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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

Or, to be more precise, a pair of jeans on you!

girl in jeans.jpgKatia Moskvitch, writing for the BBC News website last week caught my attention with this question: "What if those new jeans you've just bought start tweeting about your location as you cross London Bridge?"  Those of us who've been following the uptake of RFID technology and the big data surge know that she's stretching the point a bit--RFID devices don't yet tweet and the chances of meeting a wild RFID reader on London Bridge is still low probability.  But we also know that she's close enough to the coming reality that many marketers and advertisers are beginning to envisage.  And make lots of money from...

The Internet of Things (IoT) is already becoming a reality as far as machines goes.  Smartphones, tablets and laptops lead the way, of course.  But automobiles and buildings, fridges and washing machines are not far behind.  And the ultimate vision is that every item of any value can be tagged with an RFID device and tracked wherever a reader exists.  Moskvitch quotes Gerald Santucci, head of the networked enterprise and RFID unit at the European Commission: "The IoT challenge is likely to grow both in scale and complexity as seven billion humans are expected to coexist with 70 billion machines and perhaps 70,000 billion 'smart things'".

From a BI point of view, that adds up to big data--very big data.  It also points to a type of data to which we've had only limited exposure in BI in the past.  The data generated from the IoT can be classified as (potentially) high-volume, raw micro-event data keyed by location, time and device ID.  Beyond its volume, such data poses interesting issues for traditional BI thinking.  

While BI implementations have typically invested much time and effort in cleansing data on loading, this raw IoT data is likely to come largely directly from the machine sources to the (big data) BI environment, rather than through operational systems that create a context for data gathered in traditional business operations.  And while current BI systems do deal with machine-generated data from devices such as ATMs, manufacturing machines and telephone exchanges, for example, these sources are highly controlled, internally managed, fixed and relatively few in number in comparison to IoT sources.  IoT data will require very different modeling and analysis approaches to today's BI.

But perhaps the most interesting dilemma is presented by the fact that we will be dealing directly with devices rather than people, which is really what interests marketing.  Yes, we will receive lots of information about where and when, but the question of who will be a matter of extrapolation.  Apart from fraud and crime, of which there will be myriad opportunities, the fact is that, other than implanted devices, the relationship between a device and a person is loose and variable.  To return to that RFID tag in the young lady's jeans above, linked via a credit card to a particular person at time of purchase, we can instantly see at least a dozen ways in which we could misidentify the person whose behavior we think we're tracking.  Even working at a statistical level, there may be issues.

And then there are the privacy issues that arise.  I'll return to that topic in another post.

Posted September 29, 2011 4:39 AM
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1 Comment

Thanks Barry. Comletely agree on the building momentum of Big Data and how companies that don't exist today will be huge players in the coming years.

Just tweeted this as well.


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