Blend’s Post

Blend's new eBook "Data Democratization: The Key to Unlocking AI and Data Science Potential" is now LIVE! This guide covers the need to democratize your data, how it can benefit your entire organization and a break down of how you can leverage your data on your GenAI journey. You can access it here: https://lnkd.in/gJJnyeN4 Alice Hodgson Megan Harkins Will Boyle #datademocratization #datascience

The value of democratizing data lies in making it accessible, useful, and understandable across all teams, thereby driving more effective, data-driven decision-making, and fostering innovation. This implementation requires both technological advancements and a comprehensive change management plan to facilitate cultural and organizational shifts. By breaking down traditional data silos and offering user-friendly tools, teams are set to enhance collaboration in an agile environment. Despite its manageable risks, #datademocratization offers competitive advantage in today's modern and demanding digital landscape 🛸

Great piece on how to best manage your data assets to drive more growth and transformation in your business. Take a look and reach out with questions.

Friends and colleagues: Very excited to share my new book, "𝗛𝗼𝘄 𝘁𝗼 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲 𝗜𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗨𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗧𝗶𝗺𝗲-𝗟𝗲𝗴𝗮𝗹-𝗖𝗼𝘀𝘁 𝗠𝗼𝗱𝗲𝗹." Think of compliance programs like rockets, and the installation of a compliance program like the launch of a rocket into space. They both must follow a series of mission-critical 𝘀𝘁𝗲𝗽𝘀, they both need massive amounts of 𝗽𝗼𝘄𝗲𝗿 and they each need a highly trained and specialized 𝗰𝗿𝗲𝘄. 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗱𝗮𝘁𝗮 provides the power in my data-driven model. And if data is the power, then we first need to identify its sources, and the authors in the HBR.com article, Your Data Strategy Needs to Include Everyone, provide a list of power sources: small data, big data, statistics, analytics and AI. They also propose thinking of data sources along a spectrum from basic to complex, with basic analyses and small amounts of data on the basic end and generative AI on the complex end. The 𝗽𝗼𝘄𝗲𝗿 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 for my model fall at the basic end of the above power source spectrum -- it uses 𝘀𝗺𝗮𝗹𝗹 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁𝘀 and 𝗯𝗮𝘀𝗶𝗰 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝘀, and the data generated throughout the book is cumulatively displayed in a 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 on 𝗣𝗮𝗴𝗲 𝟱𝟵. So, grab your space suit and get ready for the launch. Safe travels, Alex #analytics #compliance #dataprivacy #GDPR #performance #strategy #KPI #metrics #dashboards #geisbusiness

Book Title: Machine Learning for Business Analytics: Real-Time Data Analysis for Decision-Making In an era where real-time data rules the roost, businesses are on a constant lookout for strategies that can help them stay ahead of the curve. With an astonishing projection that 30% of all data generated by 2025 will be in real-time, the ability to make swift, informed decisions isn't just an advantage; it's a necessity. This groundbreaking book serves as your compass in the rapidly evolving landscape of business analytics. Whether you're a seasoned professional or a curious novice in the field of machine learning, this book has something for everyone. What will you discover? *The ins and outs of leveraging machine learning for business decision-making. *Techniques to analyze real-time data, enabling your business to adapt to market changes instantly. *Strategies for providing personalized experiences to your customers, ensuring they receive what they need precisely when they need it. 🔍 Why is this book a must-read? *It's crafted by leading experts in the field, ensuring you get insights that are both deep and practical. *With real-world examples and case studies, you'll see how theoretical concepts apply in actual business scenarios. *Learn to navigate the complexities of real-time data analysis with ease and confidence. Who should read this book? *Business executives looking to make more informed decisions. *Data scientists seeking to apply their skills in real-world business contexts. *Entrepreneurs wanting to utilize cutting-edge technology to stay competitive. *Students and academicians interested in the practical applications of machine learning. Join us on this journey to mastering the art of making swift, data-driven decisions that can propel your business to unprecedented heights. Grab your copy of "Machine Learning for Business Analytics: Real-Time Data Analysis for Decision-Making" today and transform the way you think about data, analytics, and decision-making in the business world. Book Link: https://lnkd.in/g4Tm86MR Raul Villamarin Rodriguez Woxsen University AI Research Centre - Woxsen University #MachineLearning #BusinessAnalytics #RealTimeData #DecisionMaking #BookLaunch #DataScience

Curious how analysts in #DataAnalytics tackle the challenges presented by colossal #Data volumes? The struggle with the scale, speed, and variety of modern data sources is real, and legacy solutions find it challenging to keep up. Our HEAVY.AI platform offers a robust and scalable solution, bypassing pre-processing to provide true results based on all available data, queried in milliseconds. In our eBook, explore use cases emphasizing opportunities and risks in location and time data, real-time network monitoring, quick trend identification, accurate point mapping, and an improved user experience. By harnessing HEAVY.AI's capabilities, organizations can maximize #DataDriven insights, fostering strategic goals and achieving valuable business outcomes. Learn more: https://bit.ly/3PQg7z5

In a report from ETR, 48% of survey respondents revealed they have barely begun to realize the value of unstructured data. Dive into the findings of the report to discover the pressing need for skills in applying #GenerativeAI to unstructured data and next steps: https://bit.ly/4dRvSB7

Nearly 90% of businesses have stated that they plan to increase their investments in data, analytics and AI yet many will fail to see a strong return on their investment.   Why will many fail? The reasons are countless including a lack of clear objectives, poor data quality, inadequate technology infrastructure, insufficient talent and experience, integration challenges, resistance to change, and inadequate training.   One of the most common issues is ineffective communication between the data scientists and business stakeholders. Business stakeholders often don’t know how to be good data science customers.    Winning with Data Science focuses on addressing this issue. It provides business leaders with critical frameworks and key questions to ensure that they are able to get the most out of their data science teams. It arms businesses with a large collection of questions that bridges the communication gaps and tightens the relationship between data science teams and their business stakeholders.   The book introduces questions that business leaders can use as a routine practice to maximize their return on data science investments. Want to learn more? Please check out winningwithdatascience.com

👇🏽 Five questions that shape all of my technical writing that requires some form of market analysis: 1. What's the historical context? 2. How did the market shift based on this historical context? 3. How do these market conditions shape opportunities and constraints? 4. How would the decisions made by an organization account for such opportunities and constraints? 5. Is their a pattern of organizations making similar decisions? 👀 Try the this thinking framework out the next time you are reviewing a new tool or architecture (e.g. Modern Data Stack)... you may be surprised that "best practice" is often not for the reasons you expect. #data #dataengineering #datascience #ai

Data-driven decision making has rightly become one of the best investments that a company can make. But what data? How should it be used? The issue is far from simple, but this article sheds some much-needed light on the subject. Read The Economy of #DataScience by #Experis expert, Charles Kincaid.