DataFramed

Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve...

Businesses are constantly racing to stay ahead by adopting the latest data tools and AI technologies. But with so many options and buzzwords, it’s easy to get lost in the excitement without knowing whether these tools truly serve your business. How can you ensure that your data stack is not only modern but sustainable and agile enough to adapt to changing needs? What does it take to build data pro...

AI is becoming a key tool in industries far beyond just tech. From automating tasks in the movie industry to revolutionizing drug development in life sciences, AI is transforming how we work. But with this growth comes important questions: How is AI really impacting jobs? Are we just increasing efficiency, or are we replacing human roles? And how can companies effectively store and leverage the va...

The sheer number of tools and technologies that can infiltrate your work processes can be overwhelming. Choosing the right ones to invest in is critical, but how do you know where to start? What steps should you take to build a solid, scalable data infrastructure that can handle the growth of your business? And with AI becoming a central focus for many organizations, how can you ensure that your d...

Building and managing AI products comes with its own set of unique challenges. Especially when they are under intense scrutiny like mobile and home assistants have dealt with in recent years. From dealing with the unpredictable nature of machine learning models to ensuring that your product is both ethical and user-friendly, the path to success isn’t always clear. But how do you navigate these com...

Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value? ...

AI has rapidly emerged as an incredibly transformative technology, and nowhere has its impact been felt more unexpectedly than in the creative arts. Just a decade ago, few would have predicted that AI would evolve from automating routine tasks to generating paintings, music, and even poetry. Yet today, the role of AI in the arts has entered mainstream conversations, even contributing to the debate...

With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also delivering results in a cost-effective way? What strategies can help reduce the financial burden of training and deploying mo...

In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technol...

As AI becomes more accessible, a growing question is: should machine learning experts always be the ones training models, or is there a better way to leverage other subject matter experts in the business who know the use-case best? What if getting started building AI apps required no coding skills? As businesses look to implement AI at scale, what part can no-code AI apps play in getting projects...

We’ve all met someone with a limiting belief, someone who describes their relationship with data as: “I’m not a data person” or “I can’t tell a data story.” Oftentimes, this mindset starts in childhood. Data storytelling is an incredible vehicle to challenge and reshape these beliefs early on. Imagine if kids could develop the skills to ask the right questions, interpret data, and tell powerful st...

Lot’s of AI use-cases can start with big ideas and exciting possibilities, but turning those ideas into real results is where the challenge lies. How do you take a powerful model and make it work effectively in a specific business context? What steps are necessary to fine-tune and optimize your AI tools to deliver both performance and cost efficiency? And as AI continues to evolve, how do you stay...

The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while main...

The pressure to innovate with AI is immense. There is seemingly a race against the clock for organizations to incorporate AI into their product offering, aside from continual digital transformation. As the speed of AI development accelerates, many organizations struggle to keep up, facing challenges from data readiness to changing traditional business processes. How can businesses ensure that thei...

One of the prerequisites for being able to do great data analyses is that the data is well structured and clean and high quality. For individual projects, this is often annoying to get right. On a corporate level, it’s often a huge blocker to productivity. And then there’s healthcare data. When you consider all the healthcare records across the USA, or any other country for that matter, there are...

Guardrails are not something we actively use in our day-to-day lives, they’re in place to keep us safe when we lack the control needed to keep us on course, and for that, they are essential. Navigating the complexities of decision-making in AI and data can be challenging, especially on a global scale when many are searching for any sort of competitive advantage. Every choice you make can have sign...

Doing sales better is perhaps the most direct route to making more revenue, so it should be a priority for every business. B2B sales is often very complex, with a mix of emails and video calls and prospects interacting with your website and social content. And you often have multiple people making decisions about a purchase. All this generates a massive data—or, more accurately, a mess of data—whi...

One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be...

Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language m...

By now, many of us are convinced that generative AI chatbots like ChatGPT are useful at work. However, many executives are rightfully worried about the risks from having business and customer conversations recorded by AI chatbot platforms. Some privacy and security-conscious organizations are going so far as to block these AI platforms completely. For organizations such as EY, a company that deriv...

There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the s...

One of the most annoying conversations about data that happens far too often is: “Can you do an analysis and answer this business problem for me?” “Sure, where’s the data?” “I don’t know. Probably in one of our databases.” At this point more time is spent hunting for data than actually analyzing it. Rather than grumbling about it, it would obviously be more productive to learn how to solve data di...

One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how...

Meta has been at the absolute edge of the open-source AI ecosystem, and with the recent release of Llama 3.1, they have officially created the largest open-source model to date. So, what's the secret behind the performance gains of Llama 3.1? What will the future of open-source AI look like? Thomas Scialom is a Senior Staff Research Scientist (LLMs) at Meta AI, and is one of the co-creators of the...

Excel often gets unfair criticism from data practitioners, many of us will remember a time when Excel was looked down upon—why would anyone use Excel when we have powerful tools like Python, R, SQL, or BI tools? However,  like it or not, Excel is here to stay, and there’s a meme, bordering on reality, that Excel is carrying a large chunk of the world’s GDP. But when it really comes down to it...