When it comes to hybrid cloud and digital transformation, it’s all about application services and leveraging appropriate on-premise, service provider, and hyperscaler cloud resources and services seamlessly and efficiently.

Google’s data cloud enables customers to drive limitless innovation and unlock the value of their data via its robust offerings under a single, unified interface. By migrating their data ecosystems to Google Cloud, organizations are able to break down their data silos and harness the full potential of their data. However, historically, migrating data warehouses has not been an easy task.

Today, businesses are hyper focused on developing data-driven applications and experiences. Whether for customers or end-users, they want to offer software that contains interactive dashboards, personalized data visuals, infographics, and charts. The reason is simple: Better access to data in the same workflow means more people will be more data-driven in their decision-making. If you need your application to display data in charts, graphs or other data images, you need embedded data visualization tools.

Making insights for your business isn’t easy. You're expected to always do more, do it faster, all without costing a small fortune. But how can you expect to do this when you’re using the wrong kind of analytics in the first place? Let’s explore. You may have heard of the 4 different types of analytics (the image below from Gartner helps visualize each type and how we use them) Think about how many post-mortem meetings you’ve had. Hindsight is important, of course!

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

The pursuit of business insights is a central focus the enterprise. More than a mere natural resource, data is fueling strategic decision making and as a result, the future of business. Hitachi Vantara is committed to helping accelerate data-driven experiences with cloud-ready infrastructure, advanced data management solutions, and expert services. But no company or provider in the 21st century can go it alone.

Cybersecurity is a data problem at its core. Yet, security teams haven’t achieved tremendous success in utilizing the modern data stack that data analytics teams have enjoyed for years. Security teams face constant pressure from vulnerabilities and breaches in their infrastructure and supply chains because they remain on a proverbial island with antiquated technology. Cybersecurity leaders must uplevel their strategies by implementing a modern security data lake.

Business intelligence (BI) is the ability to view and analyze data to provide actionable insights - but this ability wasn't always so available. The BI tools and platforms developed in the early days were very different to the modern analytics capabilities we have today In this article, we will discuss further about the history of business intelligence platforms. We will begin with the definition of business intelligence and its scope.

Ever wondered what our most-used components are? Here at Talend, one of my “Shadow IT” jobs is to report on component usage. If you've ever used Talend Studio (either the open source Talend Open Studio or the commercial version) you most likely already know and love the component tMap.

Data is the biggest asset for any business, and its advantages are many. Data can help enterprises better understand their customers, improve advertising campaigns and bottom lines, and personalize their content. However, you cannot access these benefits without proper embedded analytics and reporting tools that make data a true part of your day-to-day workflow. Because while raw data has a lot of potential, a business needs the right tools to access, manage, share and analyze it to great effect.