BI and Augmented Analytics: A New Era of Business

What is Business Intelligence?

According to an article by TechTarget, “Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers, and workers make informed business decisions.”

In other words, businesses collect data from their operations and BI assesses the data to help in the decision-making process. Moreover, the data is collected from internal IT resources and external sources. Further, BI helps prepare the data for analysis and provides visual representations using dashboards. Hence, with the help of BI businesses engage in a better decision-making process. As a result, improving the productivity in operations, leading to an increase in revenue, and expanding horizons for the business.

How does Business Intelligence work?

• Firstly, businesses collect data from various sources and integrate them into a data warehouse or storage system.
• Further, it organizes the data into different sets according to the data analytics model to prepare them for analysis.
• Businesses also assign analysts and professionals to perform queries against the data.
• Moreover, the data visualization, dashboards, and reports include the query outcomes. 
• Hence, the businesses can then use the insights to make better decisions in strategies and planning operations.

What is Augmented Analytics?

Gartner defines the term as, “Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”

That is to say, Augmented Analytics is a data science approach that improves the analytics features in BI solutions. Moreover, Augmented Analytics leverages the functions of Machine Learning, Artificial Intelligence, NLP, etc to automate insights.

Therefore, here are all the technologies that Augmented Analytics use:

• Artificial Intelligence 
• Augmented Data Preparation
• Natural Language Generation
• Natural Language Processing
• Automated Business Monitoring
• Machine-led Insights

How does Augmented Analytics work?

• Firstly, AA enables access to an analytics solution and uses various features.
• Further, it comprehends the various opportunities in the market. Therefore, using the insights identifies the factors that affect the business.
• As a result, AA transforms businesses by providing actionable insights using data to enhance operations. 

Evolution of BI and the emergence of Augmented Analytics

Business Intelligence has 3 pivotal evolutions. Moreover, each evolution incorporates accessibility, speed, datasets, and quality of information. 

• Traditional BI

Traditional BI refers t the oldest version of BI, which provides the least user-friendly approach. Moreover, it was built on the legacy infrastructure to help IT teams collect and manage data for analysis. Further, it provided outcomes and reports using static dashboards to visualize the data. It also required experts and professionals to further the process of data analysis.

As a result, due to a lack of resources, the process could take weeks to provide actionable insight. Further, the system only offering retrospective results on queries could not offer predictive analysis. Hence, businesses would still rely on their expertise more than the insights to make decisions.

• Self-Service BI

Self-service BI was the next-in-line BI solution with a few updates. Most importantly, it enabled analysts and professionals to generate reports and offered visualizations. It also used data models by IT teams to create outcomes. Further, it was an easily accessible solution to help various departments authorize and use the insights

Although, SSBI did not reach far in the BI process as it is a complex approach. Hence, it could not manage the high volumes in data sets because it uses manual analysis resources. 

• Augmented Analytics

Above all, the evolution of BI results in Augmented Analytics which enhances the features of SSBI. AA uses modern technology like AI, ML, NLP, NLG, etc, to analyze data. Moreover, it leverages the features of the technologies to prepare the data by sourcing it from various networks. Further, AA offers businesses insight and data visualizations to simplify complex data. 

It is also an intelligent approach in BI as it provides quick insights for the real-time decision-making process. Hence, its results are accurate and easy for the user to comprehend the data for complex queries. Most importantly, it generates insights faster while providing custom designs in visualizations.

Importance of Augmented Analytics in Business Intelligence

• Data Standardization:

Firstly, AA is easily accessible for the complete organization. Moreover, its solutions offer data models and algorithms using AI and ML capabilities. Further, the models provide a user-friendly interface to help businesses use the solutions.

• Agile Decision-Making:

Further, Augmented Analytics offers insights for datasets during analysis. It also alerts the users and professionals regarding updates and recommends new datasets. Hence, with this capability businesses incorporate accurate information in their decision-making process.

• Automatic Suggestions:

Moreover, AA uses technologies like NLP to help non-technical users comprehend complex information. Therefore, it helps provide users with automatic suggestions to enhance and visualize the data. As a result, users can easily comprehend the data to discover patterns, predict changes, and make better decisions using valuable insights. 

• Data-Driven Approach:

Above all, due to the digital transformation in industries, businesses are now looking for solutions to incorporate various requirements. Moreover, data is now an important asset that all businesses must analyze for its optimum result. Data is also an important tool to comprehend the latest trends, customer behavior, segmenting audiences and targets, predicting changes, etc. In other words, data is a very pivotal asset, with analytics, it becomes actionable insights.

Top BI Solutions and Augmented Analytics

• Power BI:

Power BI combines augmented analytics and its ML features to provide efficient insights. Moreover, the solution uses AI to offer users quick insights and visuals for QnAs. It also enables data scientists to build ML models to analyze and comprehend data. 

• SAP Analytics Cloud:

SAP Analytics Cloud is a BI solution that uses Augmented Analytics to enhance its capabilities. Moreover, the tool offers businesses faster and more accurate insights. The BI solution also uses Natural Language Querying with the help of Augmented Analytics to provide visuals. 

• Qlik:

Qlik is a high-performing Associative Engine solution that helps users monitor and manage data sets. Moreover, the BI solution uses Augmented Analytics features to explore data sets and auto-generate the insights. 

BI and its Transformation with Augmented Analytics

Augmented Analytics automates the process of generating insights. Moreover, it enables businesses to relieve data scientists and other manual tasks to provide their expertise in other operations. Further, the use of Augmented Analytics in BI uses enhance ML and AI algorithms and models. 

It also reduces the scope of errors and inconsistencies that may lead to bottlenecks in the decision-making process. Hence, Augmented Analytics also revolutionizes BI processes and offers interactive insights. 

Developing Agility in BI processes using Augmented Analytics

• Firstly, Augmented Analytics executes unbiased data analysis that depends on business requirements. 
• It also identifies any issues that may arise and create bottlenecks in the BI process.
• Moreover, Augmented Analytics discovers various business opportunities and trends for BI tools to thrive on and result in expansion.
• Further, it standardizes various insights useful for the different departments in the organization. Hence, it helps BI solutions to utilize the insights and enhance performances.
• Most importantly, it helps shape actionable insights into efforts and transform processes to adopt agility.