There's greater accountability that is expected of data controllers or data processors, and this heralds the arrival of a compliance burden on entities. Join us to discuss: · The case for regulation of personal data usage · Expectations of the regulator · How do you comply with the regulations? Come, let's talk about #privacy. Pathways International and Jose Almeida will host a webinar on personal data protection, titled: Data Protection Act - Roadmap to Compliance.

As businesses move swiftly to adjust to changing circumstances, it's more essential than ever to have access to the right data, at the right time and place. It's not an exaggeration to say many businesses are drowning in data – there's more available than we know what to do with. Structure and discipline are needed to help us understand what - right now - is an insight, and what is simply noise. Increasingly, this needs to be done in real-time; otherwise, the opportunity to act on the most timely insights will be lost. Leveraging this wealth of data involves starting with a strategy in mind – what do you want to do with your data?

The fourth industrial revolution is giving rise to a data culture for accelerating digital transformation. Organizations are developing data-driven business models for utilizing data to its maximum potential. As a result, data has become an asset and a significant part of almost every business operation. Practically every organization has started implementing aggressive data collection and analysis for various applications. For this purpose, organizations deploy large data centers to store and process data.

AI/Machine Learning--AI continued to grow in popularity over the past year, becoming well-institutionalized within many large enterprises. We argued in a previous post, however, that too many companies employed AI pilots and prototypes, and not enough firms had implemented production deployments. As with analytics, the use of AI is increasingly being democratized through automated machine learning (AutoML). Several contributors to KD Nuggets' review of AI and ML trends for 2019 suggested that AutoML would become more popular over the next year. It will make machine learning models easier to create for business analyst types, as well as dramatically increasing the productivity of data scientists--that is, if they can be persuaded to use it.

It seems businesses these days are pinning their hopes on artificial intelligence to finally make bank from big data -- and this hasn't escaped the notice of software vendors. They're stamping AI on new products with such repetitive thuds, one can easily guess the puff inside the packaging. Knowing what AI can and can't do could help companies shop wisely for technology that delivers on its promise. "Cognitive organizations are not going to happen tomorrow morning," said Tripp Braden (pictured, third from left), executive coach and growth strategist at Strategic Performance Partners. A cognitive business would be fully data and AI-driven across all departments; even companies on the cutting edge are several years out from becoming one, he added.

What does a data-driven business look like? Is it endless lines of code and algorithms running on cloud infrastructure, sending signals back to a predictive analytics lab? Is it a huddle of Ph.D. data scientists poring over graphs that no one else can understand? Both experts and technologies have a place in it, but relying too much on either can blow the whole thing. A business that runs day-to-day on data needs a culture that meshes machine intelligence and human business sense.

Demand for data has been surging over the past few years. Companies are rushing to adopt in-house data warehouses and business analytics software, and are reaching for public and private databases in search of data to kick-start their artificial intelligence/machine learning (AI/ML) strategies. Due to the growing demand, good data is becoming a valuable commodity, like oil in the 20th century, and companies are beginning to compete for the most lucrative reserves. In order to understand why data is important for your business, you must first understand the five reasons if gives you a competitive advantage. Until very recently, companies did not realize that they were sitting on a goldmine of data and did not know what to do with it.

Industry 4.0, manufacturing ecosystems and the connected enterprise Deloitte University Press

The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete. As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet. So what does this mean?

Advancements in machine learning and artificial intelligence (AI) opens new doors for businesses to make data-informed decisions, and if your business does not take advantage of the growing industry of data analytics, your competitors will steamroll ahead. The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete. As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet.