Accelerate Decision Making with Real-Time Analytics on AWS

The global investment in digital transformation estimated to exceed $10 trillion over the next five years. Despite the tremendous resources going towards this cause, studies show that up to 70% of these initiatives will not achieve their stated goal. What does it take to win?

As organisations look to enrich their approach to advanced analytics, employees company-wide are filling the skills gap. These are todays citizen data scientists, dedicated to solving problems and building solutions with data, no matter what their background. For them to truly exceed, they need powerful, enabling technology and a framework to use it.

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.

Staying competitive in today’s economy requires that organizations derive intelligent insights from data and make their processes automated, intelligent, adaptive, and self-optimizing. This is where artificial intelligence (AI) and machine learning (ML) play a central role in business transformation.