Mark Strefford’s Post

You can't have a killer AI strategy without a solid #data strategy. Businesses in the US alone lose $3.1 trillion annually due to poor data quality. Operational inefficiencies, missed opportunities, and a lack of understanding of customers are all caused by bad data. 🤔 Imagine trying to use #GenerativeAI to create a segment of offline customers, but your data labels for "Offline_Purchases" are inconsistent and numerous. That mismatch means your data foundation is flawed, and could lead to an inaccurate and less valuable segment creation. So, how do you get your data AI-ready? Here are the top three areas where you should focus your attention to build a strong data foundation that will ensure your #AI engine generates the best possible results 👉 https://bit.ly/4bAEpGb 

You can't have a killer AI strategy without a solid #data strategy. Businesses in the US alone lose $3.1 trillion annually due to poor data quality. Operational inefficiencies, missed opportunities, and a lack of understanding of customers are all caused by bad data. 🤔 Imagine trying to use #GenerativeAI to create a segment of offline customers, but your data labels for "Offline_Purchases" are inconsistent and numerous. That mismatch means your data foundation is flawed, and could lead to an inaccurate and less valuable segment creation. So, how do you get your data AI-ready? Here are the top three areas where you should focus your attention to build a strong data foundation that will ensure your #AI engine generates the best possible results 👉 https://bit.ly/4bAEpGb

AI is a 'multiplier'. If you feed it data with errors or biases, those errors and biases are replicated and exacerbated. In our recent breakfast roundtable with Informatica, data leaders across financial services shared some fascinating experiences and insights around their data challenges and how they are planning to safely and effectively introduce AI into their businesses. As one of our guests put it, "It all comes down to the foundations and making sure you've got the right Data Governance in place. You have to structure data in the right way and manage it properly." Follow the link here to read more: https://bit.ly/3Rrxat8 #DataGovernance #AI #MachineLearning #FinancialServices

Artificial intelligence without the right data strategy is just...artificial! The AI genie is out of the bottle and will transform businesses in ways we cannot imagine (yet). But we can affirm data to be the primary determinant of success for enterprises in the coming years as artificial intelligence transforms business workflows. Data and AI are interrelated, and just like AI is only as good as the underlying data on which it is based, data is only as good as the models that created it. Having a robust data strategy is crucial to an AI future. More insights here: https://hubs.la/Q02j_3n-0 #artificialintelligence #data&AI #datastratergy

Get connected with the #Celsior team to discuss your data strategy. Access to clean, reliable and trusted data is crucial to your data strategy and leveraging AI effectively in your organization.

AI - and especially generative AI - is such a hot topic right now and we are all looking with our customers at new possibilities we have in taking business analytics somewhere rather magical. Yet we have to be careful that what we are doing is right for an enterprise setting. What do I mean by this? Well, I immediately see potential issues around: - Accuracy and reliability (we must always think "business critical") - Legal implications (are there things we must avoid?) - Data security (are there potential risks?) These are again things that we need to talk about - and indeed are talking about - when we make the case for enterprise AI and how to do it right. Join me in the below video to continue our conversation! #sap    #analytics    #businessintelligence #ai

Crafting an AI-driven data strategy is critical for businesses aiming to lead in innovation. Empower your #AI implementation by aligning your data strategy with business objectives: 1️⃣ Understand industry intricacies to gain a competitive edge with AI 2️⃣ Tailor AI objectives to address operational inefficiencies and market trends 3️⃣ Foster shared ownership for successful AI implementation 4️⃣ Future-proof investments with scalable AI systems 5️⃣ Proactively manage data security and system failures 6️⃣ Fine-tune algorithms and optimize system efficiency in real time Check out the web experience of our new whitepaper 👇🏼 https://lnkd.in/gjKvQ5_W #ML #datastrategy #datamanagement #GenAI

NEW: Are you using AI to streamline your data normalization process? Learn about how Tracer does it to reduce a 25-hour task to under 60 minutes. — Normalization in data analytics is the process of organizing data so it’s consistent (same order and format) across data sets. This makes the data more accurate and actionable. — AI can automate manual normalization tasks at tremendous scale. Consider data that spans multiple languages and formats. Using LLMs, this manual process can be automated. — The result? Data teams can focus on more strategic work. Read the case study: https://lnkd.in/ewKjDQZ5 #AI #data #analytics

Artificial Intelligence (AI) and digital reporting have become essential in finance for their ability to efficiently process large amounts of data, provide in-depth analysis and insights, and automate routine tasks. Read more: https://bit.ly/3VXY6DF #AI #analysis #data #reportingchain #business #businessreport #SemansysNext #validation #sustainable #conversion #processes #formats

AI is moving fast, but can you really dive in without a solid data strategy? 🤔 Balancing AI and data practices is tricky, but it's possible to tackle both together for the best results. Curious how? Read this article to know more! #AI #DataStrategy #BusinessGrowth"