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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!

September 2011 Archives

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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Lyzasoft's Scott Davis returned to the Boulder BI Brain Trust (BBBT) last Friday with Lyza version 3.0.  Here's my key takeaway.  Lyza is the first product I've seen that truly understands and delivers the type of collaborative environment needed for innovative and effective decision making.

Please read that last sentence again--the type of collaborative environment needed for innovative and effective decision making.  Note that I didn't say "collaborative Business Intelligence".  I believe that phrase can be a bit misleading, especially to BI people.  And Lyzasoft gets that.  Let me explain...

If you track back to the initial release of Lyza, the product focused on supporting the often iterative data analysis process that business analysts go through in order to reach conclusions and come to decisions.  This is a process that typically happens in the spreadsheet environment, because of the ease of trying, sharing and redoing it offers.  Lyza offered an environment that enabled better control and management of that environment.  And, most importantly, they began to build around that BI tool a collaborative environment where analyses and results could be shared and reused.  For more details, see "Playmarts: Agility with Control--Reconnecting Business Analysts to the Data Warehouse" and "Collaborative Analytics--Sharing and Harvesting Analytic Insights across the Business", two white papers I wrote in late 2008 and mid-2009.

Now fast-forward to last week and version 3.  What Scott demonstrated at the BBBT was entirely about collaboration.  The analytics tool that was Lyza version 1 was still there, but it had become simply one of any number of tools that a decision maker might use.  The focus of the new release is now firmly, and perhaps entirely, on supporting the collaborative process around decision making.  Rather than emphasizing the data, Lyza 3 seeks the intersection between people, their activities and the artifacts they create, use and share.  This emphasis on people, activities and things is not new in itself; what is new is the intuitive linkage between them and the focus on decision making and action taking that comes from prior Lyza BI tooling.  What we have here is what a decision support system really should look like--it's about supporting decision making, doh!

If we can look beyond the current hype on big data, the bling of tablets and the search for the holy grail of visualization, it becomes pretty clear that the only thing that finally matters in BI is the decision made and the action taken.  And... by understanding how the decision makers got there to enable them to more easily and effectively repeat and refine that process in the future.  This puts Lyza on the cusp of the next big emerging trend in IT--the "automation" of the human interactions that occur around the data and applications that IT already provides.  I place quotes around "automation" because, of course, this will be a very different type of automation than we have been used to in the past.  This is the integration of Web 2.0 concepts and tools into the enterprise.  Facebook with a purpose.  Twitter in context.  Social networks with a goal.

With version 3, Lyza has stepped boldly beyond the safe and well-understood confines of what BI has mostly thought about so far.  For some, it may pose the question: shouldn't this type of function come from another market with a different audience?  My response, in the form of another question is: what other market and audience should be looking at supporting, really supporting, decision makers?

The new collaborative Lyza will be available for free use from October.  I highly recommend giving it a test drive!

Posted September 14, 2011 10:53 AM
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