Where Big Data Jobs Will Be In 2015. 108610078476587441572?utm_content=buffer38bdd&utm_medium=social&utm_source=twitter. Download Brochure - Cause Analytics | Download a brochure about Cause Analytics. Big Data - The 5 Vs Everyone Must Know. 108610078476587441572?utm_content=buffer64903&utm_medium=social&utm_source=twitter. Simbeckhampson : Selling Customer Data, great ... Plus.google. Download Brochure - Cause Analytics | Download a brochure about Cause Analytics. Simbeckhampson : Bug Tracking #bugdata #dataviz ... Plus.google. Two Roads to Instant Big Data. Defining Big Data. Rethinking Big Data to Give Consumers More Control - H. James Wilson. By H. James Wilson | 10:00 AM May 8, 2014 The White House recently published two new reports on Big Data and privacy (here and here). The reports outline six policy recommendations, including new legislation to define consumer rights regarding how online activity data is gathered and used. Not surprisingly, the findings are already viewed “warily in Silicon Valley, where companies see it as the start of government efforts to regulate how they can profit from the data they collect from email and web surfing habits,” according to The New York Times.
Yet by rethinking the use of personal data from the ground up, entrepreneurs can mine new opportunities for experimentation and innovation. These interlinked objectives aim to give consumers more say over their data and more insight through personal analytics. Making Open Data personally relevant. One prototype enables a patient to use her data to understand the exact dollar amount her policy will cover for a specific procedure. The state of big data in 2014 (chart) | VentureBeat | Business | by Matt Turck, FirstMark Capital.
It’s been almost two years since I took a first stab at charting the booming big data ecosystem, and it’s been a period of incredible activity in the space. An updated chart was long overdue, and here it is (click the full-screen option to enlarge): Editor’s note: VentureBeat has cherry-picked the best of these companies to present at our upcoming DataBeat conference, May 18-19 in San Francisco. A few thoughts on this revised chart, and the big data market in general, largely from a VC perspective: Getting crowded: Entrepreneurs have flocked to the space, VCs have poured money into promising startups, and as a result, the market is starting to get crowded. Certain categories like databases (whether NoSQL or NewSQL) or social media analytics feel ripe for consolidation or some sort of shakeout (which may have already started in social analytics with Twitter’s acquisitions of BlueFin and GNIP).
Still early: Overall, we’re still in the early innings of this market. Big Data - Cause Analytics | Spot big data opportunities in your data. We turn your organisation's raw data into unified, clean and usable business intelligence which stays up to date. Rapidly create value by freeing data from legacy systems. Make lightning-fast queries on large amounts of data to spot big data opportunities. Whether you call it 'big data' or not, increasing data volumes and the variety of data types that firms must process pose a challenge to existing ways of delivering BI and analytics capabilities. How can companies make the most of big data? Companies that take advantage of big data perform better. Yet, the vast majority of companies have not tapped into the power of big data – swathes of information that technology now allows us to process for patterns and insights – to guide their decision-making.
According to a 2013 Gartner survey, fewer than 8% of companies have implemented big data technology. Although this figure is predicted to grow rapidly over the next five years, many are struggling to adjust to the big data phenomenon. In 2012, the Aberdeen Group found that the proportion of executives reporting that their companies could not use unstructured data, and complaining that the volume of data was growing too rapidly, had increased by up to 25% in the previous year.
So what must companies do to reap the rewards of big data? Organizations must, for example, recast their decision-making culture so senior executives make more judgments founded on clear data insights, rather than resorting to gut instinct. Retail - Cause Analytics | Build loyalty. Increase sales. Make better decisions with more knowledge of sales, loyalty, supply chain, price and cost. A retailer using big data to the full could increase its operating margin by more than 60 percent Knowledge Give managers the tools they need to carry out effective store forecasting and location planning. Spot opportunities and know about populations, demographics, health, wealth and other key metrics required to come to sound planning decisions. Sales Use electronic point-of-sale (EPOS) and distribution data. Loyalty Grow loyalty. Supply chain Build smarter and more resilient supply chains. Price and cost Get your costs under control and price more effectively. Visually explore product stock levels Model your customer behaviours and different sales process conversion rates.
Plus.google. Simbeckhampson : What is the state of your... Plus.google. What is the state of your #BigData project? Interesting post from Nick Heudecker, Research Director at Gartner. "The #Gartner #BI Summits are an ideal venue to connect with vendors and end users not just in BI, but also the general Big Data space. Last week in Las Vegas I led two great roundtable discussions on the the Big Data Ecosystem. Interest was high: I was supposed to have a total of 28 attendees, but had 43. "The roundtables were also a great opportunity to collect some data. "The core takeaway from the discussions was confusion. " Read on: Big Data – A Visual History | Winshuttle. From cuneiform, the earliest form of writing, to data centers, the human race as always gathered information. The rise in technology has led to the overflow of data, which constantly requires more sophisticated data storage systems.
The recognition of information overload started as early as the 1930s. The boom in the U.S. population, the issuing of social security numbers, and the general growth of knowledge (research) demanded more thorough and organized record-keeping. However, it wasn’t too long before the first flag of warning was raised. While the growth of knowledge was good for society, it was quickly leading to a storage and retrieval problem for libraries.
As information continued to boom in the following decades, organizations began to design, develop, and implement centralized computing systems that would allow them to automate their inventory systems. With business intelligence piling up, the challenge of management and storage quickly surfaced yet again. What Will Happen to ‘Big Data’ In Education? By Anya Kamenetz Yesterday, a $100 million startup lost its last customer. According to a Politico article, the state of New York, inBloom‘s last remaining client, will delete all student data on the repository due to privacy concerns. InBloom’s company spokesperson told Politico the nonprofit was “pushing forward with our mission,” though at the moment there are no known state partners. InBloom’s trajectory has shined a spotlight on the public’s sensitivity around what happens to student data.
When it first began as a mammoth ed-tech project in 2011 by the Council of Chief State School Officers, the Bill and Melinda Gates Foundation and the Carnegie Corporation called the Shared Learning Infrastructure, the purpose was to provide open-source software to safely organize, pool, and store student data from multiple states and multiple sources in the cloud. In February 2013, just a little over a year ago, SLI relaunched as an independent nonprofit named InBloom. Related. How To Choose The Best Tool For Your Big Data Project.
Trying to choose the right tool for a big data project? This chart (and three simple rules) can help guide you through the options. Flags picture from Shutterstock This chart is based on one shown by Microsoft Research senior research program manager Wenming Ye during a presentation at Build 2014 last week. “While choosing appropriate tools is important, skills remains the biggest challenge, Ye noted. “There’s a lot of talk about challenges in the tools and challenges in the data,” he said. “But what’s really important is actually the people. There’s a lack of understanding — we really have a lack of people who are able to understand and use these distributed tools. Yen suggest three key rules when dealing with big data: Make sure that you’re using data to drive decisions, and not merely tracking it for its own sake.Continuously update and refine your metrics.Use automation to conduct more experiments and ask more questions.
Simbeckhampson : Maps help to tell the story... Plus.google. #DataViz shows Chicago’s Middle Class Disappear In case you haven't seen it yet. This is the work by Daniel Kay Hertz, a masters student at the University Of Chicago’s Harris School of Public Policy. The graphic shows the demise of the middle class foundation of Chicago, an American city. "[T]he grey squares, which illustrate the middle class that dominated the most of the city’s neighborhoods in 1970s, quickly vanish over 40 years. [...]
Source: #Maps #USA #Storytelling #Demographics Posted by +Dan Durrant w/ +David Pidsley Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies. www.CauseAnalytics.com CauseAnalytics : Big data has a big future in... Anticipate Change Blog | Cause Analytics. Simbeckhampson : #BigData UK Skills Gap: playing... Plus.google. The UK is not ready for data driven jobsHayden Richards writes on 10.Oct.13 Appears there will be a rising skills gap to fill due to the emergence of new services surrounding the internet of things (aka industrial internet).
Better education for the future workforce is required. Citation:The high degree of servitization occurring in the manufacturing and industrial sector is fuelling innovation and will in the future create brand-new roles such as digital mechanical engineers in the new workplace where machine human collaboration is the norm. [...] Companies need to retrain staff in this new digital and data driven reality. / #HumanResources #Manufacturing #DriveInnovation #Collaboration . . . . Posted by +Dan Durrant Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies. www.CauseAnalytics.com Spark is a really big deal for big data, and Cloudera gets it. One of the most-important pieces of news to come out of this year’s Hadoop World conference might be Cloudera’s decision to provide full enterprise-grade support — like paid product support and inclusion in Cloudera’s Hadoop distribution, not just a technological integration — for Apache Spark.
It’s further proof that the Hadoop workload of the future could very well look very different than the Hadoop workloads of the past and present. Spark is an in-memory data-processing platform that is compatible with Hadoop data sources but runs much faster than Hadoop MapReduce. It’s particularly well suited for machine learning jobs, as well as interactive data queries, and is easier for many developers because it includes APIs in Scala, Python and Java. Spark is in use at a number of large web companies and web startups already, and a startup called Databricks that aims to commercialize Spark recently launched with $14 million in venture capital in its pocket.
Plus.google. The Future of Search and Information Usage Listening to Google's CEO, Larry Page, talk is always like going to the Oracle to discover the future. The fact is that like most of us he too can make mistakes, that some of the best ideas Google has had came from personal experiences which we can all empathise with (waiting in the cold for a bus, getting afflicted by something no one can quite cure), the difference is he is in a better position to do something about it than most of us.
He is right on his call for greater transparency in government (closed doors are always led by good intentions and we all know where that path leads). The NSA itself hinted that this may now be a more prudent approach ( Privacy (or its potential invasion) challenges us. Historically we have never had anything other than the illusion of privacy. How IBM Is Using Watson And An Innovative Workspace To Crunch Big Data For Big Solutions. IBM loves Big Data.
The bigger it gets, the more servers, storage, and services Big Blue would like to sell you (a lot more, please). But the volumes involved have already grown so big that IBM’s own researchers struggle to get a handle on it. Last year, for example, IBM fellow Laura Haas asked one of her colleagues at the company’s Almaden research center in Silicon Valley why he wasn’t using bigger data sets. Because, he replied, it takes 80% of my time just to prep the data I have. Haas realized that the more IBM’s research agenda was consumed by analytics, the more time and energy its experts would spend struggling with expanding data sets, slowing down the pace of discovery. The obvious thing was to hand the volumes in question over to dedicated data scientists, but removing researchers from the loop would only make things worse. “We call it cultivating ‘strategic serendipity,’” says Haas, who is also the director of technology and operations for the lab.
5 things that will remake big data in the next 5 years. Big data has evolved a lot of the past few years; from a happy buzzword to a hated buzzword, and from a focus on volume to a focus on variety and velocity. The term “big data” and the technologies that encompass it have been pored over, picked over and bastardized sometimes beyond recognition. Yet we’re at a point now where it’s finally becoming clear what all of this talk has been leading up to. It’s a world of automation and intelligence, where it’s easier than ever to mine data, but also to build intelligence into everything from mobile apps to transportation systems. Big was never really the end goal, but the models driving this change generally feed on data to get smarter.
Variety was never really a goal, it’s just that the more we can quantify, the more we can learn about the world around us. It’s a world we’ll delve into in great detail at our Structure Data , which kicks off just a week from today (March 19) in New York. 1. Source: Hortonworks 2. AI now comes in a food truck. 3. Big Data becomes 2013's scariest fad - Future Tense.