To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.

More people are looking for more efficient BI products to integrate into their applications in 2022, and want to know exactly what embedded BI solutions mean for their users. This article will define embedded BI, explain its growth in popularity among software users, and why we suggest Yellowfin as your embedded BI solution for better analytics.

In 2010, Eric Schmidt, then CEO of Google, made the startling claim that every two days we humans generate as much information as we did from the dawn of civilization to today, or about five exabytes of data. At the time, we had TB disk drives and could only imagine an exabyte, which is one million terabytes. The next increments from TB is the peta byte and then the zettabyte, which is 1,000 exabytes. By the end of 2010, the world had crossed the zettabyte threshold.

In a previous article, we talked about the lost art of questioning and its importance when working with data and information to find actionable insights. In this article, we will expand on this topic and explain how questioning differs depending on what stage in the process you are from transforming data and information into insights.

At Talend, we tend to describe poorly organized, unhealthy data as “digital landfills.” But we don’t often talk about actual landfills. That’s right, the ones filled with trash. As anyone watching real estate prices will know, land is a finite resource. It’s crazy to think that we’re still dedicating land to storing our garbage, where it will sit releasing pollutants and greenhouse gases for decades to come.

At Mercado Libre, we are obsessed with unlocking the power and potential of data. One of our key cultural principles is to have a Beta Mindset. This means that we operate in a “state of beta”, constantly asking new questions of our data, experimenting with technologies and iterating our business operations in service of creating the best experiences for our customers.

Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders.

At the DataOps Unleashed 2022 virtual conference, AWS Principal Solutions Architect Angelo Carvalho presented How AWS & Unravel help customers modernize their Big Data workloads with Amazon EMR. The full session recording is available on demand, but here are some of the highlights.

As an ISV company selling a SaaS application, you have built analytics into your software because you know customers highly value insights into the data that's held within your application. Giving your customers business intelligence (BI) and analytics within your application offers them a window of insight into the data to help them optimize their business. You deliver more value which boosts end user adoption and means your client buys for longer.

In this series of demystifying the tech trends, my colleagues and I will be looking at busting the buzzwords to help you keep on track. Concerned about puzzling parlance, analytics argot, techie terminology – or plain old jargon? This series breaks down words and concepts to give you the deepest insight and understanding into how to talk the talk in the world of tech, so you can engage in conversations with the confidence of being data literate.