Saving money? That gets just 17% of the votes, with an almost equal number (14%) citing the need to drive revenue, not save money, as their motivation to get smart with Hadoop. This is particularly interesting, since those companies with the most experience running Hadoop tend to use it for ETL functions (74%), followed by business intelligence (65%) and data science (62%). As mentioned above, in its early days, Hadoop was often dismissed as ETL for companies too cheap to pay for Informatica, IBM, or Oracle. In a shift, those that have yet to deploy Hadoop now look primarily for its value to transform BI (69%), not ETL (51%). Clearly, word is getting around that Hadoop, if ever it was a cheap way to do ETL, is much more than that.
Data aggregation and predictive intelligence at the scale needed for today’s enterprises requires use of machine learning for predictive modeling. Machine learning means training a machine to associate known patterns with known outcomes, and then when the machine sees new patterns it can predict new, unknown outcomes. Machine learning is really about adjusting the knobs of the predictive intelligence engine to get it closer to the right answer. Using machine learning, we’re able to now test whether or not specific actions will take place and to predict a company’s likelihood to buy and offer other data such as time to close or products reviewed. B2B marketing and sales teams can use these predictions to target the right accounts. Machine learning makes it possible to extract meaning from huge and chaotic piles of data.
This abridged guide will cover the essential things to look out for in selecting and purchasing embedded analytics software. The full guide is available here. Whether you’re producing automation software, SaaS products or cloud applications, it’s likely to assume you’re collecting a lot of data in the process. With an increasing number of companies and individuals understanding the value of using data to improve different aspects of their business, the ability to offer a powerful data analytics and BI feature within your existing application can give your product the competitive edge that it needs and greatly improve the value you offer to customers
With this latest version of the SDK, we found ourselves special-casing even more code for specific platforms, and we came to a conclusion: just release different builds for different versions. Using Babel plugins, we inject variables into the code and use dead code elimination to remove disabled branches (take a look at the end of Storage.js for an example). Making a build-time split seemed less than ideal at first, but it allowed us to ensure that our developers got exactly the features they wanted, without having to rely on potentially flaky feature-detection code. With 1.6, the npm module provides three different packages: plain old 'parse' for browsers, 'parse/node' for Node.js, and'parse/react-native' for React Native Apps. This will let us add more platform-specific features down the road.
"Technical skills can be learned but attitude is an innate personal philosophy that drives enthusiasm, customer focus, problem solving and elements like team work, quality and innovative thinking," says Behenna. "The digital revolution - for want of a less hackneyed and overtraded phrase - depends, for its success, on the spark that begins with attitude then commingles with aptitude to deliver game-changing thinking, products and services." ... "You need people who are delighted to work for an organisation that has strived to create an environment that links individual ambition to the company's success," says Behenna. "No hyperbole, just grafters with a hint of genius. That's what I would want from IT professionals."
For many FinTech startups, operating in Switzerland makes a lot of sense. The nation boasts an incredibly stable economy, a strong reputation for innovation, and an emphasis on security and privacy. On top of that, the Bitcoin is a legitimate foreign currency there, meaning there is no legal uncertainty about using it within the country. In fact, the Swiss Financial Market Supervisory Authority (FINMA) authorized the Bitcoin stock exchange (ECUREX) in May, facilitating the exchange from Swiss Francs to Bitcoin. The new draft of the provision will address virtual currencies and go into effect at the beginning of 2016. According to Switzerland Global Enterprise, other advantages include the nation's liberal laws, loosely regulated labor market, high security, and excellent free-trade policies.
“It’s not quite clear how the cloud will evolve, there is a lot of R&D that needs to be done to make the cloud available to everyone,” Agrawal said in an interview. “The cloud is a market disrupter, as a big a disrupter as the personal computer was in the 1980s. “Whole business models will change because there will be much greater focus on data services,” Agrawal said. “Right now, the cloud is not technologically optimized for high performance computing, but eventually what runs on super computers today will run on the cloud. There is an enormous amount of software and hardware development to be done, and UTSA with its Open Cloud Institute and cybersecurity programs aims to be the university that will provide the smart workers the industry will desperately need. ...”
Business executives are increasingly moving to an IT environment that is no longer focused on large complex projects but is oriented towards shorter, more sustainable efforts to drive change and innovation. This is known as "Fast IT." The goal is to promote efficiency, agility and innovation to enable IT departments to stay ahead of - and help promote - the rapid pace of dynamic change. "To deploy Fast IT is to unify infrastructure to reduce network complexity and speed up service deployment," David Meads, Cisco Africa VP, explained in an article on ITWeb. "Fast IT has three streams: software and automation, a converged and consistent infrastructure, and a flexible consumption model which enables scaling in a modular way that allows rapid growth without compromising efficiency. The key principles of fast IT are 'simple, smart, and secure."
"People are becoming aware of the value of data, not just in IT but overall," Gartner analyst and report co-author Nick Heudecker, who conducted the research in June, told CIO.com. "They're creating data and using it as a competitive advantage." ... Increasingly, CFOs, CMOs, COO, and chief data officers are introducing such analytics projects at their organizations, as they endeavor to meet CEO mandates to learn more about customers, Heudecker and co-author Lisa Kart wrote in their report. In 2015, CIOs triggered 32 percent of the big data projects, with business unit heads kicking off 31 percent of the projects. That's a shift from 2014, when research suggested that CIOs initiated 37 percent of big data projects, compared to 25 percent of projects that were started by business line leaders.
Given the strategic imperative from the c-suite to harness the power of big data, why do the majority of these projects fail? Gartner estimates the failure rate to be nearly 60 percent. Similarly, Capgemini finds that only 27 percent of executives believe big data projects succeed and of those only 8 percent are “very” successful. If adoption of big data is not the problem, what is? It seems that turning adoption into value for an organization remains elusive. Often, organizations fail to see justifiable ROI from their big data investments because no clear blueprint exists for how to take a project from inception to completion with delivering value in mind.
Quote for the day: “The simple act of paying positive attention to people has a great deal to do with productivity.” -- Tom Peters