You might have heard recently about a study finding that liking “curly fries” on Facebook correlates strongly with high intelligence. Publications such as Wired have written about it. Quid Founder and CEO Sean Gourley cited it during a presentation at Structure: Data last week. A faction of the European Union parliament even pointed to the study as yet another reason to prohibit data mining by web companies.
However, if you’re like me, hearing anybody repeat that curly fries data point as fact likely sends shiver down your spine. It’s not that it’s not true — it very well might be — but that it’s nearly useless information without more background.
That’s right, the old correlation versus causation argument is front and center once again. In all the big data world, it’s probably the biggest fallacy there is, no matter how you look at it. No, getting value from…
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Improving energy efficiency is the no-brainer, no loser environmental policy. By limiting wasted power, we reduce the number of power plants we need—and their consequent pollution—and we save money. It shouldn’t be surprising that when President Barack Obama went looking for a green policy that the entire nation could agree with during his state of the union speech, he settled on energy efficiency, challenging Americans to “cut half the energy waste by our homes and businesses over the next 20 years.”
Cutting energy waste is a matter of better lights and better insulation, better heaters and better air conditioners. But first and foremost it’s a data challenge. You can’t cut waste until you know what you’re wasting—and most of us have only the slightest idea of the energy we’re using at home. (Even big electricity users in business often aren’t much better—or need to employ human managers to monitor that…
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Analytics have exploded into prominence in the past 15 months. What was once a mysterious statistical discipline understood by few has been elevated as the enabling technology that allows companies to unlock the potential of Big Data. Big Data was everywhere in 2012. There was a track devoted to it at the World Economic Forum in January, 2012. In October, the Harvard Business Review had a cover section on Big Data which characterized analytics as sexy and dubbed its leading practitioners Data Scientists. And at Ketchum, we made analytics training mandatory for ALL employees in 2012, a first for our industry.
The digitization of all forms of analog data is at the heart of the Big Data explosion. From our click behavior online to purchases we make with a loyalty card to places we go in our vehicles, everything is captured as digital data potentially available for analysis. And with…
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Big Data is finding a role in managing business performance, as operational analytics combine with new big data technologies to get better insights, fast enough to make better decisions. Automated control systems driven by digital data and dashboard decision-making based on consumer-generated data can be used to keep organisations ahead of the game. The importance of “fresh” data – data that is reflected in decisions within a second – is now leading to new ways of measuring and managing business performance.
How successful organisations are using new tools and technologies to manage and improve performance is presented in the world’s largest study into business performance, which was completed recently. This study is based on insights from over 3,000 companies from across the globe and provides an overview of the current state of play, such as what tools are being used. The author of the study, Bernard Marr of API…
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Finding data scientists and other highly technical resources that understand the complexity of big data is one of the most common roadblocks to getting value from big data. Typically, these resources need to understand Hadoop and new programming methods to read, manipulate and model big data.
As big data analytics tools advance, addressing these technologies will become less difficult, so big data scientists must master additional skills.
To make a real business impact, data scientists must have:
1. Innate analytical skills
They must have a natural curiosity for experimenting with data and often begin analysis without a clear picture of the end goal. This is a different paradigm than solving a specific, identified problem through coding or by running a query.
2. Business finesse
Sexy dashboards ultimately fail if a business doesn’t act on what the data is indicating. To succeed, data scientists must know how to translate the impact…
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If the analytics team wrestles with getting access to data, how timely are the insights?
To address the question…Global CIO are shifting their strategy — “need to build data-as-a-service offering for my data” to enable the analytics users in the organization. The more advanced CIOs are asking – “how should I build data science capabilities as a shared foundation service?”
The CIO challenge is not trivial. Successful organizations today operate within application and data eco-systems which extend across front-to-back functions (sales & marketing all the way to fulfillment and service) and well beyond their own boundaries. They must connect digitally to their suppliers, partners, distributors, resellers, regulators and customers. Each of these have their “data fabrics” and applications which were never designed to connect, so with all the data-as-a-service and big data rhetoric, the application development community being asked to “work magic” in bringing them together.
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