15 AI tools for business analytics to gain a competitive edge

The average company pulls from 400 data sources to feed their business intelligence and analytics systems. Because of this, sifting through data, creating custom dashboards, and generating insights has become an ever-growing challenge. 

AI tools simplify these business analytics processes to enable data-driven decisions and optimal business outcomes. Here’s what you need to know to use these tools in your organization.

• How are artificial intelligence and machine learning platforms used in business analytics?
• 15 AI tools to implement in your business strategy
  • 1. Tableau
  • 2. ChatGPT
  • 3. Polymer
  • 4. MonkeyLearn
  • 5. Microsoft Power BI
  • 6. Sisense
  • 7. Akkio
  • 8. Google Looker
  • 9. Qlik Sense
  • 10. Splunk
  • 11. TIBCO Spotfire
  • 12. RapidMiner
  • 13. SAS Business Analytics
  • 14. KNIME
  • 15. Domo
• 6 bonus business analytics AI tools
• Develop business analysis skills
• Proving the value of AI tools for business analysis
• Will business analysts be replaced with AI?
• The future of business analytics is in the future of AI

Business analytics requires data professionals to wade through multiple data sources, data silos, and complex data sets to draw insights. AI-powered business analytics tools automate certain processes and allow business analysts to spend time on more strategic work.

Using artificial intelligence and machine learning algorithms, they can identify patterns, spot inconsistencies, predict trends, and deliver insights that lead to a better understanding of your business across all functions.

This, in turn, drives more informed decision making. For example, if you can identify patterns in user behavior, you’ll know what to optimize in your product or user experience. Or if you can predict customer churn, you can take steps to proactively prevent it.

AI tools give your business analysts a more comprehensive look at the business—at a fraction of the time it would take them to do everything manually. And because AI algorithms use real-time data, analysts can investigate and resolve anomalies faster.

In many ways, the future of business analysis lies with AI. Here’s a brief overview of some of the top tools for business analytics.

Tableau is a data visualization and analytics platform that doesn’t require coding experience to create and share reports. Tableau uses generative AI to automate task management, suggest questions, and deliver insights and explanations about your organization’s data in easy-to-understand language. 

This makes it a good tool for business leaders looking to get a comprehensive overview of their business and non-technical employees looking to become more data driven.

ChatGPT is a helpful assistant for business analysts. It can analyze data sets and identify trends as well as use predictive analytics to predict outcomes and customer behavior to enable strategic decisions.

Analysts can also use prompt engineering to ask ChatGPT to identify risks and challenges, explain data in plain language, and compose SQL queries. Just be mindful of data privacy and check responses for accuracy.

Polymer is another business intelligence tool that doesn’t require technical skills. Anyone can upload spreadsheets from Excel and Google Sheets, then Polymer’s AI technology will visualize the data and create interactive, searchable dashboards automatically. 

MonkeyLearn is an AI-powered text analysis tool with data visualization capabilities. It uses machine learning models to analyze text, like reviews, surveys, transcripts, and chats, to identify trends, perform sentiment analysis, sort information into categories, and create charts. It’s another tool that doesn’t require coding experience.

Microsoft Power BI is another business analytics tool that supports data visualization, reporting, and analysis for almost any data source. Because it integrates with other Microsoft tools like Excel, it provides a centralized data hub with insights across services. This makes it an intuitive choice for organizations that already use Microsoft services.

Learn more about Power BI.

Sisense is a tool analysts and developers use for data and business analytics. It offers low-code and no-code capabilities with a drag-and-drop interface and developer-first tools, SDKs, and APIs that embed analytics in your product and data pipeline. Like most other tools on this list, it provides data visualization, analysis, and reporting capabilities and can answer questions about data using natural language processing and generative AI.

Akkio is marketed as an AI assistant for data. It’s a beginner-friendly tool that assists with data forecasting, analysis, and visualization. Due to its predictive power, it’s useful for business forecasting, sales, and marketing. Users can create reports and models without needing technical skills, while business analysts can use it to create charts and predict business outcomes faster.

Google Looker is part of Google Cloud. It provides data modeling and analysis and enables teams to build custom applications with embedded analytics. As a cloud-based platform, Looker operates in your browser and can process large amounts of data at once, but reports aren’t generated automatically.

Qlik Sense is a cloud-native data visualization and analysis platform designed for users of all skill levels. It provides interactive dashboards, predictive analytics, and AI-assisted data prep. Users can explore their data by asking questions and collaborate with teammates through threads and notes.

While Splunk isn’t strictly for business intelligence, it’s a useful tool that allows organizations to centralize data management and monitor, search, and analyze data from any source. It’s especially useful for security and observability, as it allows orgs to respond to incidents and restore services faster. Splunk can process large amounts of data, allowing you to visualize and analyze data in real time.

TIBCO Spotfire is a data analytics tool for business analysts, data scientists, and data-driven leaders. It centralizes structured and unstructured data from multiple sources to provide a single source of truth. As a no-code tool, it uses point-and-click functionality for data exploration and visualization. It can also apply machine learning models to live streaming data to uncover immediate insights.

RapidMiner is a data science platform that takes its name from data mining, or analyzing datasets to uncover patterns and address business problems. The platform offers text mining, data preparation, analysis, visualization, predictive analytics, and automation. While it provides no-code capabilities, analysts can also create custom models using code if needed.

SAS Business Analytics is another way to gather and explore data from multiple sources. It uses a low-code and no-code interface while artificial intelligence provides automated insights and explanations. This allows users to identify and examine patterns faster.

KNIME is an open-source data science platform that supports a range of users and skill levels, from data experts to business leaders and MLOps engineers. It supports data preparation and analysis, monitoring, and team collaboration. It also allows users to create reusable workflows for various data analysis tasks.

Domo is a data experience platform with interactive dashboards, data storytelling and visualization, reporting, and AI-powered analytics. Users can create custom apps and AI/ML models with low-code or no-code tools to incorporate data and AI across business functions.

While the first 15 business analysis platforms are some of the most commonly used, here are a few additional artificial intelligence tools to consider:

• DataRobot

• Databricks

• IBM Cognos Analytics

• Azure Machine Learning

• Alteryx

• H2O.ai

While it’s important to understand the various AI tools for business analytics, don’t neglect the underlying skills you and your teams need to use them effectively. 

Think about it as the difference between learning how to use a drill and learning how to build a house. Specific platforms and tools may change, but foundational skills and underlying competencies will remain the same and enable you and your teams to move ahead no matter what the future holds.

• Business Intelligence Courses and Training

• Foundations of Business Analytics

• Becoming a Business Analyst Learning Path

• Certified Business Analysis Professional (CBAP®)

AI is still relatively new, and most organizations are hesitant to commit budget without proven ROI. You need a strategy to get and prove value from AI and machine learning platforms quickly.

First, understand what you want to achieve with AI technology. For example, do you want to make more accurate predictions about product demand? Develop metrics you can use to track success after implementation and determine if the tool is helping your team reach your goals. 

Just like organizations build cloud communities, consider creating communities for AI adoption. Assembling a team of cross-functional experts will ensure you tailor your tools to business needs, develop best practices, and encourage upskilling.  

Get the tools you need to build buy-in for your tech skill investments.

While AI tools can analyze data and deliver insights, you still need business analysts to:

• Mitigate bias

• Check data quality and data freshness

• Confirm the right data sources are used

• Ensure data privacy and transparency

• Communicate insights to stakeholders

AI-powered data analytics tools give your business analysts more time to focus on strategy and use their data insights to solve business problems. In short: Organizations that adopt AI for business analytics will be one step ahead of those that don’t. 

Check out our collection of beginner, intermediate, and advanced data, AI, and ML courses to give your teams the knowledge and skills they need.