. This means they need to understand the data context. They should be able to root through your data warehouse just as a data engineer would, but their primary responsibility is to deliver explanations and predictions of business phenomena — not to distribute information or to manage data ecosystems.

Putting it together

Although they are different systems and roles, data analytics and business analytics are highly connected and both important components of a healthy data ecosystem. While data analytics is a higher-level, more technical post carried out by data engineers, business analytics is targeted work done by professionals with strong knowledge of business context.

Whether you are building up a new data analytics pipeline, applying for these roles, or looking to improve your skill in analytics, it is important to have a reference point for concepts in analytics. That’s why our learning center offers a suite of related articles:

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