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Business analytics definition

Let’s start by differentiating between data analytics and traditional business analytics. The terms are often used interchangeably, but a distinction does exist. Traditional data analytics refers to the process of analysing massive amounts of collected data to get insights and predictions. Business data analytics (sometimes called business analytics) takes that idea, but puts it in the context of business insight, often with prebuilt business content and tools that expedite the analysis process.

Specifically, business analytics refers to:

• Taking in and processing historical business data
• Analysing that data to identify trends, patterns, and root causes
• Making data-driven business decisions based on those insights

In other words, data analytics is more of a general description of the modern analytics process. Business analytics implies a narrower focus and has functionally become more prevalent and more important for organisations around the globe as the overall volume of data has increased.

Using business analytics tools

Business data analytics has many individual components that work together to provide insights. While business analytics tools handle the elements of crunching data and creating insights through reports and visualisation, the process actually starts with the infrastructure for bringing that data in. A standard workflow for the business analytics process is as follows:

Data collection: Wherever data comes from, be it IoT devices, apps, spreadsheets, or social media, all of that data needs to get pooled and centralised for access. Using a cloud database makes the collection process significantly easier.

Data mining: Once data arrives and is stored (usually in a data lake), it must be sorted and processed. Machine learning algorithms can accelerate this by recognizing patterns and repeatable actions, such as establishing metadata for data from specific sources, allowing data scientists to focus more on deriving insights rather than manual logistical tasks.

Descriptive analytics: What is happening and why is it happening? Descriptive data analytics answers these questions to build a greater understanding of the story behind the data.

Predictive analytics: With enough data and enough processing of descriptive analytics business analytics tools can start to build predictive models based on trends and historical context. These models can thus be used to inform future decisions regarding business and organisational choices.

Visualisation and reporting: Visualisation and reporting tools can help break down the numbers and models so that the human eye can easily grasp what is being presented. Not only does this make presentations easier, these types of tools can help anyone from experienced data scientists to business users quickly uncover new insights.

Business analytics vs. business intelligence

On the face of it, there may not seem to be much difference between business analytics and business intelligence. Some overlap does exist between the two, but looking at business analytics versus business intelligence still creates a gap that needs some explanation.

Certainly, the terms are extremely connected, but business intelligence uses historical and current data to understand what happened in the past and what is happening now. Business analytics, on the other hand, builds on the foundation of business intelligence and attempts to make educated predictions about what might happen in the future. In order to make data-driven predictions about the likelihood of future outcomes, business analytics uses next-generation technology, such as machine learning, data visualisation, and natural language query.

Business analytics use cases

More and more departments are trying to better understand how their decisions and budgets affect the business at large. With business analytics software, it’s possible to use data to drive strategic decisions, regardless of task or department:

Marketing: Analytics to identify success and impact
Which customers are more likely to respond to an email campaign? What was the last campaign ROI? More and more marketing departments are trying to better understand how their programs affect the business at large. With AI and machine learning powering analysis, it’s possible to use data to drive strategic marketing decisions.

Human Resources: Analytics to find and share talent insights
What actually drives employee decisions regarding their career? More and more HR leaders are trying to better understand how their programs affect the business at large. With the right analytical capabilities, HR leaders are able to quantify and predict outcomes, understand recruitment channels, and review employee decisions en masse.

Sales: Analytics to optimise your sales
What is the critical moment that converts a lead to a sale? In-depth analytics can break down the sales cycle, taking in all of the different variables that lead to a purchase. Price, availability, geography, season, and other factors can be the turning point on the customer journeyand analytics offer the tool to decipher that key moment.

Finance: Analytics to power predictive organisational budgets
How can you increase your profit margins? Finance works with every department, be it HR or sales. That means that innovation is always key, especially as finance departments face larger volumes of data. With analytics, its possible to bring finance into the future for predictive modeling, detailed analysis, and insights from machine learning.