#Automated Analytics

[msn-technology]

Top 9 AI Tools for Data Analytics in 2025

In 2025, the landscape of data analytics is rapidly evolving, thanks to the integration of artificial intelligence (AI). AI-powered tools are transforming how businesses analyze data, uncover insights, and make data-driven decisions. Here are the top nine AI tools for data analytics that are making a significant impact: 1. ChatGPT by OpenAI ChatGPT is a powerful AI language model developed by…

[abathurofficial]

Abathur

At Abathur, we believe technology should empower, not complicate.

Our mission is to provide seamless, scalable, and secure solutions for businesses of all sizes. With a team of experts specializing in various tech domains, we ensure our clients stay ahead in an ever-evolving digital landscape.

Why Choose Us? Expert-Led Innovation – Our team is built on experience and expertise. Security First Approach – Cybersecurity is embedded in all our solutions. Scalable & Future-Proof – We design solutions that grow with you. Client-Centric Focus – Your success is our priority.

[productsiddha]

From Data to Decisions: Leveraging Product Analytics and AI Services for Faster B2B Innovation

In today’s competitive B2B landscape, innovation isn’t just about having a great product idea. It’s about bringing that idea to life faster, smarter, and with precision. That means making every decision based on real data, not guesswork. At Product Siddha, we help businesses unlock faster B2B innovation by combining the power of product analytics and AI services into one seamless strategy.

Why B2B Innovation Fails Without Data-Driven Insight

Most B2B companies struggle to innovate at scale because they lack visibility into what users actually do. Product teams launch features based on assumptions. Marketing teams operate without a feedback loop. Sales teams miss opportunities due to fragmented data. This disconnect creates wasted effort and missed growth.

Product analytics is the solution to this problem. When integrated with AI services, you don’t just track user behavior — you predict it. This lets you make smarter decisions that directly improve your product roadmap, customer experience, and business outcomes.

The Power of Product Analytics in B2B Growth

Product analytics turns user behavior into actionable insight. Instead of relying on vanity metrics, Product Siddha helps you understand how real people interact with your product at every stage. We implement tools that give you a complete view of the user journey — from first touchpoint to long-term retention.

With powerful product analytics, you can:

Identify high-impact features based on real usage

Spot friction points and user drop-offs quickly

Personalize product experiences for higher engagement

Improve onboarding, reduce churn, and boost ROI

This is not just reporting. It’s clarity. It’s control. And it’s the foundation of faster B2B innovation.

Accelerate Outcomes with AI Services That Work for You

While product analytics shows you what’s happening, AI services help you act on that data instantly. Product Siddha designs and builds low-code AI-powered systems that reduce manual work, automate decisions, and create intelligent workflows across teams.

With our AI services, B2B companies can:

Automatically segment users and personalize messaging

Trigger automated campaigns based on user behavior

Streamline product feedback loops

Deliver faster support with AI chatbots and smart routing

Together, AI and analytics make your product smarter and your business more efficient. No more delayed decisions. No more data silos. Just continuous improvement powered by automation.

Our Approach: Build, Learn, Optimize

At Product Siddha, we believe innovation should be fast, measurable, and scalable. That’s why we use a 4-step framework to integrate product analytics and AI services into your workflow.

Build Real, Fast

We help you launch an MVP with just enough features to test real-world usage and start gathering data.

Learn What Matters

We set up product analytics to capture user behavior and feedback, turning that information into practical insight.

Stack Smart Tools

Our AI services integrate with your MarTech and product stack, automating repetitive tasks and surfacing real-time insights.

Optimize with Focus

Based on what you learn, we help you refine your product, personalize your messaging, and scale growth efficiently.

Why Choose Product Siddha for B2B Innovation?

We specialize in helping fast-moving B2B brands like yours eliminate complexity and move with clarity. At Product Siddha, we don’t just give you data or automation tools — we build intelligent systems that let you move from data to decisions in real time.

Our team combines deep expertise in product analytics, AI automation, and B2B marketing operations. Whether you’re building your first product or scaling an existing one, we help you:

Reduce time-to-market

Eliminate development waste

Align product and growth goals

Launch with confidence

Visit Product Siddha to explore our full range of services.

Let’s Turn Insight into Innovation

If you’re ready to use product analytics and AI services to unlock faster B2B innovation, we’re here to help. Product Siddha builds smart, scalable systems that help your teams learn faster, move faster, and grow faster.

Call us today at 98993 22826 to discover how we can turn your product data into your biggest competitive advantage.

[magtecbusinesssolutions]

Transform your business with Magtec ERP! 🌐��� Discover endless possibilities on a single platform. Book a demo today and see how we can elevate your operations to the next level! 🚀📈

[digital-specialist]

Acadecraft Partners with Wadhwani Foundation's Government Digital Transformation Initiative to Develop eLearning Courses

[fraoula1]

Python for Data Science: From Beginner to Expert – A Complete Guide!

Python has become the go-to language for data science, thanks to its flexibility, powerful libraries, and strong community support. In this video, we’ll explore why Python is the best choice for data scientists and how you can master it—from setting up your environment to advanced machine learning techniques.

🔹 What You'll Learn:

✅ Why Python is essential for data science

✅ Setting up Python and key libraries (NumPy, Pandas, Matplotlib) ✅ Data wrangling, visualization, and transformation

✅ Building machine learning models with Scikit-learn

✅ Best practices to enhance your data science workflow 🚀 Whether you're a beginner or looking to refine your skills, this guide will help you level up in data science with Python. 📌 Don’t forget to like, subscribe, and hit the notification bell for more data science and Python content!

[truetechreview]

Top 5 DeepSeek AI Features Powering Industry Innovation

Table of Contents1. The Problem: Why Legacy Tools Can’t Keep Up2. What Makes DeepSeek AI Unique?3. 5 Game-Changing DeepSeek AI Features (with Real Stories)3.1 Adaptive Learning Engine3.2 Real-Time Anomaly Detection3.3 Natural Language Reports3.4 Multi-Cloud Sync3.5 Ethical AI Auditor4. How These Features Solve Everyday Challenges5. Step-by-Step: Getting Started with DeepSeek AI6. FAQs: Your…

[xlsdesignt]

what u think, to much colour, or less?

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[dtc-infotech]

𝐀𝐫𝐞 𝐘𝐨𝐮 𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬? Discover Microsoft Dynamics 365 Business Central's game-changing power. 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝟏𝟎 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: ❇ Cloud-Based ERP ❇ Boosted Productivity ❇ Cost Avoidance ❇ Seamless Microsoft Integration ❇ Scalability ❇ Data-Driven Insights ❇ Simplified Customization ❇ Enhanced Employee Experience ❇ Revenue Growth Enablement ❇ Cost Transparency 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝗻𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀, 𝗮𝗺𝗽𝗹𝗶𝗳𝘆 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗽𝗿𝗼𝗽𝗲𝗹 𝗴𝗿𝗼𝘄𝘁𝗵!

[innovatexblog]

How Large Language Models (LLMs) are Transforming Data Cleaning in 2024

Data is the new oil, and just like crude oil, it needs refining before it can be utilized effectively. Data cleaning, a crucial part of data preprocessing, is one of the most time-consuming and tedious tasks in data analytics. With the advent of Artificial Intelligence, particularly Large Language Models (LLMs), the landscape of data cleaning has started to shift dramatically. This blog delves into how LLMs are revolutionizing data cleaning in 2024 and what this means for businesses and data scientists.

The Growing Importance of Data Cleaning

Data cleaning involves identifying and rectifying errors, missing values, outliers, duplicates, and inconsistencies within datasets to ensure that data is accurate and usable. This step can take up to 80% of a data scientist's time. Inaccurate data can lead to flawed analysis, costing businesses both time and money. Hence, automating the data cleaning process without compromising data quality is essential. This is where LLMs come into play.

What are Large Language Models (LLMs)?

LLMs, like OpenAI's GPT-4 and Google's BERT, are deep learning models that have been trained on vast amounts of text data. These models are capable of understanding and generating human-like text, answering complex queries, and even writing code. With millions (sometimes billions) of parameters, LLMs can capture context, semantics, and nuances from data, making them ideal candidates for tasks beyond text generation—such as data cleaning.

To see how LLMs are also transforming other domains, like Business Intelligence (BI) and Analytics, check out our blog How LLMs are Transforming Business Intelligence (BI) and Analytics.

Traditional Data Cleaning Methods vs. LLM-Driven Approaches

Traditionally, data cleaning has relied heavily on rule-based systems and manual intervention. Common methods include:

Handling missing values: Methods like mean imputation or simply removing rows with missing data are used.

Detecting outliers: Outliers are identified using statistical methods, such as standard deviation or the Interquartile Range (IQR).

Deduplication: Exact or fuzzy matching algorithms identify and remove duplicates in datasets.

However, these traditional approaches come with significant limitations. For instance, rule-based systems often fail when dealing with unstructured data or context-specific errors. They also require constant updates to account for new data patterns.

LLM-driven approaches offer a more dynamic, context-aware solution to these problems.

How LLMs are Transforming Data Cleaning

1. Understanding Contextual Data Anomalies

LLMs excel in natural language understanding, which allows them to detect context-specific anomalies that rule-based systems might overlook. For example, an LLM can be trained to recognize that “N/A” in a field might mean "Not Available" in some contexts and "Not Applicable" in others. This contextual awareness ensures that data anomalies are corrected more accurately.

2. Data Imputation Using Natural Language Understanding

Missing data is one of the most common issues in data cleaning. LLMs, thanks to their vast training on text data, can fill in missing data points intelligently. For example, if a dataset contains customer reviews with missing ratings, an LLM could predict the likely rating based on the review's sentiment and content.

A recent study conducted by researchers at MIT (2023) demonstrated that LLMs could improve imputation accuracy by up to 30% compared to traditional statistical methods. These models were trained to understand patterns in missing data and generate contextually accurate predictions, which proved to be especially useful in cases where human oversight was traditionally required.

3. Automating Deduplication and Data Normalization

LLMs can handle text-based duplication much more effectively than traditional fuzzy matching algorithms. Since these models understand the nuances of language, they can identify duplicate entries even when the text is not an exact match. For example, consider two entries: "Apple Inc." and "Apple Incorporated." Traditional algorithms might not catch this as a duplicate, but an LLM can easily detect that both refer to the same entity.

Similarly, data normalization—ensuring that data is formatted uniformly across a dataset—can be automated with LLMs. These models can normalize everything from addresses to company names based on their understanding of common patterns and formats.

4. Handling Unstructured Data

One of the greatest strengths of LLMs is their ability to work with unstructured data, which is often neglected in traditional data cleaning processes. While rule-based systems struggle to clean unstructured text, such as customer feedback or social media comments, LLMs excel in this domain. For instance, they can classify, summarize, and extract insights from large volumes of unstructured text, converting it into a more analyzable format.

For businesses dealing with social media data, LLMs can be used to clean and organize comments by detecting sentiment, identifying spam or irrelevant information, and removing outliers from the dataset. This is an area where LLMs offer significant advantages over traditional data cleaning methods.

For those interested in leveraging both LLMs and DevOps for data cleaning, see our blog Leveraging LLMs and DevOps for Effective Data Cleaning: A Modern Approach.

Real-World Applications

1. Healthcare Sector

Data quality in healthcare is critical for effective treatment, patient safety, and research. LLMs have proven useful in cleaning messy medical data such as patient records, diagnostic reports, and treatment plans. For example, the use of LLMs has enabled hospitals to automate the cleaning of Electronic Health Records (EHRs) by understanding the medical context of missing or inconsistent information.

2. Financial Services

Financial institutions deal with massive datasets, ranging from customer transactions to market data. In the past, cleaning this data required extensive manual work and rule-based algorithms that often missed nuances. LLMs can assist in identifying fraudulent transactions, cleaning duplicate financial records, and even predicting market movements by analyzing unstructured market reports or news articles.

3. E-commerce

In e-commerce, product listings often contain inconsistent data due to manual entry or differing data formats across platforms. LLMs are helping e-commerce giants like Amazon clean and standardize product data more efficiently by detecting duplicates and filling in missing information based on customer reviews or product descriptions.

Challenges and Limitations

While LLMs have shown significant potential in data cleaning, they are not without challenges.

Training Data Quality: The effectiveness of an LLM depends on the quality of the data it was trained on. Poorly trained models might perpetuate errors in data cleaning.

Resource-Intensive: LLMs require substantial computational resources to function, which can be a limitation for small to medium-sized enterprises.

Data Privacy: Since LLMs are often cloud-based, using them to clean sensitive datasets, such as financial or healthcare data, raises concerns about data privacy and security.

The Future of Data Cleaning with LLMs

The advancements in LLMs represent a paradigm shift in how data cleaning will be conducted moving forward. As these models become more efficient and accessible, businesses will increasingly rely on them to automate data preprocessing tasks. We can expect further improvements in imputation techniques, anomaly detection, and the handling of unstructured data, all driven by the power of LLMs.

By integrating LLMs into data pipelines, organizations can not only save time but also improve the accuracy and reliability of their data, resulting in more informed decision-making and enhanced business outcomes. As we move further into 2024, the role of LLMs in data cleaning is set to expand, making this an exciting space to watch.

Large Language Models are poised to revolutionize the field of data cleaning by automating and enhancing key processes. Their ability to understand context, handle unstructured data, and perform intelligent imputation offers a glimpse into the future of data preprocessing. While challenges remain, the potential benefits of LLMs in transforming data cleaning processes are undeniable, and businesses that harness this technology are likely to gain a competitive edge in the era of big data.

[glancetelecom]

Businesses seeking to leverage this power can achieve transformative results by prioritizing quality assurance (QA) practices. Integrating real-time analytics allows for continuous improvement, while a strong focus on call center compliance ensures every interaction meets the highest standards. Click Here To Read More: https://rb.gy/p4nen1

[anishmary]

From Zero to Hero: Mastering Data Analytics for Transformation

The concept of "data" has enormous significance in the modern digital world. The enormous amount of information produced every day has revolutionized the way we view the world and move through it. The data analytics industry is one of the major players in this revolution. Let's set out on an adventure to discover the fundamentals of data analytics, consider their importance, and understand how they affect different facets of our lives.

Understanding Data Analytics Analyzing massive data sets to find hidden patterns, correlations, trends, and insights is at the heart of data analytics. It's like sorting through a gold mine of knowledge to find the gold nuggets that will help you make decisions and solve problems. Data analytics isn't just about statistics and figures; it's about turning raw data into compelling narratives that encourage informed decisions.

Why Data Analysis Is Important

The capacity to make sense of all the data in the globe has changed the game. Data analytics enables companies, groups, and people to comprehend their operations, clients, and environments more thoroughly. Here are a few main arguments in favor of data analytics:

Making Informed Decisions: Data-driven insights allow for more precise and knowledgeable decision-making. Companies might modify their tactics in light of current developments and customer preferences.

Enhanced Efficiency: Data analytics uncovers bottlenecks and inefficiencies in processes and operations, allowing for focused changes.

Predictive Skills: By analyzing past data, predictive models can predict future patterns and aid organizations in becoming ready for the future.

Customer insights: Data analytics reveals the preferences and habits of customers, enabling more individualized interactions and higher levels of customer satisfaction.

Risk Management: Identifying potential risks and fraud patterns is made easier with data analytics, reducing vulnerabilities and losses.

Scientific Discoveries: In fields like healthcare and research, data analytics aids in uncovering groundbreaking insights and advancements.

The Data Analytics Process

Data analytics involves a systematic approach that follows several key steps:

Defining Objectives: Clearly state the objectives and issues you hope to resolve through data analysis.

Data Collection: Collect pertinent data from a variety of sources, making sure it is reliable and representative. Data Cleaning: Involves altering it to ensure accurate analysis.

Exploratory Analysis: Investigate the data in depth to find patterns, trends, and early insights.

Modeling: Building statistical models or algorithms to forecast outcomes or comprehend correlations in the data is known as modeling.

Interpretation: Draw conclusions from your analysis's findings and connect them to your original goals.

Visualization: To make your findings easier to understand, present them using visual tools like charts, graphs, and dashboards.

Action: Implement the learnings from data analysis to inspire positive changes and actions.

Data analytics has become an effective means for turning data into useful insights. By bridging the gap between information overload and useful knowledge, it facilitates decision-making, ignites innovation, and opens doors. A fascinating and fruitful trip may be had by anyone interested in learning more about data analytics, whether they are a business owner, researcher, or just curious. So, take part in uncovering the secrets of the data and grasp the power of data analytics to transform. If you need help implementing data analytics in your organization or if you want to learn more, you should consult professionals or sign up for specialized courses. For instance, the ACTE Institute offers comprehensive data analytics training courses that can provide you the knowledge and skills you need.

[sigzentechnologies]

The Future of Business Management: ERPNext CRM By Sigzen Technologies

In the ever-evolving landscape of business management, staying ahead of the curve is paramount. One such innovative solution leading the charge is ERPNext CRM, a cloud-based Enterprise Resource Planning system that is reshaping the way organizations approach business automation and workflow optimization. As we delve into the future of business management, let’s explore the game-changing features…

[testforce-insights]

AI-Powered Predictive Analytics: Smarter Testing with Provar

If you're using Salesforce, you've probably heard terms like AI and Predictive Analytics being tossed around. But what do they actually mean for your day-to-day work? And more importantly — how can they help make testing smarter?

In this blog, we’ll walk through how you can use AI-powered predictive analytics alongside a test automation tool like Provar to improve testing accuracy, reduce effort, and keep up with fast-moving development cycles — in a way that’s easy to understand and implement.

What is AI-Powered Predictive Analytics?

Let’s start with the basics.

AI-powered predictive analytics is all about using past data to predict future outcomes. In Salesforce, for example, Einstein AI can help estimate things like:

How likely a lead is to convert

Which bugs might impact release quality

How long a test suite might take to run

Instead of relying solely on gut feel or static test plans, predictive analytics uses actual test data to flag risks, optimize test coverage, and surface smarter priorities.

Why Predictive Analytics Matters Now

As cloud applications like Salesforce grow more complex, traditional intuition-based decision-making just can’t keep up. In QA especially, teams often face problems like:

Testing delays due to lack of clear priorities

Uncertainty around what areas should be tested first

Missed critical bugs despite repeated coverage

AI helps cut through the noise. With data-backed insights, teams can focus on what really matters — targeted testing, faster cycles, and higher confidence.

How Provar Uses AI-Powered Predictive Analytics

Provar is more than just a test automation tool — it’s designed specifically for Salesforce, with built-in intelligence to help teams test smarter. Here’s how Provar leverages predictive analytics:

Key Capabilities of Provar

Salesforce Metadata Integration: Provar works natively with Salesforce metadata, allowing it to detect UI changes automatically and update test cases without breaking.

Failure Prediction: Based on historical test logs, Provar can flag which test cases are likely to fail in upcoming runs, helping you fix issues before they hit production.

Automatic Test Prioritization: When a new feature or code change is introduced, Provar identifies the most impacted areas and recommends which tests to run first.

Test Duration Forecasting: Provar estimates how long each test suite will take, allowing teams to plan better and fit testing into tight release windows.

These features remove the guesswork, allowing QA teams to work proactively rather than reactively.

How to Start Using Predictive Analytics in Provar

1. Collect and Store Test Data

Begin by consistently running your tests in Provar and saving the logs. The more data you feed the system, the smarter your predictions become.

2. Link With Change Management

Provar easily integrates with CI/CD tools like Jenkins and Git. By syncing with your change history, Provar can automatically assess where the risks are after a code update.

3. Use AI Recommendations

Provar will start suggesting which tests to run, when to run them, and even highlight which areas are at highest risk — so you’re always testing with impact, not just effort.

Benefits of AI-Powered Predictive Analytics in Testing

This isn't just about fancy dashboards — predictive analytics brings tangible, real-world benefits to teams using Provar for test automation:

Key Benefits

Risk Management: Spot high-risk areas early, before they derail releases

Efficient Resource Allocation: Focus on what’s critical instead of re-running everything

Better Product Quality: Find bugs earlier and reduce negative customer impact

Smarter Collaboration: Make decisions based on data, not debates

Real-World Example: Insurance Industry

One insurance company that ran over 1,000 test cases per release adopted Provar’s AI-powered testing strategy and saw the following results:

30% reduction in total test cases

Twice as many critical bugs caught early

Zero delays in the release timeline

These kinds of results are possible because Provar is built specifically for Salesforce and works seamlessly with Salesforce-native AI features like Einstein for even stronger insights.

Why Provar Stands Out

Provar isn’t just another automation tool. It’s a smart, integrated, and AI-powered solution that helps you understand:

What to test

When to test it

How to optimize testing every step of the way

Compared to generic tools, Provar offers better adaptability to UI changes, stronger Salesforce metadata alignment, and more intelligent analysis features.

Wrap-Up: Smarter Testing Starts with Provar

By integrating AI-powered predictive analytics into your QA process, you’re not just automating — you’re transforming the way testing works. You’ll make better decisions, test more strategically, and release with confidence.

With Provar, you gain a Salesforce-native testing platform that empowers your team to stay ahead of change and minimize risk — all while saving time and resources.

📘 Want to learn more? Explore Provar’s predictive capabilities and real-world use cases at Provar.com or request a free demo to see AI-powered testing in action!

[digital-specialist]

Qatar Partners With Scale AI for AI-Powered Digital Transformation of Government Services

[projectmates]

Construction Document Collaboration Platform Enhance your team’s collaboration with Projectmates’ document-sharing platform. The software allows teams to work on documents simultaneously, leave comments, and track changes in real time. Whether it’s drawings, contracts, or RFIs, the system ensures everyone is on the same page, reducing errors and improving the decision-making process.