AI for data analytics: A guide to modern tools

Your organization has invested millions in data infrastructure. You have Tableau dashboards, Power BI reports, and a data warehouse full of insights. Yet your analysts still spend most of their time just finding and processing data. Your business users still wait days for simple answers. And your most valuable insights remain trapped in PDFs, presentations, and scattered documents.

You're not alone. According to McKinsey, 60-73% of enterprise data goes unused for analysis. Why? Because traditional analytics tools solve yesterday's problems:

• They require structured data in specific formats

• They need complex ETL processes

• They can't handle unstructured information

• They demand technical expertise to use them effectively

The solution? AI for data analytics. Instead of spending hours processing data or waiting for analyst support, teams can now get instant insights through natural conversation with their data. Modern AI tools for data analytics eliminate traditional barriers, making insights instantly accessible and actionable for everyone.

How to use AI for analyzing data?

Modern AI tools have revolutionized the analysis process in three key ways:

First, they eliminate data preparation headaches. Rather than forcing information into rigid formats, AI can analyze your data where it lives, whether in spreadsheets, PDFs, or business applications. That quarterly sales report trapped in a PDF? AI can analyze it instantly.

Second, they enable natural conversation with data. Instead of learning complex query languages, anyone can ask questions like "What were our top-performing products last quarter?" or "Show me customer satisfaction trends." The AI handles the heavy lifting of connecting and analyzing data from multiple sources, such as Salesforce CRM, Zendesk customer tickets, and NPS surveys stored on your SharePoint.

Third, they automate routine analysis. Those repetitive calculations and report updates that consume your analysts' time? AI handles them automatically, freeing your team to focus on strategic insights and decision-making.

This means your organization can finally unlock the value trapped in its data. Analysts spend less time processing information and more time driving business value. Business users get instant answers to their questions. And insights flow freely across the organization, improving decision-making at every level.

How to choose the right AI tools for data analysis?

Not all solutions are created equal. Many Gen AI tools for analysis still require weeks of setup time, complex data indexing, and expensive data migration, essentially creating new barriers while trying to solve old ones. Organizations need a solution that delivers immediate value without adding infrastructure complexity.

Key evaluation criteria

When evaluating AI tools for data analysis, several factors are critical:

• Speed to value: How quickly can teams start getting insights?

• Ease of implementation: What technical setup is required?

• Data handling: Can it work with existing data in its native format?

• Integration capabilities: Does it connect seamlessly with current tools?

• Security: How does it protect sensitive information?

What is the best AI tool for data analysis?

Qatalog is currently one of the best AI tools for data analysis thanks to its no-index RAG technology that allows data analytics and insight teams to connect directly to their existing data sources and start analyzing immediately — no data copying or migration required.

The system understands the business context and provides synthesized answers from multiple sources. It can analyze numbers from your sales spreadsheets while simultaneously understanding the context of customer emails, support tickets, and presentation slides.

Here’s a great example of using Qatalog’s AI for data analysis across multiple data sources—

If you ask, 'Why did sales drop last quarter?', Qatalog’s AI will connect the dots between your sales figures (structured data in your CRM) and customer feedback (unstructured data in support tickets and emails) to give you a complete picture. It might reveal that while sales numbers declined by 15%, customer complaints about a specific product feature increased during the same period, giving you a complete picture of the trend.

Why do data teams choose Qatalog’s AI for analytics?

The shift to real-time AI analytics transforms how teams work with data:

• Natural language replaces complex queries, making insights accessible to everyone

• Real-time processing eliminates batch delays, enabling faster decision-making

• Automated analysis reduces manual work, freeing analysts for strategic thinking

• Unified analysis of structured and unstructured data provides complete insights

• Predictive capabilities without requiring data science expertise

A real-world example of using AI for data analysis

Here's how an electronics manufacturer uses AI to analyze consumer insights. Their team of four analysts served the entire global organization but struggled with fragmented data and manual processes. Before AI:

• Market share data sat trapped in quarterly PDFs

• Customer feedback required tedious manual NPS calculations

• Valuable insights scattered across Nielsen, Statista, and internal platforms went unused

• Analysts spent most of their time processing data instead of generating insights

Within days of implementing Qatalog's real-time AI for enterprise data discovery, this team revolutionized their workflow. Reports that once took days to analyze now yield insights in seconds. NPS calculations happen automatically. Most importantly, business users across the organization can now find the insights they need instantly without waiting for analyst support.

Analyzing data with AI delivered a clear business impact:

• Insight delivery time cut from days to seconds

• Automated NPS calculations eliminated manual work

• Analysts freed to focus on strategic initiatives

• Self-service insights available across the organization

• Better decision-making through faster access to information

How to get started with AI-powered data analysis

Ready to transform how your organization works with data? Here's how to begin:

1. Evaluate your current challenges

Start by identifying where your team spends the most time on manual data work. Look for:

• Repetitive calculations and report creation

• Time spent searching for information

• Delays in delivering insights to stakeholders

2. Try Qatalog's free 14-day trial

• Connect your first data source in minutes

• Experience immediate insights without complex setup

• Test with your actual business questions

• See how your team can benefit from AI-powered analytics

3. See value immediately

Unlike traditional analytics implementations that take months to show results, you can:

• Start asking questions about your data on day one

• Get immediate answers from your existing data sources

• Share insights across your team

• Measure the impact on your workflow

How does Qatalog’s AI complement traditional BI tools?

While BI tools like Tableau and Power BI excel at structured data visualization and reporting, Qatalog adds a new dimension to your analytics capabilities by:

• Working with your data where it lives; no ETL or data warehousing required

• Understanding natural language questions about your business data

• Processing information in real-time through direct API connections

• Analyzing unstructured data like documents, emails, and support tickets alongside structured data

• Automating analysis that would typically require manual data processing

What types of analysis can Qatalog AI perform?

Rather than replacing your BI tools, Qatalog AI enables new types of analysis:

• Unified search and analysis across multiple data sources

• Auto-calculation of metrics from scattered data (like NPS scores)

• Trend identification across both structured and unstructured sources

• Pattern detection in customer feedback and communications

• Real-time querying of live data sources

For example, while your BI dashboard shows sales metrics, Qatalog can help you understand why those metrics changed by analyzing customer feedback, support tickets, and market data simultaneously, connecting dots that would be impossible to link in traditional BI tools.

Is data secure while using AI analytics tools?

Yes, Qatalog ensures secure enterprise search and analytics. Its core architecture is designed with enterprise data security in mind. Unlike other AI solutions that copy and store sensitive data, Qatalog works directly with your information where it lives, adhering to existing permissions and security protocols. This approach eliminates common data governance and compliance concerns, enabling faster and safer analytics deployments.

FAQ

Can AI analyze Excel data?

Modern AI data analytics tools like Qatalog can analyze Excel files instantly. Whether you're working with financial models, sales reports, or customer data, the AI can understand your spreadsheets in their native format without restructuring or reformatting your data.

Can AI analyze information in PDF reports?

Qatlaog’s AI can now extract and analyze this information automatically. The system understands tables, charts, and text within PDFs, making historical reports as searchable and analyzable as live data.

Can AI analyze big data?

What technical skills are needed to handle AI in data analytics?

One of the biggest advantages of using AI in data analytics is that it doesn't require technical expertise. You can gain insights from your data by asking questions in plain English. No coding, SQL, or data science skills are needed. Qatalog's natural language interface means your marketing team can analyze campaign performance, sales can track customer trends, and executives can explore market dynamics – all without writing a single line of code.

What is the ROI of implementing AI to analyze data?

Organizations implementing Qatalog typically see returns in three key areas:

First, there's the immediate time savings. When analysts spend 70% less time processing data, you're not just saving their time – you're enabling them to focus on strategic work that drives business value.

Second, faster insights lead to better decisions. When your team can instantly answer questions that previously took days to research, they make more informed choices and respond more quickly to market changes.

Third, democratizing access to insights means more people making data-driven decisions. Instead of insights being bottlenecked through analytics teams, everyone can access the information they need, when they need it.

Is there AI to analyze data for personal use?

While most AI tools are designed for business data analysis, the growing trend of democratizing analytics has made AI data analysis more accessible for personal use. Solutions like Qatalog can be valuable for professionals managing complex personal or small business data needs.

For example, a wedding planning business could use Qatalog to analyze information scattered across various tools:

• Emails with vendor quotes and client communications

• Google Sheets tracking multiple wedding budgets and guest lists

• Calendar appointments for venue visits and client meetings

• Documents and inspiration boards in Google Drive

• Task lists and timelines across different projects

This level of analysis goes beyond gen AI tools like ChatGPT by:

• Connecting multiple data sources in real-time

• Understanding context across different types of information

• Automating repetitive analysis tasks

• Maintaining secure access to sensitive client information

• Enabling quick insights across all customer projects

While Qatalog is built for enterprise-grade analytics, its no-index RAG technology and intuitive interface make it equally powerful for professionals managing complex personal or small business data needs. The same features that help large organizations make sense of their data can help individuals and small teams work more efficiently with their information.

Key takeaway

While other AI tools for analysis are still figuring out basic Gen AI integration, Qatalog is pushing the boundaries of what's possible. Our roadmap for early 2025 includes enhanced visualization capabilities, an expanded integration ecosystem, and even faster processing times. We're not just solving today's analytics challenges with artificial intelligence — we're building the future of enterprise decision-making.

You don't have to take our word for it. We offer a 14-day free trial with full access to our Pro-tier features. Connect your first data source in minutes and experience the future of analytics firsthand. There's no commitment required – just immediate results.