ORLANDO, FL: Qlik^®, a global leader in data integration, data quality, analytics, and artificial intelligence, today introduced its new agentic experience at Qlik Connect^® 2025. The agentic experience will provide a single, conversational interface allowing users across the enterprise to interact naturally with data, using specialized AI agents to quickly uncover insights, drive faster decisions, and boost productivity, bringing new simplicity to complex data-driven workflows.

Qlik has consistently pushed forward the possibilities of data exploration, visualization, and analytics. At the heart of this continuous innovation is the Qlik engine—a unique technology that indexes relationships across data, enabling the discovery of unexpected connections. The agentic experience builds directly on this engine, expanding the ability of both users and agents to intuitively access these relationships, surface insights, and take action across diverse data.

The agentic experience will enable users to access insights and take action simply by engaging in natural language dialogue. Operating seamlessly across Qlik Cloud—including data integration, data quality, and analytics solutions—the experience removes friction, providing fast, intuitive visibility for smarter decisions and higher productivity.

At Qlik Connect, the company demonstrated how specialized AI agents will support users within this new experience:

• Introduced last year for unstructured data, Qlik Answers will bring together structured and unstructured data in a single natural language experience, delivering trusted answers and enabling automated actions.
• Revealed for the first time at the event, a discovery agent will proactively identify critical risks and opportunities across applications and datasets, presenting insights and recommended actions through a personalized feed.
• Demonstrated as a concept, a pipeline agent will allow users to describe desired business outcomes conversationally, prompting automated recommendations and the design of necessary data pipelines.

As enterprises face unpredictable market conditions and increasing pressure to make critical decisions rapidly, investments in AI have grown. With its agentic experience, Qlik is focused on helping customers turn data into timely, high-quality decisions and results. Qlik’s agentic experience is specifically designed to empower teams to accelerate both decisions and productivity in rapidly changing environments.

“This new agentic experience is about removing the distance between data, decisions, and outcomes,” said Mike Capone, CEO of Qlik. “People want a seamless, conversational way to engage with their data—one that fits naturally into their work and delivers clear, trusted answers in context. We’ve built this experience to reflect how business decisions are made.”

“There’s a growing demand for AI that does more than generate responses—enterprises want systems that can reason across complex data, explain their outputs, and drive action,” said Megha Kumar, Research Vice President, Worldwide Analytics & AI, IDC. “Qlik Answers combines structured and unstructured data with automation in a governed, explainable framework. It’s a strong example of how agentic AI can support real enterprise decision-making.”

“We’re under constant pressure to make faster, better decisions with data that’s scattered across the business,” said Yuzuru Fukuda, Corporate Executive Officer – Senior EVP, Enterprise Division CEO, Fujitsu. “The ability to ask a question and get a trusted, contextual answer—across structured reports, unstructured content, and automated workflows—is exactly the kind of capability we’ve been waiting for. It has the potential to remove a lot of friction from how decisions get made.”

The Qlik agentic experience is scheduled to begin rolling out this summer, starting with private previews. Through live demos and executive briefings, teams attending Qlik Connect can experience the agentic capabilities firsthand.

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Matplotlib is a versatile, open-source plotting library for Python, originally developed by John D. Hunter in 2003. It allows users to create static, animated, and interactive visualizations with ease, making it a cornerstone of scientific computing and data analysis.

Key Features

• Wide Range of Plot Types: Line graphs, bar charts, histograms, scatter plots, pie charts, and more.
• Highly Customizable: Control over every element—axes, labels, legends, colors, markers, and styles.
• Integration: Works seamlessly with NumPy, Pandas, and other scientific libraries.
• Interactive Backends: Supports GUI toolkits like Tkinter, Qt, and web-based interfaces like Jupyter Notebooks.

Anatomy of a Matplotlib Plot

• Figure: The overall window or page that everything is drawn on.
• Axes: The area where data is plotted (can be multiple per figure).
• Axis: The x and y axes within each axis.
• Plot Elements: Titles, labels, legends, gridlines, and data markers.

Example Code

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]

y = [0, 1, 4, 9, 16]

plt.plot(x, y, marker=’o’, label=’Squared Values’)

plt.title(‘Simple Line Plot’)

plt.xlabel(‘X Axis’)

plt.ylabel(‘Y Axis’)

plt.legend()

plt.grid(True)

plt.show()

Real-World Use Cases of Matplotlib

1. Business Performance Tracking

Companies use Matplotlib to visualize monthly sales, expenses, and profit margins. For example:

months = [‘Jan’, ‘Feb’, ‘Mar’, ‘Apr’]

sales = [10000, 12000, 15000, 17000]

costs = [7000, 8000, 9000, 10000]

plt.plot(months, sales, label=’Sales’)

plt.plot(months, costs, label=’Costs’)

plt.title(‘Monthly Business Performance’)

plt.xlabel(‘Month’)

plt.ylabel(‘Amount’)

plt.legend()

plt.grid(True)

plt.show()

This helps managers spot trends and make informed decisions.

2. Investment Strategy Comparison

Financial analysts use Matplotlib to compare growth across different investment strategies—conservative vs. aggressive portfolios—over time.

3. Machine Learning Model Evaluation

Data scientists visualize model accuracy, loss curves, and confusion matrices to evaluate performance. Matplotlib is often paired with libraries like scikit-learn and TensorFlow for this purpose.

4. Geospatial Data Visualization

Using Matplotlib’s Basemap Researchers use a toolkit to plot geographic data—such as earthquake locations or climate patterns—on maps.

5. Scientific Research

Scientists use Matplotlib to plot experimental results, such as temperature changes, chemical concentrations, or astronomical observations. It’s essential for producing publication-quality figures.

6. Customer Behavior Analysis

Retailers analyze purchase patterns, peak shopping hours, and product popularity using Matplotlib to visualize correlations and distributions.

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