Data Science Central
An explosion of Big Data is rapidly changing the IT landscape. While Big Data generates vast opportunities for new sources of revenue, customer insights and operational efficiencies, it also creates new challenges for existing data infrastructure. To keep up with this explosion and capitalize on new data-driven business opportunities, enterprises must select a data analytics architecture that fits the specific needs of their business and the structure of their data.
Business intelligence (BI) sits at the center of many organizations’ efforts to enable data-driven decisions and actions through their enterprises. But BI is changing, both for organizations just getting started with BI and those that have invested in developing an enterprise standard. We have entered the age of BI “democratization”: tools and applications are becoming easier to use, more visual, and more adaptable to the requirements of a greater variety of users. Across business functions, users are excited by the potential of the new BI and visual analytics technologies and are clamoring for the opportunity to move beyond the limits of spreadsheets and canned reporting. However, a balance must be struck because no organization wants BI democratization to devolve into BI chaos.
It’s probably not news to you that Hadoop is becoming an essential part of using big data to make fact-based decisions.It’s probably not news to you that Hadoop is becoming an essential part of using big data to make fact-based decisions.
Have you ever thought of working in Artificial Intelligence (AI)? With the advancement in digital technologies, AI and machine learning are the buzzwords today. And if you want to build a career in this fast-growing domain, now’s the time to start. Join domain expert and Big Data influencer, Ronald Van Loon, and Anand Narayanan, Chief Product Officer at Simplilearn, as they discuss why AI and machine learning are so popular and what skills can help you carve out a career in this field. In this fireside chat you’ll learn about: 1. AI and its various disciplines. 2. Some real-life applications of AI and machine learning. 3. Career choices in AI and what skills can help you land a job in this domain.