Data Analytics Makes the Transition From Novelty to Commodity  

As analytics become readily available to all organizations, they will take on the characteristics of a commodity.

It’ll become a commodity like a carpenter’s tools are a commodity. A good electric drill used to cost hundreds of dollars. You can pick one up for less than $50 today.

However, buying one doesn’t make you a master carpenter or mean that you know how to build a house. The constraint isn’t the technology, it’s the blend of business, technical, communication, and leadership skills. And unlike technology, those aren’t infinitely reproducible.

It’s the visionary that creates the future, not the database you bought.

Google finds there a need for speed in its driverless cars  

Google has programmed its driverless cars to break speed limits by up to 16kmph because it believes that allowing them to do so will improve road safety.

Between this and AirBnB / Uber’s launch before getting regulatory approvals in all market, does anyone else get the feeling that legal compliance is just becoming something that individuals, not corporations need to worry about?

What’s even more bizarre is that one could even argue that it’s a form of positive civil or economic disobedience.

Has Innovation Become A Four-Letter Word?  

Innovation overpromised and underdelivered. It became a “flavor of the month” that captivated employees’ attention and energy only to leave them feeling disillusioned and contemptuous of the “next big thing” dreamed up by the senior executive team.

TDLR: If it isn’t part of your fabric, it’s just something else that’ll end up getting ignored.

Could This ‘Data Lake’ Concept From GE Revolutionize Energy Analytics?  

A data lake, enabled by the open-source software Hadoop, is simply a collection of information in its raw format. Rather than process the data and file it away in a rigid way, GE and Pivotal are storing it in its original form and sifting through it when needed.

I’m not yet sure we need another bit of jargon but the principle’s a good one. You often don’t know what you need until you look for it and find it’s gone.

For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights  

Yet far too much handcrafted work — what data scientists call “data wrangling,” “data munging” and “data janitor work” — is still required. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.

Not much new on that front – analysts have always spent around 50% to 80% of their time managing data. What is new is the recognition that it doesn’t need to be that way.

That’s one of the reasons why the growing discussion about Chief Analytics Officers / Chief Data Officers is so interesting; if information’s really a differentiator, it needs executive representation. Anything less means it gets bogged down in operational noise.

Google Knows You Better Than You Know Yourself  

Now’s tracking runs headlong into our need to lie—a little! sometimes!—to ourselves. It’s a truism of this era that Facebook statusing and avatar design and Instagram filters have transformed how we self-present: the way we tell other people true and untrue stories about who we are. What’s transformative about Now is how it makes it harder to tell such stories to ourselves.

Also why it’s so hard for many organisations to adopt analytics. What do you do when your work tells you that everything you thought was true was wrong? Cognitive dissonance isn’t an easy thing to handle.

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