Alistair Croll

Alistair has been an entrepreneur, author, and public speaker for nearly 20 years. He’s worked on a variety of topics, from web performance, to big data, to cloud computing, to startups, in that time. In 2001, he co-founded web performance startup Coradiant (acquired by BMC in 2011), and since that time has also launched Rednod, CloudOps, Bitcurrent, Year One Labs, the Bitnorth conference, the International Startup Festival and several other early-stage companies. Alistair is the chair of O’Reilly’s Strata conference, Techweb's Cloud Connect, and the International Startup Festival. Lean Analytics is his fourth book on analytics, technology, and entrepreneurship. He lives in Montreal, Canada and tries to mitigate chronic ADD by writing about far too many things at Solve For Interesting.

Three kinds of big data

Looking ahead at big data's role in enterprise business intelligence, civil engineering, and customer relationship optimization.

Photo of the columns of Castor and Pollux by OliverN5 on FlickrIn the past couple of years, marketers and pundits have spent a lot of time labeling everything “big data.” The reasoning goes something like this:

  • Everything is on the Internet.
  • The Internet has a lot of data.
  • Therefore, everything is big data.

When you have a hammer, everything looks like a nail. When you have a Hadoop deployment, everything looks like big data. And if you’re trying to cloak your company in the mantle of a burgeoning industry, big data will do just fine. But seeing big data everywhere is a sure way to hasten the inevitable fall from the peak of high expectations to the trough of disillusionment.

We saw this with cloud computing. From early idealists saying everything would live in a magical, limitless, free data center to today’s pragmatism about virtualization and infrastructure, we soon took off our rose-colored glasses and put on welding goggles so we could actually build stuff.

So where will big data go to grow up?

Once we get over ourselves and start rolling up our sleeves, I think big data will fall into three major buckets: Enterprise BI, Civil Engineering, and Customer Relationship Optimization. This is where we’ll see most IT spending, most government oversight, and most early adoption in the next few years. Read more…

Comments: 10 |
Survey results: How businesses are adopting and dealing with data

Survey results: How businesses are adopting and dealing with data

A glimpse into enterprise use of big data.

Feedback from a recent Strata Online Conference suggests there's a large demand for clear information on what big data is and how it will change business.

Comments: 3 |
The feedback economy

The feedback economy

Companies that employ data feedback loops are poised to dominate their industries.

We're moving beyond an information economy. The efficiencies and optimizations that come from constant and iterative feedback will soon become the norm for businesses and governments.

Comments: 13 |
Cooking the data

Cooking the data

In a world of full disclosure, cooking the data is the new cooking the books.

Open data and transparency aren't enough: we need True Data, not Big Data, as well as regulators and lawmakers willing to act on it.

Comments: 3 |

There’s no such thing as big data

Even if you have petabyes of data, you still need to know how to ask the right questions to apply it.

Today's big companies are losing to small upstarts simply because those firms ask better questions. To compete, large enterprises need to learn how to harvest the data they have on customers, markets, competitors, and products.

Comments: 23 |
Everyone loves a science fair

Everyone loves a science fair

Get your submission in for the Strata Conference Science Fair by January 14.

Strata's science fair will showcase the creative edges of big data. If you have an interesting tool or technology to show — the more beta, the better — let us know.

Comments Off |
Big business for big data

Big business for big data

What IBM's acquisition of Netezza means for enterprises.

Netezza sprinkled an appliance philosophy over a complex suite of technologies, making it easier for enterprises to get started. But the real reason for IBM's offer was that the company reset the price/performance equation for enterprise data analysis.

Comments: 5 |