Existing Big Data solutions are mainly focused on the discovery and analysis of data. The solutions are scalable and highly available but tedious when swapping in and swapping out occurs in disarray and thrashing takes place. The resolution for thrashing through machine learning algorithms and support nomenclature is through simple techniques. Organizations that have been collecting large customer data are increasingly seeing the need to use the data for swapping in and out and thrashing occurs in both transaction processing and online analytical processing. Therefore, there is a growing need for support on thrashing using machine learning algorithms and solutions for use in "Big Data."

The Big Data industry, already worth $100 billion+, is growing twice as fast as the software business as a whole. It is so strategic to the future of Enterprise IT that giants like Oracle, IBM, Microsoft, and SAP have now between them spent more than $15 billion acquiring software firms specializing in data management and analytics.

As the management and analysis of massive amounts of data becomes an industry in its own right, is your own company up to speed?

You have great SaaS business app ideas. You want to turn your idea quickly into a functional and engaging proof of concept. You need to be able to modify it to meet customers' needs, and you need to deliver a complete and secure SaaS application. How could you achieve all the above and yet avoid unforeseen IT requirements that add unnecessary cost and complexity? You also want your app to be responsive in any device at any time.

The taxi industry never saw Uber coming. Startups are a threat to incumbents like never before, and a major enabler for startups is that they are instantly “cloud ready.” If innovation moves at the pace of IT, then your company is in trouble. Why? Because your data center will not keep up with frenetic pace AWS, Microsoft and Google are rolling out new capabilities. 

In IT, we sometimes coin terms for things before we know exactly what they are and how they’ll be used. The resulting terms may capture a common set of aspirations and goals – as “cloud” did broadly for on-demand, self-service, and flexible computing. But such a term can also lump together diverse and even competing practices, technologies, and priorities to the point where important distinctions are glossed over and lost.
 
Application transformation and DevOps practices are two sides of the same coin. Enterprises that want to capture value faster, need to deliver value faster – time value of money principle. To do that enterprises need to build cloud-native apps as microservices by empowering teams to build, ship, and run in production.
 
According to Forrester Research, every business will become either a digital predator or digital prey by 2022. To avoid demise, organizations must rapidly create new sources of value in their end-to-end customer experiences. True digital predators also must break down information and process silos and extend digital transformation initiatives to empower employees with the digital resources needed to win, serve, and retain customers.
 
Disruption, Innovation, Artificial Intelligence and Machine Learning, Leadership and Management hear these words all day every day... lofty goals but how do we make it real? Add to that, that simply put, people don't like change. But what if we could implement and utilize these enterprise tools in a fast and "Non-Disruptive" way, enabling us to glean insights about our business, identify and reduce exposure, risk and liability, and secure business continuity?
 
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience.