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Data Analysts aren’t a new addition to the workforce. In fact, the science of analysing data, facts and figures has been around for a while now. However, the terms business analyst/business analytics and data analyst/data analytics are often used interchangeably.

In smaller organisations, these positions may be the same, and “business analyst” becomes the broad title for a job that involves data or systems analysis. In larger companies, though the roles sometimes blur in the sense that analysts in both categories work with data. What the analysts do with that data is entirely different. Their skill-set and sometimes, their job environment, is also different.

Given the status quo, it is important for us to make a distinction between the terms “business analytics” and “data analytics”.

Earlier this year, we shared wrote about data analytics with our blog post titled, What is Data Analytics: 4 Benefits of Data Analytics. It covered what is data analytics and why it is important for businesses.

Today, we’ll be taking a look at the differences between business analytics and data analytics.

 

 

An Overview of Business Analytics and Data Analytics

Both business analytics and data analytics involve working with and manipulating data, extracting insights from data, and using that information to enhance business performance.

 

 

Business Analytics

Business analytics, a sub-division of business intelligence, focuses on the big picture of how data can be used to improve weak areas in an existing procedure or to add value or cost optimisation in a specific business process. This may involve the use of reporting or financial analysis tools, data visualisation tools, and data mining to improve specific business functions such as sales and marketing. An example would be using business analytics to determine whether a company should develop a new product line or prioritise one project over another. 

Business analytics focuses on creating solutions and solving existing challenges that are unique to the business and usually stays at the forefront of the data pipeline as opposed to data analytics, which is more focused on the backend. Successful business analytics applies data-derived insights to support decision-making processes and drive practical changes throughout the organisation.

 

There are three main kinds of business analytics: descriptive, predictive and prescriptive. These are usually implemented in stages and together can answer or solve just about any question or problem a company may have. 

  • Descriptive analytics answer the question, ‘What has happened?” This type of analytics evaluates historical data for insights on how to plan for the future. Thanks to the widespread availability of self-service data access and discovery tools and dashboards, executives and non-technical professionals can reap the benefit of insights generated by big data to improve business performance.
  • Predictive analytics is the next step on the path to insight. It uses machine learning and statistical techniques to help businesses anticipate the likelihood of future events. However, because predictive analytics is probabilistic in nature, it cannot actually predict the future; it can only suggest the most likely outcome based on what has happened in the past.
  • Prescriptive analytics explores possible actions to take based on the results of descriptive and predictive analysis. This type of analytics combines mathematical models, and business rules to optimise decision making by recommending multiple possible responses to different scenarios and tradeoffs.

 

Organisations may use any or all of these techniques, though not necessarily in this order. Business analytics can also be implemented in any department, from sales to product development to customer service, thanks to readily available tools with intuitive interfaces and deep integration with many data sources. Many of these solutions offer users the ability to apply advanced analytic models without the help of a data scientist, creating new opportunities to find hidden insights in large datasets. 

Business analytics requires adequate volumes of high-quality data, so organisations seeking accurate outcomes must integrate and reconcile data across different systems, then determine what subsets of data to make available to the business.

 

 

Data Analytics

Data analytics is the process of analysing and categorising data – sorting, storing, cleansing, identifying patterns, and interpreting insights by using various statistical techniques, big data processing, and technology. It is a crucial practice for improving organisational or operational efficiencies and developing strategies to seize new business opportunities. 

One of today’s most popular and recognisable forms of data analytics is machine learning, which processes massive volumes of data and uncovers patterns within that data to make intelligent predictions and produce unique insights that answer a particular business question or solve a specific business problem.

Data analysis attempts to answer questions such as, “What is the influence of geography or seasonal factors on customer preferences?” or “What is the likelihood a customer will defect to a competitor?”. 

Data analysis is more technical than business analytics and requires the use of sophisticated analytics tools like Python and Tableau. Data findings must also be translated into meaningful information to present to different teams or to business leaders who need to be able to understand and interpret the insights easily.

 

Most commonly-used data analysis techniques have been automated to speed the analytical process. Thanks to the widespread availability of powerful analytics platforms, data analysts can sort through huge amounts of data in minutes or hours instead of days or weeks using:

  • Data mining involves sorting through large data sets to identify trends, patterns, and relationships
  • Predictive analytics aggregates and analyses historical data to help organisations respond appropriately to future outcomes like customer behaviour and equipment failures
  • Machine learning uses statistical probabilities to teach computers to process data faster than conventional analytical modeling
  • Big data analytics applies data mining, predictive analytics, and machine learning tools to transform data into business intelligence
  • Text mining spots patterns and sentiments in documents, emails, and other text-based content

 

As more organisations move their critical business applications to the cloud, they are gaining the ability to innovate faster with big data. Cloud technologies create a fast-moving, innovative environment where data analytics teams can store more data and access and explore it more easily, resulting in faster time to value for new solutions.

 

 

Business Analytics vs. Data Analytics

This infographic shows a clear picture of the differences between these two terminologies. 

 

 

Conclusion

From the newest startups to established global enterprises, every organisation needs to leverage data for innovation and business growth. The practices of data analytics and business analytics share a common goal of optimising data to improve efficiency and solve problems, but with some fundamental differences.

Whichever path you choose, you’ll need to the skills and expertise to execute it. Check out these courses which can help you do so.

Business Analytics & Data Visualisation

Introduction to Data Analysis: Mining Insights From Data

Introduction to Data Visualisation: Presenting Insights From Data

Introduction to Data Storytelling: Narratives from Data

 

 

Source:

Business Analytics vs. Data Analytics: Which is Better for Your Business?

Data Analytics vs Business Analytics

What’s The Difference Between Data Analysts And Business Analysts?

What’s the Difference Between Data Analytics and Business Analytics