3 business intelligence tools. Does BI need artificial intelligence ? | AI in business #16

In the world where data is becoming the new currency, business intelligence (BI) tools are key. But is inserting artificial intelligence (AI) into the equation a necessity or just a fashionable addition? Let’s dive into the world of BI tools to understand how AI can enrich them.

What is business intelligence?

Business Intelligence is not just the process of transforming raw data into valuable information. It is the bridge that connects data with decisions, enabling companies to better understand the market, the competition and their operations. The key elements of Business Intelligence are:

• Data – a raw material that is processed and analyzed to become information.
• Information – properly interpreted and placed in context,
• Knowledge-based on data and information, is the key to making sound business decisions.

The 3 most popular business intelligence tools

Let’s take a look at the most popular tools that improve the transition from data to AI-assisted knowledge.

1. Tableau – a platform that employs artificial intelligence to help users create interactive reports and dashboards. Tableau’s most interesting features include:
• Ask Data – to ask questions in natural language, and Tableau provides the answers in the form of visualizations,
• Explain Data – helps understand what’s behind the data, explaining anomalies and trends,
• Smart Recommendations – suggests the best ways to visualize data, combine data sources and create calculations,
• Einstein Discovery – allows you to quickly create and implement advanced predictive models and present their results in Tableau.

Tableau also integrates with multiple platforms, such as Salesforce, Google Cloud, and Amazon Web Services (AWS), making it a flexible and versatile solution for businesses.



Data visualization in Tableau.

Source: Tableau.com

2. Microsoft Power BI – offers a rich set of AI components to easily and quickly enrich your data with pre-built or custom machine learning models. Microsoft Power BI features based on artificial intelligence are called AI Insights, which include:
• Text Analytics – enables analysis of emotions in processed text, extraction of key phrases, language detection and proper name recognition. It can therefore examine customer feedback, automatically understand key topics from product reviews, detect the language of emails, or identify names of people, organizations and places from newspaper articles,
• Vision – it can automatically tag images and segregate them with labels that describe the image content. It can, among other things, classify product photos, tag landscape or animal photos, recognize faces or logos, or generate captions to describe scenes in images,

Power BI is integrated with Azure, enabling advanced analytical models and cloud functions.



Data Visualization in Microsoft Power BI.

Source: powerbi.microsoft.com

3. Oracle BI – a comprehensive solution that features AI components, in areas:
• generative AI – to create new content based on existing data, such as reports or presentations,
• predictive tasks – to forecast future behavior, performance and trends based on historical and current data. For example, Oracle BI can forecast demand, sales, profitability, risk, customer loyalty and many other business metrics using built-in or custom analytical models,
• responsible AI – to build confidence in data analytics through procedural transparency. This Oracle BI component is designed to help users understand the logic and provide justifications for AI recommendations, monitor the performance and accuracy of analytical models, detect and eliminate biases and discrimination in data and algorithms, and collaborate with other users and experts to improve the quality and value of business information.


Data Visualization in Oracle Business Intelligence.

Source: docs.oracle.com

BI vs AI – differences and example applications

While business intelligence focuses on analyzing data, artificial intelligence adds the ability to draw conclusions and make decisions on its own to the equation.

BI (business intelligence) is a term that refers to various tools and techniques for collecting, integrating, analyzing and presenting business information. The goal of business intelligence is to support better decision-making by providing accurate, timely and relevant information.

AI (artificial intelligence), on the other hand, deals with tasks that require natural language understanding, image recognition or decision-making.

Here are three key differences between BI and AI:

• Goal: Business intelligence aims to support better decision-making by providing accurate and timely information, while AI’s goal is to automate tasks that require human intelligence.
• Technologies: BI has a variety of tools and techniques to collect, integrate and analyze data, while AI features advanced machine learning and deep learning algorithms to create computer systems capable of performing complex tasks.
• Scope: Business intelligence focuses on analyzing business data and providing decision-support information, while AI can be applied to a wide variety of areas, including supporting BI operations and drawing conclusions from data.

For example, BI collects and analyzes data on customer buying behavior, while AI allows you to create a system that recommends products to customers based on analysis of their buying behavior. It seems that what they have in common is mainly the word “intelligence.”

Perspectives on AI-supported business intelligence

Artificial intelligence not only enriches BI tools but also opens up new possibilities. Thanks to AI, BI systems can:

• better understand the needs of users,
• provide more precise forecasts and
• automatically adapt to changing market conditions.

In the future, we can expect even more integration of business intelligence with AI, which will bring new opportunities and challenges for businesses. AI can enable the automation of many analytical tasks, for example, it can be used to:

• automatic input cleaning,
• the creation of statistical models or machine learning, as well as
• generating visualizations and reports.

AI can also help discover new patterns and relationships in data that might be overlooked by humans. This will help companies gain new insights into their operations and make better business decisions.

BPM, business analytics and AI-enabled BI – what’s the difference?

BPM focuses on managing and improving business processes, while business analytics tools analyze data and provide insights into business performance. BI encompasses both areas and relies on various tools and techniques to support better decision-making. Despite some overlapping between these areas, each has its focus and set of tools:

• BPM (Business Process Management) is a discipline that deals with managing and improving business processes in an organization. BPM tools help to design, model, execute, monitor and optimize business processes to increase efficiency and effectiveness.
• Business analytics tools are used to analyze data and provide insights into business performance. These include data mining, predictive analytics and statistical analysis tools. Business analytics tools help to identify trends, patterns and relationships in data to support decision-making.
• Business intelligence (BI) is a broader term that includes both BPM and business analytics. BI involves combining various tools and techniques to collect, integrate, analyze and present business information. The goal of BI is to support better decision-making by providing accurate, timely and relevant information.

Does BI need artificial intelligence?

In the era of digital transformation, while operating on big data, the combination of business intelligence with artificial intelligence is becoming indispensable. Tools such as Tableau, Power BI and Oracle BI show how powerful this blend of technologies has become, providing companies with tools that help them make better business decisions.

However, does BI need artificial intelligence? This is a question with no clear answer. On the one hand, artificial intelligence can help analyze and interpret large data sets, providing valuable information and guidance to decision-makers. On the other, it can get costly, complex and prone to error or manipulation.

In the future, we can expect even more integration of BI with AI, which will bring new opportunities and challenges for businesses. In a world where data is the key to success, the responsible combination of BI and AI is becoming a really important issue.

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