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How Augmented Analytics is Redefining Business Intelligence

May 22, 2023

How Augmented Analytics is Redefining Business Intelligence

In every industry, today’s businesses require good data to make informed strategic decisions. Technology continually makes the process of gathering, interpreting and visualizing data more accessible and practical, ensuring that it is delivered in real-time so leaders can make accurate insights that shape their business plans. Even with the technological advancements already made in business analytics and intelligence, the most significant evolutions in the field are likely still to come through augmented analytics powered by machine learning and artificial intelligence.

Exploring Augmented Analytics

In augmented analytics, automated tools assist in data preparation and generating insights. They help data experts create usable business intelligence by automating many aspects of data science.

According to technological research and consulting firm Gartner, automated analytics enables technologies such as machine learning and AI for data preparation, insight generation, and explanation to augment how people explore and analyze data in analytics and BI platforms. It also boosts expert and citizen data scientists by automating data science, machine learning, and AI model development, management and deployment.

Understanding business intelligence, business analytics and the history of both in the workplace is critical to appreciate how augmented analytics can change the game.

What is Business Intelligence?

According to visual analytics company Tableau, business intelligence (BI) is an infrastructure that helps collect, store and analyze data from business operations. Business intelligence uses technology to analyze data and generate reporting to understand what has been and is happening in an organization. BI analyzes past and present data points to drive current business needs. All companies use business intelligence in their operations in some form.

What is Business Analytics?

Tableau defines business analytics (BA) as taking a company’s raw data and turning it into useful information, including identifying trends, predicting outcomes and more.

Business analytics uses technology, such as data mining, predictive analysis and forecasting, to explore past business performance to help drive business planning. BA impacts business operations by promoting future growth more than it informs day-to-day decisions. An analysis of past data helps predict future outcomes, meaning BA is used for long-term decision-making.

Business Intelligence vs. Business Analytics

While business intelligence and business analytics focus on collecting and analyzing data, visualizing it, and generating reports, they are different. The terms are often used interchangeably in business, but subtle differences exist.

The significant difference between business intelligence and business analytics is the business needs and questions they address. This variance is most easily illustrated in the distinction between descriptive and predictive analytics.

Descriptive vs. Predictive Analytics

Business intelligence focuses on descriptive analytics. Descriptive analytics looks at historical and present data to show what is happening in a business. BI answers "what?" and "how?” so companies can replicate what works and discontinue what doesn't.

Business analytics, however, prioritizes predictive analytics. BA professionals use data mining, modeling and machine learning to determine future outcomes. BA addresses “why?” so businesses can anticipate future needs and outcomes and make the necessary changes to grow organizations and succeed in their industries.

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Evolution of Business Intelligence

Business professionals have been using data to inform their choices for decades. In recent years, through augmented analytics, BI insights have become easier to pull and interpret, making them incredibly accessible and improving the overall impact of BI on a business.

Let’s look at the different types of BI throughout the years, leading to augmented analytics and its role in the field today.

For a time, businesses relied on traditional business intelligence, which required data professionals with an information technology background to aggregate data for analysis. Traditional BI involved pulling data and generating reports manually. This required business managers to know what data to request and depend on a third party to pull the data they needed to make decisions. Of course, this process took time and critical insights could be delayed, meaning some essential data and wisdom would not be available for significant decisions.

Self-service business intelligence (SSBI) made insights more accessible. SSBI offered significant improvements over traditional models because people outside IT departments could create reports and pull their data using models built for them by IT professionals. Of course, in more complex cases, self-serve BI still requires experts to know what data to pull and how to interpret the data, which could be challenging for non-experts to understand and interpret.

The Rise of Augmented Analytics

Augmented analytics deliver insights and visualization that make data accessible and valuable to the average user in real-time using machine learning and artificial intelligence. Able to analyze data in real-time, augmented analytics can provide insight into the why of the information reported, not just the what, so that users can make timely, predictive conclusions that benefit their organizations. Over time, systems develop more understanding of users' needs to directly answer concrete questions, often anticipating questions that average users may not know to ask.

Augmented Analytics Examples

Augmented analytics can impact a variety of industries. Business intelligence use cases of augmented analytics include the following sectors, among many others:

  • Marketing: Augmented analytics can help businesses understand clients' needs to plan successful promotions, messaging, marketing campaigns and more.
  • Financial Services: Financial services companies thrive on good data. Augmented analytics can track fraud, help financial analysts manage risks and find growth opportunities.
  • Healthcare: Electronic medical records allow healthcare organizations to use data to improve patient care, monitor and prevent outbreaks and more.
  • Hospitality: The hospitality industry can tailor pricing and improve service using augmented analytics to deliver excellent customer experiences.

This is just a tiny look at the way industries can benefit from augmented analytics. Professionals who understand how to use technology to collect and interpret data will be in high demand in the coming years to ensure that businesses move forward in an informed way that leads to success and customer happiness.

Be Part of the Future with a Master of Business Analytics


Study business analytics at Ohio University to be part of the future of business intelligence. Throughout our online Master of Business Analytics program, you will use business analytics case studies, project-based learning and business simulations to build confidence in your analytics skills. The curriculum explores topics like database management, cybersecurity, the ethical use of analytics and communicating findings to a non-technical audience. You’ll also cover new technologies that will play a massive role in the future of business intelligence.

OHIO also offers a Quantitative Bridge Course for students returning to school after years of working in the field. This course allows students to review quantitative skill areas and reacclimate to higher education before their first program course.

Find out more about how OHIO can impact your career in analytics.

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