Every technology industry is talking about the benefits of Artificial Intelligence. More than a buzzword, AI is hyped as a panacea, while at the same time, it is often misunderstood by those who might benefit from it the most. AI may mean different things to different people, there are plenty of aspects that apply to all disciplines. The ability for a machine to "learn" from data it is presented is at the core of all AI use cases. The term "machine learning" derives from that most basic idea.

Today any investment is inexplicably linked with data, especially in the form of data flow. Without adequate quantity of data and in the absence of appropriate and relevant data-base, investment becomes a high risk venture. There is often a bridge between consumer data collection and data analytics in the companies, which reduces the probability to maximise return on investment (ROI). Thus, this article will guide you on techniques to maximise ROI from your data analysis strategies through AI-based machine learning system. We are living in the world of data explosion, which is a great opportunity for the companies who work with a large-scale consumers' real-time data-base.

Another common eDiscovery pitfall is the use of standard approaches for every case. Rather than dig in and discern data minimization and cost estimates for each case, many practitioners use generic formulas. Dubious tenets like "every stage of large cases goes to law firms" or "law firms always manage review for us" still rule the day. Teams automatically slap project planning formulas like 0 to 6 months for ECA, 6 to 12 months for full-blown eDiscovery and 12 to 24 months to finish eDiscovery, motions and trial preparations onto every eDiscovery project.

Another common eDiscovery pitfall is the use of standard approaches for every case. Rather than dig in and discern data minimization and cost estimates for each case, many practitioners use generic formulas. Dubious tenets like "every stage of large cases goes to law firms" or "law firms always manage review for us" still rule the day. Teams automatically slap project planning formulas like 0 to 6 months for ECA, 6 to 12 months for full-blown eDiscovery and 12 to 24 months to finish eDiscovery, motions and trial preparations onto every eDiscovery project.

Predictive analytics can be defined as a form of data mining that uses statistical modeling to analyze historical patterns, and then uses these models to project future outcomes. The deployment of artificial intelligence allows analytics technologies to spot relationships between variables that humans are simply incapable of seeing. In this article, we want to bring that theory to life with five predictive analytics use cases. There have been some newsworthy stories in this field, notably the "Target Knows When You're Pregnant" headlines that garnered so much attention a few years ago. Things have developed quite a bit since then. The evolution of widely available and accessible analytics platforms has provided access to sophisticated statistical models for companies of all sizes.

IBM's aggressive push into the data analytics market continued today with the announcement of Project DataWorks, a Watson initiative that IBM said is the first cloud-based data and analytics platform to integrate all types of data and enable AI-powered decision-making. Project DataWorks is designed to lower the complexity for business managers and data professionals to collect, organize, govern, secure and generate insight from multi-sourced, multi-format data. The goal: become what IBM calls "a cognitive business." "It's a system that will on-board data, tools, users, apps, all in a scalable and governed way," Rob Thomas, VP of Products, IBM Analytics, told EnterpriseTech. "The purpose is simple: we are preparing all data within a company for use by AI. We're helping people leap in to the future around AI and machine learning."