As the Internet of Things (IoT) hits full throttle, it’s easy to talk about how new technologies are improving every day life. For years people have imagined a world where smart machines understand what’s happening around them in order to better serve us – from Rosie, the Jetson’s maid, to WALL-E, the small waste-collecting robot. According to recent estimates from Goldman Sachs, there will be 28 billion devices connected to the Internet by 2020, and it will be larger than the PC, tablet and smartphone markets combined.

However, what is often overlooked is that it’s not the devices per se that are causing this improvement. The reality is that the development of more devices is just further moving us into the Digital Age. Motion sensors, GPS watches, Siri and Amazon Dash are all transitioning us into an era where data is king. But these devices are just the mode in which data is collected. The real value is the ability to analyze this growing amount of data and gain insights that will impact individual lives and businesses.

The declining cost of sensors, ubiquitous connectivity and new ways to empower business users is quickly pushing along the next IoT wave. This new wave will open up opportunities for leading-edge companies to improve efficiency, launch new products and innovate. Already, companies riding the IoT wave are analyzing the growing masses of unstructured and structured data and broadening access to these insights to non-IT users. With data generated from Web logs, robots, oil wells, cell towers, servers, mobile devices and products, companies are leveraging IoT and big data analytics to improve business, increase revenues and better serve people.

Tapping into User Data to Reduce Energy Consumption

Connected homes are the top consumer market for the IoT. With access to data from meters, demographics and energy consumption, smart-meter companies have helped customers adjust their habits to reduce energy use and save money. We’ve seen one smart-meter company help its customers save up to $500 million in energy spending. With data analytics, forward-thinking energy management companies are able to run analyses on consumer thermostat data to better understand energy usage patterns. By deriving accurate and unique insights from multiple data sources, product managers can help consumers reduce energy consumption, saving both money and resources.

Driving New Athletic Advantages

Over the past few years, wearable technologies have slowly gathered more speed in transforming mainstream conceptions of health and fitness. Activity trackers, smart phone applications and smart scales have all contributed to a self-monitoring and data-collecting phenomenon dubbed the “Quantified Self” movement. The 2012 U.S. Women’s cycling team found their competitive advantage by recording and analyzing their physiological and psychological data. As a result, they went from a five-second deficit at the world championships to earning a Silver medal in the 2012 London Olympics by 8/100th of a second — a triumphant feat that was achieved not only through dedication and athletic ability, but also through enhancing training with insights gained from analyzing big data.

Bringing Sensor Data to Industry Experts to Improve Operations

The IoT is making its way deep into the earth with well sensors. An energy company is collecting data about the average production of oil, gas, and water from each of its wells and combining it with historical well performance and geospatial data to look at efficiencies and deficiencies based on location and equipment. Extending this knowledge to non-IT users, like production engineers, the company was able to lower operational spending per oil field and realize $126 million per year in incremental revenue.

Using Data-Driven Knowledge to Boost Customer Support

The IoT is also creating the opportunity for new revenue services. One enterprise hardware company combined data generated from server logs, product catalogs and customers to offer predictive maintenance and premium support. With this knowledge, support teams were able to send out replacement parts before components actually failed, and sales teams were able to look at usage patterns to improve forecasting and renewal negotiations. Combing data and analytics equipped the company with crucial insights to deliver unparalleled customer service.

With the help of data analytics, a world where intelligent machines make lives easier is not such a far-fetched idea after all. We’re seeing it emerge in the form of smart meters and sensors, and we’re already witnessing businesses reap the benefits of this new world order. Companies that capitalize on the convergence of data analytics and the IoT will undoubtedly blaze the way in their industries with unmatched innovation and customer success. As competition builds and business leaders look to new ways to deliver top-notch services, build innovative products, reduce system downtime, increase customer engagement or boost production, it would be wise to consider where you can leverage the combination of data analytics and the IoT to drive results.

Datameer‘s infographic provides an excellent overview of how businesses can make sense of the vast IoT data, to build top-notch products that will greatly influence the way we live.

About Stefan Groschup, CEO & Co-Founder of Datameer – Stefan Groschupf is co-founder and CEO of Datameer, a provider of big-data analytics. A big-data veteran and serial entrepreneur with roots in the open-source community, Groschupf was one of the early contributors to Nutch, the open-source project that spun off Hadoop.

Image Credit: Mike / Activate The World / CC BY 2.0

Choosing the right language for data analysis can be almost as complicated as actually learning the language. For many reasons, R and Python are two of the most popular: R is often praised for its great features for data visualization, as it was developed with statisticians in mind; plenty of programmers love multi-purpose Python for its so-simple-a-child-could-do-it syntax.

Why not just learn both?

The fact is, your time is limited. As data scientist and Dataconomy contributor Joshua Ebner says: ‘Learning a new programming language is a large investment in your time, so you need to be strategic about which one you select. The reason to focus on one programming language is because you need to focus much more on process and technique, not syntax. You need to learn how to think about data and how to solve problems using the tools of data science’.

How do these two languages relate to one another? What are the strengths of R over Python, and vice versa? Just like there’s no single best tool in a toolbox, there’s no single programming language that’s perfect for every data problem you want to solve. However, you need to be able to devote a significant amount of your time to truly master one tool. Spending 100 hours on Python or on R will yield considerably better results than splitting your time on ten different tools. In the end, your time ROI will be higher by concentrating your efforts.

The Data Science Wars

Data science online learning platform DataCamp‘s infographic provides a basic comparison between these two programming languages from a data science and statistics perspective, perfect for aspiring data scientists looking for the right language to start with.

And The Winner Is…

Even though the infographic suggests R and Python are equally good for budding data scientists making their first steps on the field, we believe R is the winner, at least for data science beginners, who are moving on from spreadsheets into programming languages. It is not only the most widely used language among data scientists, but it is also popular in academia, and in business. R also offers a simple approach to learning the key skills of data science: data manipulation, data visualization, and machine learning. After mastering the fundamentals data science in R, you’ll probably (want to) learn other languages to solve specific problems.

(Image credit: Michael Doherty)

Visual.ly, the data visualization and infographics platform that provides on-demand creative services team to companies from its curated freelance workforce, has secured $3.3 million in fresh funding to further fuel the development of its platform.

“The rise of content marketing has generated an unprecedented demand for compelling visual content to address an increasing number of business goals,” notes Matt Cooper, the new CEO of Visually. “As brands struggle to keep pace, Visually serves as a trusted partner, helping brands, publishers, and agencies scale in the short-term or as an ongoing extension of their in-house creative teams.”

Visually intends to invest further in content marketing development and distribution tools having witnessed an 80% growth in average spend per client, through the last 12 months.

Over the past 18 months, Visually has gained recognition in branded content development in the last 18 months and has added to its portfolio a full suite of content solutions for marketers, content strategists, and PR professionals, including branded videos, interactive web experiences, whitepapers and short-form micro-content optimized for distribution on social media channels.

“Visually is changing the way brands source creative services and content,” Cooper added. “Collaborative marketplaces are the future of the service industry, and Visually is the perfect complement to the traditional agency model, providing cost-effective, predictably high-quality content quickly and at scale.”

Brands such as AOL, Red Bull, Twitter, NBC, and P&G are among the 1,300 plus brands that use Visually.

The additional funding was led by Crosslink with investments from 500 Startups, Mitch Kapor Foundation and SoftTech VC. Matt Cooper, of oDesk, has been hired as CEO and Board Member while  Silicon Valley veteran and investor Mark Goines as Chairman of the Board. This investment raised the total funding to date to $15.4M.

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(Image credit: Visual.ly)

2014 has been a huge year in big data- and big data publishing. Viktor Mayer-Schoenberger and Kenneth Cukier re-published and added an extra chapter to their bestselling “Big Data”; Nate Silver graced the publishing world with his presence once more with the Best American Infographics of 2014. We’ve compiled a list of the most insightful, beautiful, thought-provoking and challenging books on big data this year. Whether you’re a casual data enthusiast or a hardcore statistician, you’re sure to find a book among our selections to add to your Christmas Wishlist.

1. Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schoenberger & Kenneth Cukier
A collaborative project by Viktor Mayer-Schönberger of the Oxford Internet Institute and Kenneth Cukier of The Economist, “Big Data…” explores how the data explosion is touching every facet of our lives. Protecting us from future diseases and exploding manhole covers, overhauling our retail experiences and transforming every industry, it’s indisputable that the big data revolution is colouring how we experience the world. This book- re-released in paperback this year with an additional chapter- may be the defining guide to big data for uninitiated.
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2. The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits by Russell Glass & Sean Callahan
A complaint we hear time and time again is that companies have plentiful data, but no idea how to use it. Contrary to popular belief, hiring a data scientist to crunch some numbers is far from the most effective strategy; industry leaders should be integrating a data-driven approach into every aspect of their company culture. “The Data Driven Business” is an excellent tool for helping to implement such strategies in organisations of any size. Filled with examples of how businesses are using the data to outshine the competition- and cautionary tales about ignoring the insights at your fingertips- this book is a must-have guide for those looking to infuse data into their business practices.
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3. Advances in Complex Data Modeling and Computational Methods in Statistics by Anna Maria Paganoni and Piercesare Secchi
Truly a statistician’s Bible. A quick glance at the contents page demonstrates that this book offers a comprehensive insight into some of the most widely-used and valuable methods in computational statistics today. It includes:
“Statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration.”
If that list has inspired more intrigue in you than confusion or fear, buy it now. It will become your most treasured possession.
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4. Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault by WH Inmon and Dan Linstedt
New database technologies & data science tools offer data analysis at unprecedented speed and scale; but for most businesses, it’s neither feasible nor desirable to do away with the existing architecture altogether. This technical and insightful read offers a guide into an often-overlooked area of the data scientist’s workflow; how to integrate new big data tools into existing IT architectures. Author Bill Inmon- the man who defined data warehousing, and was the first to offer data warehousing classes to the world- proves to be a knowledgeable guide on infrastructure old and new.
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5. Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani et al.
Big data the whole company get on board with has certainly been a trend in big data publishing this year, and this book is one of the best. This book- written by a whole troupe of data presentation specialists- walks you through the best practices for data visualisation, communication and presentation. It helps you turn you data into comprehensible and engaging insights, so that your whole organisation can understand and act on the information at hand. The roadmap between data and decision making is often fraught with peril- this book will definitely help you down the path.
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6. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners by Jared Dean
It’s indisputable that big data can create modern companies a huge amount of value- but, as the blurb of this book acknowledges- “having the data and the computational power to process it isn’t nearly enough to produce meaningful results”. This book is aimed at both data science practitioners and business leaders alike, helping both parties to harness data science and reap bottom-line results. This book walks you through many of the key developments in big data technology today- from MPP to in-memory procession, from text mining to machine learning algorithms- and could prove an invaluable resource for industry professionals and data scientists alike.
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7. Smart Cities: Big Data, Civic Hackers, and the Quest for the New Utopia by Anthony M. Townsend
It’s becoming increasing clear that we’re living connected, data-infused lives. Perhaps this is most apparent in how our lives as urban citizens are being affected- from the sewage systems to transport, from our stores to wifi access. This book is a fascinating glimpse into how our cities are getting “smart”, drawing on examples from all over the world. An insightful read for urban planners, tech enthusiasts, entrepreneurs, and any city-dweller interested in discovering how smart cities shape how we live, work and see the world.
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8. Big Data Now: 2014 Edition by Raymond I Morrison
Every about this book- right down to the front cover- serves as a reminder of just how far we’ve come today. Data warehousing and business intelligence were once considered revolutionary- now, constant monitoring of performance via sensor data, the explosion of the internet and the rise of social media & connected devices have blown such developments out the water. The book serves as a wonderful account of the dizzying heights big data has scaled up to the present moment.
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9. The Best American Infographics 2014, by Nate Silver and Gareth Cook
In what is undoubtedly the most visually pleasing entry on this list Gareth Cook & big data wunderkind Nate Silver explore the year’s best infographics. The infographic has risen to prominence as the medium for making sense of the data deluge, and looking through this beautiful compendium, it’s easy to see why. Ranging topics such as population and demographics to wine pairings, this book is the perfect gift for any aesthetes or data enthusiasts.
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10. Data Science at the Command Line: Facing the Future with Time-Tested Tools by Jeroen Janssens
Janssens, a senior data scientist at YPlan, is on a mission to make the lives of data scientists everywhere easier. This book demonstrates how to the harness the power of the command line, using shell commands and short scripts to join up various tools at your disposal. This book makes a compelling argument as to why the command line is an “agile, scalable, and extensible technology”- and although it might be for everyone, this book could just help you to improve your data science workflow.
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11. Big Data, Big Analytics: Emerging Business Intelligence by Dio L Herben
Named as one of our Top Acquisition Trends for 2014, Business Intelligence is certainly still a massively valuable and relevant space. But BI is not the same as it used to be- it’s moved away from merely retrospective analysis of historic data, and towards real-time, operational analytics- and even predictive analytics which forecast future performance. This book is an invaluable read for anyone in the BI sphere looking to discover how big data has transformed BI, and keep abreast of the latest trends in a field which continues to adapt and innovate.
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12. Trendology: Building an Advantage through Data-Driven Real-Time Marketing by Chris Kerns
As you might expect from a book by marketers for marketers, everything about this book is attention grabbing. The cover, the contents- even the blurb:
Should an airline be talking about the royal baby? What’s a candy bar doing Tweeting about a soccer match? Since when does laundry detergent weigh in on TV shows? Those conversations seem crazy, right? They’re mismatched, they’re nonsense…and they are working.
This book takes a data-driven approach to examining the real-time marketing strategies of some of the world’s biggest brands on Twitter, including Disney, MTV, Starbucks, Coca-Cola, BMW, J.C. Penney, Nike, Sony, IKEA. It uncovers what has made these brands into the social behemoths they are today- and proves to be an insightful guide into how just about any business can emulate some of their success.
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13. Practical Data Science Cookbook, by Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, and Abhijit Dasgupta
Our favourite book published this year for the aspiring data scientist. Rather than offering a mere glossary of technologies, and no insight into the day-to-day work and best practices of a data scientist, this book delves into what a data scientist actually does. Filled with data science projects, pipelines and programming challenges in R and Python, this book is fantastic starting point for anyone looking into the fast-growing and fascinating field of data science.
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14. Data Science and Big Data Analytics by EMC Education Services
The last entry on our list is somewhat of a cheat, as it’s yet to be publically released, but we have high hopes for EMC’s training manual for budding data scientists. Covering how to contribute to a data science team, what a data science lifecycle looks like and the key techniques you may need to use, this is a great resource for aspiring data scientists. It’s also released the day before Valentine’s Day- why not order it for your data-obssessed significant other? We can’t think of a more romantic gift.
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Image Credit: DeVry University

Image Credit: Capgemini

Image Credit: Deloitte 

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If you’re not sick of World Cup fever just yet, we have a few World Cup visualisations to brighten up your weekend.

The first is a visualisation showing how World Cup teams are drawn from league players around the world. Although you have to be a national citizen to play for on a particular country’s team in the World Cup, you don’t have to be a citizen to play in their leagues. This visualisation by Guy Abel, a statistician and R programmer at the Vienna Institute of Demography, shows where members of the World Cup team usually play around the world. The arrows flow from the World Cup teams to the players’ regular league teams. Arrows folding back on themselves denote players who play in league teams from their own country (check out Russia and Italy, for example). The visualisation was created in R using the Circlize package and data scraped from Wikipedia; you can access the code for this plot on Github.

The second is a much less data-heavy visual history of the final stadiums of the World Cup (click here to enlarge). Grass Form, the creators of this infographic, had this to say:

Of course part of the World Cup legend are the iconic stadia; from the timeless twin towers of Wembley to the newly-revamped Maracanã which will take pride of place at this year’s tournament, these coliseums have provided the platforms for the most iconic moments in the history of the game.

Interestingly, the infographic shows the capacity of each stadium too. The Estadio Do Maracana, the home of the World Cup this year, hosts 96,000 people; compare that to Stade de Olympique de Colombes, home of the 1938 World Cup which seated a paltry 14,000 people.

If this article hasn’t sated your appetite for all things World Cup, you should check out our posts on using data to predict the World Cup winner, and how the German team are using big data to gain the competitive edge.

(Sources: Cool Infographics, Revolution Analytics)