Prime 8 Consulting’s Post

How AI is Revolutionizing Business Intelligence

#AI can help prevent human mistakes in data analytics! AI’s impact in data analytics is undeniable. It can eliminate and prevent human error working with data and do it much faster than anyone could hope to accomplish. As we enter a new era of data analytics, AI will continue to play a significant role. ExperienceFlow.AI can identify and implement AI’s role for your enterprise. Click here to know more! https://lnkd.in/d5J9r6jY Giri Srinivas ATG A Anand R. Arjun I. Srinivas Koppolu Rama Mohan Venkata Kadayinti #decisionintelligence #artificialgeneralintelligence #autonomousenterprise #digitalnervoussystem

Improving your Machine Learning Model Performance is sometimes futile AI models employed in a business enterprise are weighed in with respect to the KPIs. AI models cater to the business enterprise productively only when they augment the business growth and success which is marked using KPIs associated with that particular business. The business enterprise works much efficiently when data analytics and other departments are seamlessly integrated for a particular goal. Monitoring and correlating help in bridging the gap between performance analysis and business growth. Thus, it is essential to view every improvement in the machine learning pipeline through the lens of KPI, this helps in quantifying what factors affect the business growth making the data scientist or engineer wary of how to tweak the model for optimum business growth. #machinelearning #artificialintelligence #predictiveanalytics #analytics #econometrics #technology #kpiimplementation

Statistical analysis of rounded or binned data https://lnkd.in/dUKWTAVF AI News, AI, AI tools, Innovation, itinai.com, LLM, Matthias Plaue, t.me/itinai, Towards Data Science - Medium 🚀 **Practical AI Solutions for Middle Managers** Are you looking to leverage the statistical analysis of rounded or binned data to drive your company's success with AI? Discover how AI can transform your operations, enhance decision-making, and drive growth. 🔍 **Understanding the Impact** The article "On the Statistical Analysis of Rounded or Binned Data" sheds light on the challenges of rounding or binning in statistical analyses. It delves into Sheppard's corrections and total variation bounds, offering insights into addressing errors when computing statistical values from rounded or binned data. 📊 **Practical Insights** Sheppard's corrections provide approximations to estimate original data from rounded values, offering valuable insights when the probability density function is smooth and the sample size is moderate. Total variation bounds and Fisher information-based bounds help constrain the error in computing the mean based on rounded or binned data. 🤖 **AI Solutions for Your Business** Looking to harness the power of AI for your company? Connect with us at hello@itinai.com to explore how AI can redefine your operations, identify automation opportunities, and drive performance through strategic KPI management. 🌟 **Spotlight on AI Sales Bot** Discover our AI Sales Bot at itinai.com, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Explore how AI can revolutionize your sales processes and customer engagement. 🔗 **Useful Links** - AI Lab in Telegram @aiscrumbot – free consultation - [Statistical analysis of rounded or binned data](link to the article) - Towards Data Science – Medium - Twitter – @itinaicom Let's unlock the potential of AI for your business together! #AISolutions #AIforMiddleManagers #DataAnalysis #AIInnovation

𝗠𝗮𝗸𝗶𝗻𝗴 𝗦𝗲𝗻𝘀𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮: 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗔𝗜 Imagine you're organizing a photo album. You have tons of pictures, but not all are equally important. Some might be blurry or irrelevant. That's where sorting comes in. In the world of data science and AI, feature selection and pattern extraction are like your sorting tools! 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻:: Choosing the Right Photos What it is : Selecting the most useful information (features) from your data. Think of it like: Picking the best pictures for your album based on relevance and clarity. Why it's important: Reduces complexity, improves model performance, and helps us understand which features matter most. Example: In a spam email classifier, features might be words or phrases. Feature selection helps identify the most indicative words for spam, like "free" or "urgent." 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Finding Hidden Themes What it is : Identifying new patterns or relationships within your data by creating entirely new features. Think of it like : Grouping related pictures in your album to tell a story. Why it's important : Uncovers hidden insights, simplifies complex data, and creates more powerful features for models. Example: Analyzing customer purchase history. Pattern extraction might create a new feature combining items frequently bought together, helping recommend similar products. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Feature selection chooses from existing data. Pattern extraction creates entirely new features. So, which one to use? It depends! Sometimes, both are helpful. Feature selection is good for interpretable models where you want to understand why something works. Pattern extraction is great for complex problems where hidden patterns might hold the key. Remember: Both techniques help us make sense of data and build better AI models! Muhammad Irfan Dr. Sheraz Naseer - (PhD Artificial Intelligence, Data Science) Muhammad Haris Tariq Mehran Ali Shaheryar Yousaf

🚀 Embracing the Future: AI's Impact on Data Analysts 🚀 The rapid rise of Artificial Intelligence (AI) is transforming various aspects of our professional landscape, and data analysts find themselves at the forefront of this exciting revolution. While some may fear that AI will replace data analysts entirely, I firmly believe that it will revolutionize the role rather than replace it. In the past, data analysts spent considerable time on manual tasks like data cleaning and visualization. However, AI has now empowered us to automate these processes, allowing data analysts to shift their focus towards more strategic endeavors. Imagine utilizing AI to develop predictive models, uncover fraud, and optimize marketing campaigns - these opportunities are now within our grasp! To thrive in this evolving landscape, data analysts must embrace AI and continually expand their skill set. Those who adeptly leverage AI's potential will be able to automate more tasks and delve deeper into complex strategic analysis. From generating invaluable insights from massive datasets to crafting interactive visualizations, the possibilities are limitless! As businesses increasingly rely on data-driven decisions, the value of data analysts equipped with AI skills will soar. Our ability to translate data into actionable recommendations will be a critical asset in shaping the future success of companies across industries. Conversely, those who resist integrating AI into their skill set risk falling behind. Embracing AI is not just a choice but a necessity to remain competitive in the data analytics field. By adapting and mastering AI-powered tools, we can stay ahead of the curve and unlock the full potential of data-driven possibilities. In conclusion, let's welcome AI as an empowering ally rather than a formidable foe. As data analysts, we have an unprecedented opportunity to reshape our role, making it more impactful and rewarding than ever before. Together, we can embrace the potential of AI and revolutionize the world of data analytics! #AI #DataAnalytics #FutureReady

Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area https://lnkd.in/dyfsUsNX AI News, AI, AI tools, Innovation, itinai.com, Jin Cui, LLM, t.me/itinai, Towards Data Science - Medium 🚀 Deriving a Score to Show Relative Socio-Economic Advantage and Disadvantage of a Geographic Area 🚀 🔍 Motivation: Publicly available data on socio-economic characteristics of geographic areas in Australia, such as income, occupation, education, employment, and housing, presents an opportunity to rank these areas based on their advantage or disadvantage. 🔧 The Problem: Understanding which data points explain the most variations is crucial for deriving a score that accurately reflects the socio-economic status of different geographic locations. 📊 The Data: Utilizing data from the Australian Bureau of Statistics (ABS) at the Statistical Area 1 (SA1) level, we have a detailed dataset to analyze and derive meaningful insights. 🔍 The Steps: Our Python code showcases the application of Principal Component Analysis (PCA) to derive a socio-economic score, which is then validated against the Index of Economic Resource (IER) published by ABS. ✅ The Validation: The derived scores are rigorously validated against the published IER scores to ensure accuracy and alignment with the ABS methodology. 🎯 Concluding Thought: By leveraging dimensionality reduction techniques like PCA, we can effectively calibrate socio-economic scores, providing valuable insights for informed decision-making. 🌟 Spotlight on a Practical AI Solution: Explore our AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. 🔗 Useful Links: - AI Lab in Telegram @aiscrumbot – free consultation - Towards Data Science – Medium - Twitter – @itinaicom If you're looking to harness the power of AI to stay competitive and drive business growth, connect with us at hello@itinai.com and stay tuned on our Telegram channel or Twitter for continuous insights into leveraging AI. Let's unlock the potential of AI together! #AISolutions #AIforBusiness #DataInsights

How Much Data Do We Need? Balancing Machine Learning with Security Considerations https://lnkd.in/dW_myAhJ AI News, AI, AI tools, Innovation, itinai.com, LLM, Stephanie Kirmer, t.me/itinai, Towards Data Science - Medium 🚀 **AI Solutions for Middle Managers: Balancing Machine Learning with Security Considerations** Data scientists thrive on large volumes of data, but organizations must balance this with privacy and security needs. Finding a middle ground is crucial. 🔒 **Data Science vs Security/IT: A Battle for the Ages** Data scientists are data hungry, but organizations must consider data security and privacy. Acquiring and retaining data is essential, but it's important to avoid becoming data hoarders. 📈 **Institutional Considerations** Data retention often starts with a scarcity of data, leading to the collection of vast amounts of information. However, navigating data security regulations and public demand for better protection has become increasingly complex. 💡 **AI Solutions for Your Company** 1. **Identify Automation Opportunities**: Use AI to enhance key customer interactions. 2. **Define KPIs**: Ensure AI efforts align with measurable business outcomes. 3. **Select an AI Solution**: Choose tools that suit your needs and offer customization. 4. **Implement Gradually**: Start with a pilot, gather data, and expand AI usage prudently. 🤝 **Connect with Us for AI KPI Management Advice** For insights and advice on leveraging AI for KPI management, reach out to us at hello@itinai.com. Stay updated on leveraging AI by following us on Telegram or Twitter. 🌟 **Practical AI Solution Spotlight** Discover our AI Sales Bot at itinai.com, designed to automate customer engagement round-the-clock and manage interactions across all customer journey stages. 🔗 **Useful Links** - AI Lab in Telegram: @aiscrumbot for free consultation - [Towards Data Science – Medium](https://lnkd.in/debe8PDP) - Twitter: @itinaicom Explore how AI can transform your sales processes and customer engagement at itinai.com.

How will AI transform data analytics? Ahead of his presentation at the 2023 Financial Services Exchange, Faizal Chaudhury, CPA, CIA, CGMA, talks about AI's impact on data analytics. https://loom.ly/w-5-n6w #IaMagIIA #dataanalytics