Improve Your Business Performance: Why Meaningful Data is more important than Big Data. Any business looking to sell more, spend less or build better customer relationships first needs to understand how their organisation really operates. By far the most accurate way of doing this is by analysing data. However, Business Intelligence (BI) is failing and Big Data has become a catch-all term that is irrelevant to many companies. What decision makers really need is Meaningful Data. This Bright North article explains how meaningful, relevant data can help solve business problems, optimise performance and make accurate predictions about future behaviour. Successful business optimisation is grounded in understanding. Any business looking to sell more, spend less or build better relationships with customers first needs to understand how their organisation really operates.
By far the most accurate way of doing that is by analysing data. The Business Intelligence (BI) industry has been built around the premise of capturing and presenting this data. The Failure of BI Bad Data. Why Big Data Is Not Truth. Photo The word “data” connotes fixed numbers inside hard grids of information, and as a result, it is easily mistaken for fact. But including bad product introductions and wars, we have many examples of bad data causing big mistakes. Big Data raises bigger issues. The term suggests assembling many facts to create greater, previously unseen truths. It suggests the certainty of math. That promise of certainty has been a hallmark of the technology industry for decades.
Kate Crawford, a researcher at Microsoft Research, calls the problem “Big Data fundamentalism — the idea with larger data sets, we get closer to objective truth.” Myth 1: Big Data is New In 1997, there was a paper that discussed the difficulty of visualizing Big Data, and in 1999, a paper that discussed the problems of gaining insight from the numbers in Big Data. “But now it’s reaching us in new ways,” because of the scale and prevalence of Big Data, Ms. Myth 2: Big Data Is Objective Myth 3: Big Data Doesn’t Discriminate. A Giant Step Forward for the Internet of Things and Big Data. Andy Stanford-Clark, IBM Master Inventor Andy Stanford-Clark, an IBM Master Inventor who lives in the United Kingdom, jokes that his goal was “world domination” in 1999 when he and Arlen Nipper of Eurotech invented a protocol aimed at greatly improving machine-to-machine communications.
This was at the time when another British technology pioneer, Kevin Ashton, coined the term “Internet of Things” to describe how the Internet could be connected to the physical world via a vast network of sensors. Stanford-Clark believed that his protocol, now called MQ Telemetry Transport, or MQTT for short, would enable organizations to quickly and affordably gather, integrate and make use of all of that sensor data. It would be an essential underlying technology for the Internet of Things. Fast forward to today. OASIS, one of the leading technology standards bodies governing the evolution of the Internet, has just announced that it will accept MQTT as an industry standard protocol. We need a data democracy, not a data dictatorship.
( gigaom.com ) -- The democratization of data is a real phenomenon, but building a sustainable data democracy means truly giving power to the people. The alternative is just a shift of power from traditional data analysts within IT departments to a new generation of data scientists and app developers. And this seems a lot more like a dictatorship than a democracy — a benevolent dictatorship, but a dictatorship nonetheless.
These individuals and companies aren’t entirely bad, of course, and they’re actually necessary. Apps that help predict what we want to read , where we’ll want to go next or what songs we’ll like are certainly cool and even beneficial in their ability to automate and optimize certain aspects of our lives and jobs. In the corporate world, there will always be data experts who are smarter and trained in advanced techniques and who should be called upon to answer the toughest questions or tackle the thorniest problems. 5 strategic tips for avoiding a big data bust. "Big data" has arrived as a big business initiative. But the hip, experimental, ad hoc veneer of blending data streams to surface bold discoveries belies a massive cultural and technological undertaking not every organization is ready for.
Without a strategic plan that includes coherent goals, strong data governance, rigorous processes for ensuring data accuracy, and the right mentality and people, big data initiatives can easily end up being a big-time liability rather than a valuable asset. [ InfoWorld's Andrew Lampitt looks beyond the hype and examines big data at work in his new blog Think Big Data. | Download InfoWorld's Big Data Analytics Deep Dive for a comprehensive, practical overview. ] Following are five strategic tips for avoiding big data failure. In many cases, the advice pertains to any data management project, regardless of the size of the data set. But the advent of massive data stores has brought with it a particular set of pitfalls. Related articles. Big Data and a Renewed Debate Over Privacy. Big Data’s Evolution: 5 Things That Might Surprise You.
Over the past several years, Big Data has gone from being a somewhat obscure concept to a genuine business buzzword. As is often the case with buzzwords, when you dig a little deeper you find that many people have substantial misconceptions about what Big Data is, where it came from and where it is going. Here are a few things that might surprise you about the evolution of Big Data: There are more “failures” out there than you’d think. We’re bombarded with the hype, but the reality is that this is still an early technology. As people are unfamiliar with the tech components of Big Data, they’re often prone to thinking that they can jump in and do everything themselves.
Dave Spenhoff is the VP of Marketing at Infochimps. API > Knowledge, Examples, White Papers, Articles and Templates on Performance Management, Balanced Scorecard, Key Performance Indicators, BI. The Advanced Performance Institute (API) is pleased to announce the findings of a worldwide study conducted in conjunction with Actuate Corporation. We have taken the 20th anniversary of the Balanced Scorecard - one of the most popular performance management tools - to conduct a global study to understand the current state of business performance management (BPM).
The study 'Measuring and Managing Performance - A Global Study' incorporated responses from over 3,000 companies across the globe, making it one of the largest and most comprehensive surveys ever conducted in the field of performance management. The newly released white paper 20 Years of Measuring and Managing Performance: From KPIs and Dashboards to Performance Analytics and Big Data outlines the findings of the study and provides a BPM maturity model. More mature BPM approaches generate significantly higher business benefits. The seven factors that were common among more mature BPM approaches were: Some key numbers include: Seven dirty secrets of data visualisation. Net magazine is the number one choice for the professional web designer and developer. It’s here that you find out about the latest new web trends, technologies and techniques – all in one handy package. Each issue boasts a wealth of expert tips and advice, including in-depth features and over 30 pages of advanced front- and backend tutorials on subjects as diverse as CSS, HTML, JavaScript, WordPress, PHP, and plenty more. net compiles the hottest new sites from around the web, and being the voice of web design, our mission is to source the best articles written by the best people in the industry and feature interviews and opinions crammed with inspiration and creative advice.
In short, If you're serious about web design and development, then net is the magazine for you. Editorial Advertising. Big Data Disaster. The World's Top 10 Most Innovative Companies in Big Data | Most Innovative Companies 2013. 1_Splunk For deploying its data crunchers to improve operations for its 5,000 clients, from Zynga to Rutgers University to Comcast to the FBI. 2_Quid For tracking emerging tech trends with trillions of data points. Quid funnels information from patent applications, research papers, news articles, funding, and others, to create interactive visual maps of current happenings in technology sectors. 3_Kaggle For pitting data scientists from across the globe against each other to solve research problems for prize money. 4_ZestFinance For hacking a new credit rating system by analyzing thousands of variables to determine an applicant’s credit score, with the goal of providing better access to loans for those with poor credit. 5_Apixio For curing data headaches by streamlining electronic health records. 6_Datameer For visualizing business intelligence by designing data sets into a spreadsheet-like interface, and then converting the analytics into stunning infographics and dashboards. 7_BlueKai 8_Gnip.
Privacy hampers big data development. Business schools’ big data revolution. ©Ferguson In which country would a company with fewer than 50 employees decide to limit its own growth? In France, it would seem, according to research published by a team of London School of Economics professors. Employee protection legislation in France places onerous demands on larger companies and has a direct impact on productivity, say the researchers. Luis Garicano, who led the LSE team, has come up with these figures after analysing data from 67,000 companies over five years.
This kind of “big data” analysis is proving to be one of the trendiest subjects in business schools. From government legislation to personalised medicine to online marketing, big data and data analytics promise to help organisations make better decisions. What exactly is big data? “You’re never going to get a concise, consistent answer,” says Andrew McAfee of MIT Sloan.
Murat Kristal, operations management professor at the Schulich school at York University in Toronto agrees. So how does data analytics work? DARPA Funds Python Big Data Effort - Government - Information. Department of Defense has dished out $3 million for Python big data analytics libraries from a $100 million fund for big data research and development. Military Drones Present And Future: Visual Tour (click image for larger view and for slideshow) The Defense Advanced Research Projects Agency, which is spending $100 million over four years to advance big data technologies, recently awarded $3 million to develop data analytics and data processing libraries for popular computer programming language Python. The funding, awarded to data visualization and analytics company Continuum Analytics, will go toward the development of a scientific computing library for Python called Blaze and a visualization system called Bokeh, Continuum announced in a blog post. [ What else is DARPA up to?
Read DARPA: Your Tech Will Self-Destruct. ] Bokeh, meanwhile, is a Python library for big data visualization, what Continuum terms a "scalable, interactive and easy-to-use visualization system" for big data sets. A More Perfect Union, Part 2. This is Part 2 of our in-depth profile of the big data techniques that gave Barack Obama a second term in office. Read Part 1. The Experiments When Jim Messina arrived in Chicago as Obama’s newly minted campaign manager in January of 2011, he imposed a mandate on his recruits: they were to make decisions based on measurable data. But that didn’t mean quite what it had four years before. But for all its reliance on data, the 2008 Obama campaign had remained insulated from the most important methodological innovation in 21st-century politics. The first Obama campaign used the findings of such tests to tweak call scripts and canvassing protocols, but it never fully embraced the experimental revolution itself.
To that end, he hired the Analyst Institute, a Washington-based consortium founded under the AFL-CIO’s leadership in 2006 to coördinate field research projects across the electioneering left and distribute the findings among allies. Alex Lundry created Mitt Romney’s data science unit. We don’t need more data scientists — just make big data easier to use. Virtually any article today about big data inevitably turns to the notion that the country is suffering from a crucial shortage of data scientists. A much-talked-about 2011 McKinsey & Co. survey pointed out that many organizations lack both the skilled personnel needed to mine big data for insights and the structures and incentives required to use big data to make informed decisions and act on them. What seems to be missing from all of these discussions, though, is a dialogue about how to steer around this bottleneck and make big data directly accessible to business leaders.
We have done it before in the software industry, and we can do it again. To accomplish this goal, it’s helpful to understand the data scientist’s role in big data. Currently, big data is a melting pot of distributed data architectures and tools like Hadoop, NoSQL, Hive and R. While difficult to generalize, there are three main roles served by the data scientist: data architecture, machine learning, and analytics. Big Data Is on the Rise, Bringing Big Questions - Tech Europe.
OXFORD—The next Next Big Thing is Big Data. Evangelists claim it has the power to reveal hidden truths about our companies, about our lives, about society as a whole. So important is it that last week’s Silicon Valley Comes to Oxford annual event was built around the topic. Inevitably the real world crashes into digital utopia. According to Peter Tufano, the dean of Oxford’s Said Business School, which played host to the event, while awareness of the topic was high among enterprises, only about 6% of companies have got beyond a pilot stage, and 18% are still in one.
“That means three-quarters of industries are looking at this and saying ‘what is this all about?’” Why aren’t they looking at Big Data? “The answer across all business,” he said, “was ‘we don’t know what the business case is.’” But according to speakers at the event, the business case has already been answered. Mr. According to Mr. Stephen Sorkin, vice president of Engineering for the U.S. Mr. Mr. From Big Data Science to Big Data Action. From the dawn of civilisation through to the year 2003, Google calculates that humans have produced 5 exabytes of data. That’s a lot of stone tablets. But with the explosion of mobile devices, 3G and 4G networks and social networks, we now produce 5 exabytes of data every two days. That means that every photo you upload to Flickr or Facebook, every video you share with friends on YouTube or Vimeo and every one of the billions of tweets broadcast on Twitter is contributing to the avalanche of data.
But add to this the fact that each of these items comes with contextual data. At the same time that you update your profile or publish a photo, you may also be sharing your geolocation, your likes and preferences, your upstream and downstream behaviours, and your attitude to topics (based on sentiment). And this is just the tip of the big data iceberg. The rise of big data is a blessing and a curse for CMOs Marketers don’t need data they need action. How Important Each Day of the Year Is, Visualized. How Big Data Sparks Creativity And Culture In Startup Land. Where are big data and BI heading in 2013? - in-memory processing, big data, technology trends 2013, cloud BI, business intelligence.
Digesting Big Data [Egestion vs. Excretion] Big Data Spending Will Reach $20 Billion by 2016. Structured vs. Unstructured Data | Fed IQ. Big Data: Big Brother Business on Steroids? Visual Analytics Brings Big Data in Google's Cloud to Life | Innovation Insights. Big Data’s Importance to Business Marketing. Photos du journal. Mobile Consumer Spending: Big Data Insights for Holiday Retailers. Big Data: The Hidden Opportunity. YouTube. Failure to Manage the Three Vs Is Not an Option - Robert Plant. Deja VVVu: Others Claiming Gartner’s Construct for Big Data. Personal data transfers: Google Cloud SQL service offers EU-only processing and storage capability. A step closer to cleaning up the mobile cloud mess | Mobile Technology.
The Principles of Gray-Box Testing. Chartio launches its ‘beautiful intelligence’ tool to make data easier on the eye (and wallet) Study: 39% Of Google Search Referrers Now "Not Provided" The Top 5 Keyword Mistakes Regarding Your Search Engine Optimization. Multi-cloud 101: Tips for navigating public, private, and hybrid clouds.
3rd Workshop on Ontology Patterns – WOP2012 (Proceedings at CEUR) Gartner tips rise of the chief digital officer - Strategy. SAP Takes Big Step Putting CRM On Hana - Software - Enterprise. Google opens up on seven years of its data center history — Cloud Computing News. 3. Come for the data. Stay for the insight. Data Nerd's Corner. Big Data - You Can Start Small. Big Data’s Big Moment. Big Data: A Pile of Nuts. GoGrid Partners with Boston Big Data Research Group hack/reduce With Free Cloud Hosting. Headline Story | equities.com. Is Technology Making You More (or Less) of a Jerk? - Michael Schrage. Big Data Means Big Bucks for Small Business.
Analytics – What is Smarter Analytics. MicroStrategy Uses Cloud to Deliver Self-serve Business Intelligence. 5 trends that are changing how we do big data — Data | GigaOM. The foundations of a new personal data ecosystem. Using data to create successful strategies. Brands have too much data and not enough talent. Big Data Leading to New Breed of Service Provider CIO. Big Data in Law: Cloud Challenge, Analytics Opportunity. MDM: It’s Not about One Version of the Truth. Humanizing Big Data. Aussie small businesses waking up to big data: SAS. Adopters, Not Vendors, Stand to Gain the Most from Big Data. Insight: Crunching the numbers to boost odds against cancer.
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