This edition shares some perspectives on data-driven concept in an era of artificial intelligence (AI). #Leadership #AiLeadership #DigitalTransformation #Transformation #Innovation #Strategy #DataDriven #BigData #Data #DataQuality #ArtificialIntelligence #GenAI #GenerativeAI #Governance #ValueRealization #WorldOfAbundance
Sweden Business Artificial Intelligence Leadership’s Post
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Does your organization face significant challenges keeping pace with rapid technological advancements, especially with #AI evolving so quickly? You're not alone. While many organizations have built substantial product and data teams, their ability to adapt and innovate at business speed often falls short. Read more on this and other areas where #IT #consulting leaders like West Monroe's Cory Chaplin expect to focus in 2025, after the jump.
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Execs - are you trying to solve data issues when they pop up on AI projects? Don't play Whac-A-Mole, says EY's Traci Gusher in Fast Company. The problem with a piecemeal approach to data quality, accessibility and lineage is that it creates an environment where data is everybody’s job—and nobody’s. Without a holistic data strategy, you risk duplicated investments and short-term solutions, undermining your ability to create sustained value and ROI. One of the key issues is a lack of #DataInfrastructure. The recent EY AI Pulse Survey shows just 36% of senior leaders are investing in the quality, accessibility and governance of their data at scale, meaning their AI is missing crucial information that would enable it to produce better, more accurate results. Read Traci's article for more insights on data infrastructure, governance and talent: https://lnkd.in/eW8mWpKr #data #datagovernance #datatalent
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The importance of Data Quality in ALL your data projects is crucial. Refer to the Barr Moses comments about the Gartner Top 5 Investment Trends. https://lnkd.in/gNQ7xDRN
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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Senior Director, Product Management - DataOps | Global Leadership | BI | Analytics | Cognitive | Data Governance | Data-Driven Insights | AWS | Data Literacy | A/B Testing | Agile | SAFe | MLAI|
Recently, I was asked to contribute to an article on achieving AI-grade data at scale. Here are the three key steps to make your data AI-ready: Automate Data Preparation: Cut manual processes with automation to improve data access, discovery, and quality checks, saving time and reducing errors. Establish Insight and Control: Catalog and classify data to ensure visibility, proper usage, and regulatory compliance throughout its lifecycle. Efficient Data Delivery: Automate delivery to ensure consistent, high-quality data for AI initiatives while reducing workload on teams. Solid data foundations are crucial for GenAI success, transforming pilot projects into game-changing outcomes. Read more here: https://lnkd.in/eDzV33yJ #AI #GenAI #DataManagement #Innovation #Automation #Governance
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CIO | CTO | CDO | Head of IT | IT Manager | Digital Transformation | Digital & Innovation | Customer Experience | CRM & Agile | AI & ML
Building a data operating model for success in the GenAI era. Scaling an #AI strategy goes beyond technology; it demands a robust data strategy and operating model to orchestrate processes, people, and the tech stack. It also involves prioritizing business data domains, ensuring data governance and compliance are aligned with business objectives. More in this engaging article by #McKinsey.
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Data consultancy growth accelerator ✦ myDataBrand Founder ✦ Creator: Data Quality and Data Governance Leadership Forum (21K+)
The problem I have with these Gartner studies is that they always show DQ/DG near the top of investment. And yet do you know how many calls I get from orgs desperate to launch data quality projects and asking for recommendations on hires, tools and firms? Not many. Far less than a decade ago. And that’s despite having the largest and most active data quality and governance leadership forum, and a chunky network of DQ/DG consultancies. There is demand, don’t get me wrong, but it’s the same steady flow it’s been for years. If these stats are to be believed then there would be a massive surge in projects and scarce supply every year, because DQ and DG are always up there. And if it’s all ‘going to kick off’ in 24 months time then surely we’d be seeing a strong uptick right now. Nope, not seeing it. Or perhaps I’ve got it wrong and this is FINALLY the big wave of demand we’ve waited so long for. Hmm 🤔 But do you know what I have seen in more recent years? Cloud based data platform builds in hyper demand, far outstripping DQ/DG. What are you all seeing? #dataquality #datagovernance
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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This is a great reminder that AI-first thinking often deprioritizes data governance and data quality, which are essential for AI success. We regularly have discussions with industry participants, clients, and prospects about this topic and our response is very much in line with Barr’s post. Just last night, I attended an industry networking event and AI was the hot topic. Discussion of opportunities that lie in the deployment of AI were grounded by the lack of data that would support effective deployment of useful AI tools. In the past year, two of our Managing Directors embraced these ideas: In one post, Phillip Dauer asks “Are you ready for AI?” and outlines 4 foundational building blocks that make data high-quality, transparent and auditable so that AI output can be trusted by you, your management, and most importantly, your clients. In a four-part blog series, Arun Krishnamurthy breaks down data governance for asset managers. In his series, Arun makes the case for data governance as a foundation for AI, whether it supports risk, portfolio management, regulatory reporting, or any other asset management process. Data governance has long been a high priority for organizations, but remains elusive for many. The bottom line is that whether AI tops your 2025 priority list or you simply know that your data isn’t cutting it for everyday use, data governance is essential to improve data quality, reliability, and accessibility throughout the investment lifecycle. I’d be curious to know if these investment priorities line up with yours. Is AI a top initiative? Do you feel it’s being prioritized at the expense of foundational challenges such as data quality and data governance?
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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Customer Success at Microsoft | Cybersecurity & AI Evangelist | Empowering US Government Customers through their Digital Transformation and Security Modernisation journey
Think of AI and governance as a driver and a GPS. AI is the GPS, providing directions and optimizing routes, while governance is the driver, making sure the journey is safe and follows the rules of the road. Together, they ensure a smooth and efficient trip. 🚗📍 #ai #governance
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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GenAI platforms blithely suggest that customizing or fine-tuning a model is as easy as "upload your data". Unfortunately the most valuable proprietary enterprise data is messy 💩. For example, a leading telco struggling with co-pilot performance found that the call transcripts used to tune the model had low quality Q&A pairs and sparse coverage for rare (P0) customer support requests. Problems achieving production quality AI OR effectively evaluate performance for real-world use cases are usually data problems. Snorkel AI complements incredible platforms like Monte Carlo by equipping AI developers to collaborate with SMEs to curate AI quality data (vs tossing spreadsheets over the fence and asking for hours of manual effort or trying bypass HITL completely). 🚀 🚀 🚀
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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Top 10 Thought Leader for Agentic AI | Investor, Industry Analyst, Strategic Marketing Leader, Passionate Promoter, Relentless Imagineer, Tireless Encourager of Success, Consummate Analytics Professional
This is an interesting conundrum. Major investments will be made in AI ready data in 2025, and probably also in 2026. The conundrum is the opportunity that this creates for AI enabled data engineering. The big winners will be unified data engineering platforms that AI enable everything they do. Keep your eyes out for the most powerful AI Data Engineering Agents in 2025.
According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.
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