The world is making rapid progress in the areas of Big Data, Artificial Intelligence and Machine Learning. These are the core drivers of what many analysts have come to refer to as the Fourth Industrial Revolution, epitomised by the increased whittling away of the boundaries that hitherto existed between the physical, digital and biological worlds. There is a clear imperative for pharmacists, pharmaceutical scientists and medical professionals in the field of research and development in developing countries like Nigeria, to increasingly tap into this world of Big Data, Artificial Intelligence and Machine Learning and partake of the revolution that is happening before our very eyes. And the reason is simple. Artificial Intelligence is helping to make pharmaceutical research and new drug discovery less expensive and definitely more productive. Researchers realise that in the time that it would have taken to test the efficacy of say a handful of chemical molecules manually, with AI, it is possible to test several hundreds of different chemical molecules.

Customer data platforms (CDPs) and data management platforms (DMPs) are regularly confused. The mix-up stems from the fact that marketers use both CDPs and DMPs to collect data and to create audiences. A customer data platform allows you to collect data from relevant touchpoints where customers interact with your business. A CDP will organize this data and relay it to your other martech tools. Armed with such data, you can better target your audience with messages honed in line with your brand communication strategy.

As a growing number of companies are looking to build out and leverage artificial intelligence solutions across their organization, they're often delayed due to poor data quality that exist across their business operations. This quality deficiency prevents them from proceeding with their intended AI rollout. Once AI is fully implemented, it can improve data quality throughout a company. Being faced with data quality issues forces a company to shift priorities and resources from implementing AI to fixing these quality shortcomings before they can proceed. This means extensive time delays, allocation of resources, and a slow draining of the AI budget.

The exponential growth of data is creating scaling and cost challenges. Not being able to scale storage and compute resources independently results in suboptimal resource utilization of data center infrastructure investments. Customers need to solve for this while controlling their licensing costs. They also need to perform real-time analytics on their ever-growing data sets. With Intel's Data Management Platform (DMP), you can build an infrastructure that allows you to operate on petabyte-scale data and harness the power of that data.

Data is streaming through businesses at increasingly faster rates, creating time-to-insight and time-to-action data integration challenges which can be crucial to rising above competitors. As fast access to information becomes a greater demand for businesses, a more apparent challenge is in condensing data into useful information for developing insights. Many companies are spending more time and resources on increasing data and preparing it than they are on analysis. Agile, efficient, flexible data integration strategies are crucial to expanding big data analysis. If you're looking for easier ways to handle data integration projects, you may want to try looking into introducing artificial intelligence (AI) capabilities to your data integration strategies toolkit.

Immuta Inc., the data management for AI vendor, said it will use a $20 million funding round to establish a European beachhead in London as it sharpens its focus on helping enterprises develop "ethical" AI algorithms that comply with new European privacy rules as well as possible U.S. data privacy regulations. The Series B funding round announced Wednesday (June 20) was led by DFJ Growth and joined by new investors Citi Ventures and Dell Technologies Capital. The startup's customers include banks such as Barclays (NASDAQ: DTYS), insurers and U.S. intelligence agencies. Immuta's data management platform is designed to provide greater control of the data fed into algorithms, speeding deployment as well as increasing visibility into how automation tools are functioning. The goals include "controls around data science" Immuta CEO Matthew Carroll said in an interview, along with helping enterprises get a handle on what he called "analytics ethics."

With a successful Informatica World 2018 behind us I'd like to share a recap of my session on Next Gen Analytics (NGA) Strategies in a six-part blog series (one on each strategy). I suggest you read the blog I wrote the week before Informatica World that summarized the strategies. To illustrate these six NGA strategies, I'll summarize some of the strategic focus areas from our most successful customers working on NGA initiatives. See if you recognize any of the strategies listed above as you read through these customer stories (HINT: I underlined them for you). Every company wants to be more engaged with customers and provide a better customer experience.

As artificial intelligence and machine learning technologies make their way into advanced data management platforms, the emphasis for developers and data scientists is broadening to include not just deployment but "control" of the data accessed by these automation tools. Along with the goal of completing often-tedious data science tasks in minutes and even, some claim, seconds, new enterprise tools also emphasizing greater visibility and control of the development processes underpinning AI and machine learning applications. One such data management platform released this week by analytics startup Immuta of College Park, Md., seeks to ride the wave of AI and cognitive systems algorithms increasingly used by business to augment decision-making. The company argues that relying on these algorithms make companies more susceptible to risks ranging from critical errors to fraud. To gain control of algorithm-driven business models, the company argues: "Organizations require greater control over the data being used by machine learning and AI models."

Businesses large and small are being lured in by the potential of artificial intelligence (AI), machine learning (ML), deep learning and cognitive computing, while others are still trying to figure out how to tell them apart. It's easy to fall under the spell of technology that promises to solve all of your business challenges, just for the asking. Ask any business user and their objective seems simple: offer the right product, to the right person, at the right time, via the channel of their choice. But delivering on this magical formula is hard if customer information is scattered across systems and channels. Companies collect large volumes of data, but often have no single source of truth across functional groups, like sales, marketing and support.

Salesforce announced the acquisition of Krux in November 2016. The announcement was made at the recently concluded Data Matters event at The Venetian, Las Vegas. Salesforce DMP made its presence felt at one of the most sought-after Martech events for the community of brand marketers, publishers, agencies, partners, and employees. Salesforce Data Management Platform powered by Krux. As per the blog by Krux Co-founder and CEO Tom Chavez that appeared on Salesforce, Salesforce DMP is built for the community of Data Trailblazers.