Dai Clegg
VP product marketing , Acunu
Follow me on Twitter, LinkedIn, Google+

Dai is a multi-decade veteran of the tech industry. He has spent time at, IBM, Netezza, Oracle and others. His roles have spanned the software product lifecycle through product developer, product manager and product marketer, evangelist and executive marketer.

With a long background in data he is now most interested in big data and particularly all the new technologies springing up to make big data more than just an unmaterialized promise or, worse, merely a hyperbolic bubble.

Big data is growing up

May 8, 2014
I recently started researching a longer piece I’m writing about big data architectures, and I realized something: big data is growing up. It has moved rapidly from a collection of critical, but over-hyped technologies disrupting core relational database market segments to an emerging eco-system to revolutionize data management and analytics. Read More

A revolution in the visualization of data

March 3, 2014
We're collecting more data, from more sources, in more formats. The opportunities to derive insight from all these new sources have exploded, but the essential point is not just to discover, identify, categorize and summarize the data—it is also important to communicate it, and that so often means visualization. Read More

The 'Museum Clickers' of Big Data

December 3, 2012
When thinking of use cases for big data analytics, consider your need for immediacy. Do you have the need to know now, not just the ability to know now? In other words, would you do something differently at that moment if you knew the answer immediately? Read More

Real-time Analytics - Low Latency and High Velocity

October 5, 2012
Well there’s real-time, then there’s real-time, then there’s real-time.  As so often with me, this post was first drafted on a plane, and when it comes to in-flight technology, real-time means very real-time. Read More

Crowdsourcing Your Way to Big Data Value

August 21, 2012
Recently, I was in Nice for a three-day gathering of 150 European IBM Big Data specialists. Looking around the room at the opening plenary made me think how fast the world of big data is moving and how quickly our community is growing. Read More

Semi-Structured Data Analytics: Relational or Hadoop Platform? Part 2

August 11, 2012
For some vendors, the only use case for unstructured data is to turn it into structured data to analyze it in a relational database. Read More

Semi-Structured data analytics: Relational or Hadoop platform? Part 1

June 26, 2012
What exactly is 'semi-structured' data? How is it different from relational data? And what about 'structured, but not relational' data? Dai Clegg explains the intricacies of semi-structured data and how it fits into relaitonal or Hadoop platforms. Using an example of a telco seeking affinity analysis, find out how to leverage semi-structured data.  Read More

Exploring Uncharted Data: Is there any insight out there?

June 8, 2012
The biggest table in any Netezza database that I know of has over 600 billion rows!! That’s the claim made by our customer, Catalina Marketing. So although most of the data in the world is not relational, there is a huge amount of relational data and IBM technologies are more than capable of performing the most complex analytics on it. Netezza has extensive libraries of in-database analytic functions1 to support SPSS, SAS, R and other analytic tools and languages.  And the special capability that Netezza has to deal with ad-hoc queries means that if your data is relational, or can be mapped to a relational schema conclusively, like the CDRs I wrote about in a previous post, it is a great platform for analytics.  If!   Read More

Big Data: The Data Velocity Discussion

May 15, 2012
If there’s more and more data arriving and time isn’t expandingi, then data must be arriving at greater and greater velocity. In my last post I talked about Variety in the Volume, Variety, Velocity triumvirate. There’s more to be said about that, but first I’d like to take a run at Velocity. We’ve got used to the idea that you load stuff into a database (or other data store) then you take a look at it. That’s just too slow for lots of operational decision making processes.  And if you think about it, as the volume of data available increases the bar is constantly rising on real-time analysis. But for many kinds of decisions, you just need the data that comes with the event you want to decide about: is this a fraudulent transaction? Was this call dropped?   Read More

Big Data: The Data Variety Discussion

May 1, 2012
We'll start from the very beginning.  It's a very good place to start... Big data is all about Velocity, Variety and Volume, and the greatest of these is Variety. At least it causes the greatest misunderstanding. Read More