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"Feeding a GenAI system with inaccurate data is like feeding an athlete with junk food." Find out which expert said this on Solutions Review Expert Roundtable: Bad Data on GenAI, hosted by Susan Walsh - The Classification Guru, and joined by Darshana Sivakumar from Databricks, and Millie Beetham from ZoomInfo. Here are some highlights: 💡 Accurate data is essential; inaccurate data leads to bad business decisions. 💡 Data silos are more common than you think. 💡 Many companies do not want to invest to clean their problems, meaning the situation eventually gets so bad that it then costs even more to fix! 💡 Look to prevent rather than cure. 💡 A lot of people inputting data are not data professionals. 💡 Its important to have one unified data governance. 💡 It's all about context, it depends on what you are talking about. 💡 Start with the end in mind. Follow a clear path, and have a back up plan. Get the full run down here ➡ https://lnkd.in/eRW5YrKT

🌟 Tackling the Data Paradox with the #NationalBestseller Book - Mastering the Data Paradox 🌟 While going through #MasteringTheDataParadox by Nitin Seth, I've uncovered a myriad of challenges organizations face in navigating the complexities of data utilization. It's clear that while organizations invest heavily in addressing the 'physical' issues surrounding data infrastructure, they often overlook the underlying 'logical' issues, perpetuating a cycle of inefficiency. Delving deeper into the symptoms, I've identified nine root causes that lie at the heart of the Data Paradox, each with its unique set of challenges: Clarity of Objectives and Data Needs: Undefined business problems result in irrelevant data and lack of insights. Data Quality Concerns: Data volume makes quality monitoring difficult, leading to uncertainty and reluctance in its use. Silos (Data/System/Organizational): Organizational silos hinder data accessibility and collaboration, impeding effective data utilization. Need for Iterations: Dynamic business problems require iterative approaches, but legacy systems often lack agility for real-time data demands. Legacy Infrastructure Constraints: Outdated infrastructure poses limitations on scalability and flexibility, hindering organizations' ability to leverage Big Data for insights. Lack of Alignment/Collaboration Across Teams: Siloed approaches to data strategy result in disjointed efforts and implementation challenges, exacerbating the Data Paradox. Absence of Democratized Data Access: Limited data sharing and security concerns impede seamless access to data across teams, hindering decision-making processes. Lack of Data Culture: Despite abundant data, organizations struggle to instill a data-driven culture, leading to reliance on gut-based decision-making rather than data-driven insights. Lack of High-Quality Data Talent: The scarcity of skilled data professionals with end-to-end problem-solving capabilities hampers effective utilization of data and collaboration across business, IT, and operations teams.   In conclusion, addressing the Data Paradox requires a holistic approach that prioritizes solving 'logical' issues alongside 'physical' infrastructure enhancements. By fostering clarity of objectives, breaking down silos, and cultivating a data-driven culture, organizations can transcend the Data Paradox and thrive in the Data-first world. Stay tuned for more insight about the book 📚 Order your copy- https://amzn.to/3QtkSzS #MasteringtheDataParadox #AI  #DigitalTransformation #BigData #ProblemSolving #LinkedInReview 🚀📊 Nitin Seth Incedo Inc.

Attention, Data Professionals: Dive into Data Delight at ElevateIT: Phoenix Technology Summit ! Einstein (supposedly) said it best: "Not everything that counts can be counted." But in today's data deluge, it's easy to drown in numbers and miss the real insights hiding beneath the surface. Are you tired of decisions that feel more like disasters than delights? Me too! That's why I'm thrilled to announce I'll be unlocking the secrets of data-driven decision-making at the ElevateIT Phoenix Technology Summit, where I'll be rocking the VIP Theater on March 13th! Join me as we dive into: * The 4 V's of Big Data: Volume, Variety, Velocity, and... what's the missing V? (You'll need to come to find out!) * Neutralizing cognitive biases that trip us up on the way to being data-driven. Bye-bye, confirmation bias! * My 5-step framework for turning data into dazzling decisions: LOOK, LINK, LISTEN, LEVERAGE, and LEARN: . Prepare to be challenged! This keynote isn't just about numbers; it's about transforming your relationship with data. Imagine qualitative and quantitative insights dancing hand-in-hand, revealing a path to innovation so clear, it feels more like magic than math! Ready to turn data denial into data delight? Grab your tickets to ElevateIT Phoenix at https://lnkd.in/gM8mcA8R and let's make 2024 the year we harness the power of data, together! Don't miss your chance to experience the data magic! #WhatPerezSays #UnleashingFirepower #ElevateITPhoenix #DataDrivenDecisions #KeynotesThatDelight #LetsGetMagical

You Need To Think Data Centric To Be A Successful Business: Stop Being Data Driven, Application Centric. Many businesses find themselves drowning in data yet left thirsting for genuine insights. Despite being tech-savvy and frequently working with data, business leaders still struggle to fully harness this wealth of information. Indeed, their data is stuck in data silos, duplicated, jammed into different formats and of such terrible quality that it’s virtually unusable, and definitely untrustworthy. A change of approach is required. Businesses need to pivot towards a data centric approach as opposed to merely being data-driven or focused on applications. In this article, I’ll explain the concept of a “data-centric” mindset – clarifying what it encompasses and what it doesn’t, while also contrasting it with such catchphrases as “data-driven” or “application-centric.” #datacentric #datadriven #applicationcentric Mentions in this article: TDAN.com - The Data Administration Newsletter, neptune.ai https://lnkd.in/e-A7FdNg

By organizing their team into six general functions, data leaders can build an organization that thrives in the era of artificial intelligence. Get the details in this TechRepublic exclusive by Forrester’s Kim Herrington.

Data silos are more than just a technical nuisance; they represent a significant barrier to innovation and progress within organizations. These isolated pockets of data not only slow collaboration but also restrict our ability to make informed decisions. They result in missed opportunities. 1) The lack of shared insights and information leads to a disjointed understanding of objectives and strategies. 2) By limiting access and visibility, these silos prevent us from seeing the full picture, making it challenging to identify trends, patterns, and opportunities that could propel our business forward. 3) They represent a real cost to your business, not just in terms of resources but also in the potential revenue and innovation lost due to delayed or uninformed decision-making. How can we effectively dismantle these silos to create a more integrated, transparent, and collaborative data environment? Some progress was made during the Data Science movement of the last decade --- essentially breaking down the barriers so that you no longer have to know the right data librarian and what treat they prefer in order to get access to the data you need (at least not all the time). But, our progress has just scratched the surface because of at least three reasons: 1) The commonly promoted systems (data lakes) didn't solve the essential connection between computational power and useful data -- thankfully AI/ML and GPUs are still disrupting some of the most extreme nonsense promoted 10 years ago. This is rapidly improving. 2) Not every problem needs scale-out. Scale-up (just get a few faster systems and better compilers) are still the best way to solve many problems. This will always be a trade-off. 3) Not enough awareness was paid to the essential reality that different users *need* and *care about* different data. (or different emphasis on the *same* data). You will have "apparently" redundant data driven by different use-cases. It's OK. It is very hard to create "cross-use-case" data-lakes for every need without over-paying significantly. This is an essential complexity tied to purpose-driven maintenance of data. What else do you see as some of the key questions to be answered, or the key issues to address? What frameworks are emerging that are helping to break down the incentives and human energies that can lead to the silos.

In alternative data, what you see isn’t all there is. You need to go beneath the surface. 👇 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 is what you see. But building 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀? It’s what keeps data flowing efficiently from different sources. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 is the goal. But without 𝗱𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, insights are unreliable. Governance keeps data accurate and compliant. That’s what makes insights reliable. 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 might seem simple. Yet, 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 makes it feasible. It’s about using models to uncover meaningful cost efficiencies. 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 is essential. But 𝗰𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 makes it work. Data alone doesn’t create value. When tech teams and finance sync up, insights become actionable. Understand the engine that lets you turn data into action. Go beyond surface insights. Make alternative data your advantage. Discover what Nimble can do for you → https://lnkd.in/enkKYzPh #DataScience #AlternativeData #FinancialData #DataEngineering #PredictiveAnalytics

There is a misconception that data governance is becoming passé. This is untrue In fact, instead of being obsolete, it is actually becoming more important than ever. As AI continues to spread through various sectors, data governance becomes even more vital. *** 500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organizational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.