DAMA UK

Time to pause As the festive season is upon us, we hope it can be a time to pause, reflect, and recharge. It presents a well-deserved opportunity for data workers to step away from their screens and enjoy a much-needed break. After a year of managing complex datasets, meeting tight deadlines, and driving meaningful insights, taking time off if you’re able to, is not just a luxury—it carries real benefits for you and your organisation.  Rest and relaxation over the holidays allows us data professionals to reset both mentally and physically. This downtime helps to rekindle creativity, boost energy, and restore focus, ensuring we return to work with a fresh perspective and renewed motivation. It’s also a chance to connect with loved ones, embrace the joy of the season, and indulge in hobbies or activities that bring personal fulfilment.  The power of rest cannot be underestimated. By prioritising well-being during the festive season, data workers are better equipped to face the challenges of the new year, ready to innovate, problem-solve, and continue their vital contributions to the data-driven world. So, from all here at DAMA UK, here’s to embracing the break, savouring the moments of peace, and returning in January with a mindset poised for success✨ Seasons Greetings All #2024 #data #festiveseason #damauk

“As we approach 2025, organisations are realising that traditional approaches to data governance are no longer sufficient.” Information Security Buzz discusses emerging challenges in data governance as organisations approach 2025, driven by generative AI adoption, stricter regulatory requirements and evolving cybersecurity threats. We believe that one of the most important ways to stay safe while using AI in data governance is by having a strong centralised data management system backed by clear governance policies. 🛡️ If your data is scattered across different platforms, it’s hard to control what AI models can access, which opens the door to data sovereignty and confidentiality risks. For example, if you centralise your data in a secure document management system, you can control what gets used for AI training while keeping sensitive information protected. At the same time, adopting a Zero Trust framework can help by limiting data access only to verified users, reducing the risk of breaches. ☝️ It’s also crucial to monitor how AI interacts with your data, especially with ever-changing privacy laws like GDPR and CCPA. Regular audits can help ensure compliance. And, honestly, educating your team is a big piece of the puzzle too. When people understand how AI uses data, they’re more likely to follow best practices and flag potential risks early. With all that in mind, are you feeling prepared? How do you see AI fitting into your data governance strategy in 2025? https://lnkd.in/gNH8yan2 #aigovernance #data #compliance #datagovernance

Before we know it, the future of data will be upon us. Our 3rd and final festive visitor has a few words to say on what that might look like. Ready to meet The Ghost of Data Yet to Come? 👻 Watch here 👇 #data #dataprofessionals #datagovernance

📢 Did you miss it? Daragh O Brien is founder and managing director of Castlebridge, one of Ireland’s leading consultancies working in data strategy, data governance, and data protection. He’s also a published author. With his no nonsense approach, Daragh guided us through the big questions around "AI Ethics and the Multiverse of Madness". Whether we embrace AI with enthusiasm or not, it’s here to stay and we need to ensure ethical and regulatory standards are upheld. Don’t miss out on this important opportunity to learn about what Daragh calls ‘the bullshit bingo’ of AI ethics. Catch up here 👉https://lnkd.in/dQaiCRz #dataai #aiethics #datastandards

Balancing centralised and decentralised data teams is a tricky puzzle! Organisations can swing from one model to the other due to neither option fully solving their challenges. 🤷 Centralisation works well for setting consistent standards, sharing expertise and ensuring better resource allocation. But, as Raphaël Hoogvliets states, it can slow things down, especially when business units are waiting for central teams to act. On the flip side, decentralisation empowers teams to act fast and align closely with business needs. But without a central framework, you risk data silos, inconsistent practices and duplicated efforts. Often, what is found to work best is a hybrid model. You keep a central data governance team responsible for setting standards, managing core infrastructure and providing oversight. Meanwhile, embedded data teams within business units can adapt those standards to their unique needs while staying aligned with the central strategy. It’s definitely not one-size-fits-all, though! So if you’re currently trying to strike this balance, don’t forget that industry, company culture and even leadership buy-in can tilt the balance one way or the other. Great post, Raphaël. 👏 #datastrategy #data #damauk

The Modern Data Stack (MDS) refers to a set of unified cloud-based tools and technologies designed to efficiently manage, process, and analyse data. It is modular, scalable, and built to handle the complexities of today’s data-driven environments, delivering a seamless cohesive solution to previously fragmented systems. Surging data volumes present organisations with increasing challenges around effectively managing and leveraging information. The Modern Data Stack addresses these demands by providing a  structured framework that enables businesses to uncover actionable insights and maximise the value of their data. Let’s look at the MDS key characteristics: 🔶 Cloud-Native: Primarily hosted on cloud platforms for scalability and cost efficiency 🔶 Modular: Comprising distinct layers (e.g., ingestion, storage, transformation, and analysis) that can be integrated as needed 🔶 Real-Time: Supports real-time data processing and analytics 🔶 User-Friendly: Designed for accessibility by both technical and non-technical users 🔶 Interoperable: Tools within the stack easily integrate and communicate with each other The MDS is widely used for ingesting and consolidating data from multiple sources, powering business intelligence (BI) tools for dashboards and insights, providing clean, structured data for training models and enabling organisations to act on insights quickly. Benefits: 🔷Easily adapts to growing data volumes 🔷Accelerates data processing and analysis 🔷Pay-as-you-go cloud pricing eliminates heavy infrastructure costs 🔷Customisable to fit specific business needs 🔷Enables teams across the organisation to leverage data effectively In short, the Modern Data Stack introduces efficiencies, streamlines data workflows and empowers businesses to innovate and remain competitive. #dataanalysis #structureddata #damauk

“Applying gen AI to solve useful problems at scale requires a paradigm shift in how we think about data management and integration.”   diginomica sheds light on the key challenge of how to efficiently extract value from unstructured data that lacks the neat structure of databases or spreadsheets, from documents and images to videos and IoT data. Traditional ETL (Extract, Transform, Load) workflows aren’t enough. Instead, we need flexible, iterative pipelines that allow us to index and preprocess data dynamically for AI. Techniques like building a global file index and leveraging multi-modal AI encodings can support organisations to find the flexibility they need.  One aspect that is particularly interesting is the blending of metadata and context. Metadata has traditionally been about tagging files with basic information, but when it comes to unstructured data, metadata needs to be multi-dimensional, capturing details like the entities in a document or the context of an image. This richer, dynamic metadata is crucial for feeding gen AI models with relevant, trustworthy data. Blending metadata and context in unstructured data pipelines is a promising but complex process. There are some key challenges: 👉 Contextual boundaries  👉 Data variety 👉 Evolving standards  👉 Data quality  👉 Cross-modal integration  👉 Scalability of index  👉 And, of course, with all things now you've got to consider security risks Addressing these challenges requires not only technical solutions but also a forward looking strategy that anticipates the evolution of AI and data management. For all of us in data, this is an exciting time. Generative AI is driving innovation, but it’s also pushing us to rethink foundational concepts like metadata, workflows and governance. Read the full article: https://lnkd.in/gJ26xzcg #datagovernance #generativeai #dataintegration

As the festive season approaches, we find ourselves with a 2nd unexpected visitor - this time it’s The Ghost of Data Present 👻 To learn what they have to share with us take a look below! 👇 And, if you missed The Ghost of Data Past, you can catch up using the link in the comments... #data #datagovernance #damauk

📢 Public Webinar this Friday. How do we ensure ethics align with the responsible adoption of AI? AI ethics has rapidly emerged as a hot topic, popping up in discussions like an unpredictable game of whack-a-mole. But what does it truly mean? What should we say to those innovators eager to experiment with shiny cutting-edge tools? Or to the legal teams filing lawsuits over copyright infringement or defamation? These are pretty important questions we’re all likely to face sooner or later. So why not get ahead of the curve and start addressing them now? Daragh O Brien is founder and managing director of Castlebridge, one of Ireland’s leading consultancies working in data strategy, data governance, and data protection. He is a co-author of Ethical Data and Information Management: Concepts, Tools, and Methods, published by Kogan Page, and Data Ethics (2nd Edition), published by Kogan Page in June 2023 so is very well placed to host this webinar. Join Daragh for a whistlestop tour through the bullshit bingo of AI Ethics in "AI Ethics and the Multiverse of Madness". Sign up here 👉 https://lnkd.in/eXTGJriN #data #aiethics #datatools

🤫 Here is a cheat sheet for anyone grappling with the often tricky decision of choosing the right visualisation for data. A clear, concise guide like this is so valuable, especially for ensuring that the story your data tells resonates with your audience. One tip we’d add is to always consider the level of granularity your audience needs. ☝️ For example, executives typically prefer high-level overviews like bar charts or pie charts that focus on key metrics and trends, while analysts or technical teams often benefit more from detailed visuals like scatter plots, histograms or line charts that reveal patterns and correlations. Additionally, don’t underestimate the power of context in your visuals. As simple as it may sound, a well crafted title, annotations or even a subtle use of colour can emphasise key takeaways and guide your audience’s attention towards what you intend them to see. 💡 Remember, a great chart doesn’t just display data - it creates understanding. This is definitely one to save for later if you find yourself second guessing visualisation. Fantastic resource, thank you for sharing this Yassine Mahboub! 🙌 #datavisualisation #datagovernance #data #dataquality