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.

Data scientists and machine learning engineers in enterprise organizations need to fully understand their data in order to properly analyze it, build models, and power machine learning use cases across their business. Due to the lack of tooling specifically designed for data discovery, exploration, and preliminary analysis, this presents a significant challenge for these teams.