Dataiku’s Post

At DataDiligence, we live and breathe all things data! From data sources, ingestion, platforms, analytics, transformations, machine learning, data products, artificial intelligence, business intelligence, pilots, and deployment - we are trusted advisors for your entire data journey. Our team and delivery partners bring decades of collective experience across diverse use cases, technologies, and software. At DataDiligence, we're not just about delivering data models; we're obsessed with turning data outputs into tangible commercial outcomes. What sets us apart is our commitment to supporting our clients beyond the model deployment phase. We guide teams through change, adoption, and refinement processes to ensure measurable results. Our clientele spans various sectors and maturities, with a focus on small and mid-cap private equity funds and their portfolio companies. Whether it's shaping nascent data strategies or implementing sophisticated, AI-first technologies, we're here. Industries like SaaS, fintech, insurance, retail, healthcare, hospitality, and manufacturing feature prominently on our client list. When it comes to large data and AI delivery projects, we leverage our extensive network of domain-expert delivery partners. This ensures that our clients benefit from the right expertise, precisely when they need it, all at a competitive price point aligned with their ambitions. We're not confined by geography or language barriers, including code language! Our team is agile and ready to staff projects globally with expert resources. So, if you're seeking a partner to navigate the intricate world of data and AI, from inception to impact, look no further. #DataDiligence #DataAnalytics #AI #MachineLearning #DataScience #BusinessIntelligence #privateequity

A solid post from our friends at Databricks: "Helping all employees understand the value [of] a data and #AI-first strategy... #businessleaders can create internal champions who help lower resistance to #change and ensure the #modernization journey starts strong." #innovation #emergingtech https://lnkd.in/e2TmTG4T

As #GenAI tech has become readily available to businesses, the primary limiting factor to implementing GenAI has become the quality of your own internal data. Forrester analysts Brett Kahnke and Michele Goetz preview how operations teams need to change. #data #cio #cto #cdo

Success with genAI “hinges on high-quality, ‘business-ready’ data, which is guided by a robust data foundation, data governance, and standards.”

Absolutely! Data quality forms the bedrock of General Artificial Intelligence (Gen AI), serving as its foundation. High-quality data ensures accuracy, reliability, and relevance in training AI models, ultimately enhancing their performance, adaptability, and decision-making capabilities.

In my view, the question that is concerning is - what and why of use case selection at the organisation level vs starting with a Big Bang approach in favour of few tools that drags the organisation into managing cost and scope creeps Success of the #dataquality foundation using Gen AI and its applicability to handful use cases is the key. Structured Documentation aligned with newer roles within CDO organisation like knowledge creators in favour of strong actionable metadata layer is an important area that we might see unfolding. Who should be a Knowledge creator in a large organisation? Should this be traditional Product organisation or a revamp that brings product, business and technology together who form Knowledge COEs targeted towards evolutionary and foundational knowledge base for key use cases that Gen AI, or AI models can leverage? Reference: https://lnkd.in/geFTx98j

How to execute your operating model for Data and AI Teams and Roles

I have an Update to share and ask for input. Our AI and ML Data service line is getting ready, and we want to share some key best practices and values that we'll use when taking our clients on the AI journey. Here’s a hint of what you can expect: 📚 Education in AI and Data Science: Establishing a common vocabulary to strengthen communication and collaboration. Leveling up on understanding the costs, risks, pros and cons of different data solutions, various collaboration and operational frameworks. Building alignment and trust between Montrose Software and our clients. 🎯 Aligning with Business Goals: Understanding key business priorities and challenges where AI can make an impact. 🔍 Defining the Problem Space: Delving into operational and financial models to clearly outline the problem space and establish success metrics, tracking them daily. 📊 Data Intimacy: Getting up close and personal with data to identify high-value opportunities for quick wins. 🌐 Data Strategy: Initiating comprehensive data strategy programs if necessary. 🛠 Choosing the Right AI and ML Toolsets: Selecting the most efficient AI toolsets and ML algorithms within organisational constraints to make sure we deliver the right things at the right time with the right tools by the chery picked experts at hand. 🤝 Empowered Product Teams & MVPs: Implementing empowered product team practices and values, enabling data-driven decisions, and delivering MVPs with actionable feedback loops. 🔄 Iterating, Measuring and Improving: Continuously tracking feedback, iterating, and pivoting as needed to refine our approach and objectively measure the quality of the AI solutions. ⚙️ MLOps & FinOps: Establishing MLOps for robust data science operations and FinOps for financial control, ensuring alignment with success metrics. 💬 Let me know if I missed something fundamental or if you disagree. What would be your biggest challenges that you would be looking to help with? Which of the sections would you like to see a blog post for first? Please share your thoughts in the comments. Follow us for updates and message me for collaboration opportunities focused on tangible outcomes. cheers and cu later! #AI #Innovation #DataScience #BusinessStrategy #SuccessMetrics #MLOps #FinOps #AIProduct #DataStrategy #MachineLearning #BigData

Day 3: Key Elements of 𝐀𝐈𝐎𝐩𝐬 🚀 Welcome to Day 3 of our 𝐀𝐈𝐎𝐩𝐬 series! Today, we're diving into the 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 that make AIOps a game-changer for IT operations. 🌟 1. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 📊: AIOps thrives on massive amounts of data from various IT systems and tools. By collecting, aggregating, and analyzing this data, AIOps uncovers hidden patterns and provides valuable insights that drive smarter decision-making. 2. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐈 🤖: Machine learning algorithms and AI models are at the heart of AIOps. They continuously learn from historical and real-time data to identify anomalies, predict issues, and generate actionable insights. This intelligence helps preempt problems before they impact operations. 3. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 🔄: Automation is the backbone of AIOps, streamlining IT processes and reducing the need for manual intervention. Automated workflows enable quick resolution of routine tasks and incidents, significantly improving operational efficiency and allowing IT teams to focus on strategic initiatives. 4. 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 🔗: Effective AIOps implementation requires seamless collaboration among IT teams and smooth integration with existing IT systems and tools. This ensures a unified approach to managing IT environments, breaking down silos, and fostering a culture of continuous improvement. Stay tuned for tomorrow's post, where we'll explore the incredible benefits of AIOps and how it can transform your IT operations! 🚀 𝐔𝐩𝐜𝐨𝐦𝐢𝐧𝐠 - 𝐃𝐚𝐲 𝟒: 𝐓𝐡𝐞 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐀𝐈𝐎𝐩𝐬 #AIOps #ArtificialIntelligence #MachineLearning #ITOperations #TechInnovation #BigData #Automation #PredictiveAnalytics #ITManagement #FutureOfIT #DevOps #CloudComputing #AI #TechTrends #ITInfrastructure

As an engineer and exec at Fortune 100 companies, Maria J. Marti needed a tool that could detect data anomalies without a lot of manual input so she could trust the data that drove her teams. She founded ZeroError.ai and applied the power of #AI to create what she always wanted: an executive-centric, AI-driven app that can detect errors and fraud for timely decision-making. Learn more in our Startup Spotlight: