Brad Schneider’s Post

The AI wave and demand for training and inference #data is accelerating the growth in data providers at an unprecedented pace

Given everything that's happening from the economy to politics around the world, transparency and accessibility are going to be critical in 2024. And open data is is well-suited to bring solutions to a world that deeply needs it right now. One reason to be more hopeful!

2024… the year of open data? With the New Year firmly here, we’re thinking long and hard about what’s in store for the open data community and where we’ll be at the end of 2024. What do we think the 5 big trends this year will be? 1️⃣ Transparency and ethics will become the benchmarks for buying any data-related products and services. 2️⃣ More privacy-conscious than ever before, businesses will turn towards open datasets for their marketing and sales needs. 3️⃣ Further advancements in AI will make open data more robust, complete and reliable. 4️⃣ More data giants in the world of science, business and art will allow access to their invaluable data, further enriching the open data ecosystem. 5️⃣ Continuously tight budgets will lead businesses to look for all-in-one solutions for their data needs. And while we’re strapped in and ready to look closely at what actually happens, we’re continuing to create one tool to extract, analyse and visualise data, grounded in open data and enhanced by AI. The world of open data at your fingertips - now that’s our New Year’s resolution! #opendata #datademocratization #newyearsresolutions

#Datasourcing #trainingdata is more timely and relevant than ever, as we enter the era of compressed, enterprise-specific, country/language/culture focused LLM development...

Have you heard about the most common #DataSourcing methods? #Trainingdata can be sourced via synthetic data generation, public datasets, data marketplaces, and crowd-sourced platforms. Read further on our blog: https://hubs.ly/Q02h1n_m0

Seam is on a mission to make customer information accessible to everyone, regardless of their technical abilities. With their AI interface, business users can now answer any questions they have using data. This is a game-changer in an industry where technical skills can often be a major barrier to entry. Check out the article to learn more! Link: https://lnkd.in/eZn6xini

You might be ready for #AI, but is your #data? 🤔 Our CEO Afif Khoury shares on Forbes why data reigns so supreme in the #ageofAI and the steps companies need to take to become AI-forward.

Data Quality is king. Find out how to ensure reliable insights for your business & learn why the Near platform stands out with its three-pillared approach: 🌍 Global, Quality Data 🔒Privacy and Transparency 🤖Data Science and #AI

Oh boy... let's talk. You want A.I., I get it! Every software vendor now tells you they have A.I., I get why they are telling you that. But... If A.I. runs on data (which it does), and if A.I. will only be as smart as the data you feed it (which it will), and if the solution you are looking at is a point solution and doesn't have access to much data (because it's a point solution, which by definition means it has limited functionality and access), then get ready to babysit a toddler 😅 In my role I get to speak to and advise some of the largest brands in the world on A.I. The first thing I ask them... Where is all your data? - No data = no A.I. - Fragmented or partial data = fragmented and partial intelligence. Sound like fun? If someone is telling you they have A.I. in their solution, ask them what data they have access to and if they think it's in your best interest to use A.I. based on such a limited set. Their answer will tell you all you need to know about whether you should trust them or not. If you want to be A.I.-forward... Step 1... get the data. As much as you can. This is now your no 1 job... to think through how to start to consolidate that data in one place (architected for easy consumption). We can help. More steps in this article... Thanks Forbes #bewareofpointsolutionAI

What do I see the BEST data teams do that others should replicate? I thought about this for a bit last week, here’s my top 3: 1. Think like an executive It’s easy to get caught up in the latest tools (especially with recent AI advancements), but data teams need to focus on their end users. → What do they care about? → What are the top 1-2 things they need to know this week? → How can you make it as easy as possible to understand? → What questions will they ask next that you can proactively answer? Complete all data requests with those questions in mind. 2. Collect data on your data You basically want to create a feedback loop on what’s working (and what’s not). → Monitor usage of data assets → Solicit feedback from end-users → Understand the business and its core loops You want to make yourself as big of an asset as you possibly can. 3. Use the right tools to become efficient I know I said it’s easy to get caught up in tool developments — but the truth is, if you use the RIGHT tools, you can become a lot more efficient. Only add something to your data stack if you know for a fact it’ll make you more efficient. There’s so many shiny new objects in data-land right now. Stay focused only on tools that deliver clear value! (And this is coming from a tool vendor! I think that LLMs is on that list but I’m very biased 😇) Anyway, that’s all from me — in your experience, what else do the best data teams do on a regular basis?