Wednesday, December 10, 2014

HP in Barcelona Discover 2014

If like me you weren't able to attend the HP techfest in Barcelona, you can watch most of the speaker presentations on video.

As we said back in 2012, IT buyers needed a provider like HP that could deliver them solutions for their systems issues--legacy systems, burgeoning data, mobile applications, and higher hurdles for system security and compliance to name a few.  CEO Meg Whitman's accomplishments include convincing them that HP was such a partner.  Barcelona drives the same messages given consistently throughout 2014 with updates on new products and services, along with customer participation. 

A staple menu item in all the presentations is "Big Data," a term which clearly causes audiences to either cringe, yawn or glaze their eyes over.  

Software EVP Robert Youngjohns gave an interesting presentation on the New IT, forged from the fires of Big Data.  HP's approach, he says, distinguishes among three distinct types of Big Data:
  • Business data, coming from traditional sources like the corporate ERP, CRM, BI, and HR systems, for example).
  • Machine data (examples were log files and sensor data, i,e, the Internet of Things)
  • Human data (Email, video, photos)
The first category, which seems to be where the corporate data engine room hits the financials, is growing slowly.  It is the other two categories, particularly the third where the growth is explosive. He went on to say that the HP big data platform distinguishes among each big data category and tailors the offering accordingly.

His presentation brings up my concerns about the vacuity of the term Big Data.  Businesses are still growing, in broad terms, at GDP-like rates, or at the upper end, single digit multiples of the same. Machine and human data are exploding at ridiculous rates.

He quotes another fluff statistic: more photos are being produced each day than in the first 100 years since the invention of the photographic process.  Surely, the obvious conclusion is that the value of machine and human data are fractions of traditional business data, from data about exploration wells to customer purchasing patterns. 

If the value were commensurate, IBM, Exxon and other Big Data producers capable of deploying new systems would be seeing explosive growth in revenue or profit.  However, nothing like this is visible. Bellwether companies like Cisco sometimes see revenue shrinking. 

Youngjohns gives a personal example that raises the same issue.  He is a tech geek at home too.  His home wireless network is very complex. He has the controls for his climate system managed by sensors and a panel.  He has imaging, music, television and other activities on wireless subsystems. He decided to find out why NetFlix was operating very slowly, and so created a reporting system of activity logs which, he said, soon generated a terabyte of data!  

Now, I couldn't hear anyone laughing in the audience, but they should have been. The architecture of this network is clearly faulty, at least the infrastructure and maybe more. Leaving this aside, big data in this case was bad data, and unproductive for its expected benefit, namely to make NetFlix run faster.  The hardware and software costs per unit of data are so low, which merely masks the unproductive nature of the activity. 

Moving on to another presentation on Data Centers and the Cloud, the same issues came up in a different way.  Data centers, we are told, have to provide services for all kinds of devices, like fitbits, videos, and sensors, again the Internet of Things. These apparently create large data streams. 

However, much of this are personal data, surely. Why should a commercial infrastructure expand to support this data demand?  One of the speakers really struggled to come up with examples of supported devices that didn't sound as ridiculous as Fitbits, but he couldn't.  The demands being put on data centers from personal uses like Instagram, SnapChat, Facebook and other social media stem from the BYOD policy that has become the standard in corporate America: it is a policy that requires more resources and which ultimately reduces employee productivity.  

He did cite an example of instrumenting the corporate vehicle fleet, where a large volume of operational data (speeds, routes, mileage, fuel consumption) would create large data center demands. This doesn't sound earth shattering, but just a move up from what's being done currently by operators like UPS.

The Haven platform brings together ArcSight, Autonomy and Vertica for data analytics, while also providing a broader platform and tools for asset and security management. Sounds like things are being put into place for HP.

Of course, now it is going to split into two pieces. Stay tuned. 

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