Gartner BI & IM summit - day 1

This is somewhat a summary of my twitter feed from today and also some more details I didn’t have time to tweet.

Soft skills matter

The standout session for me today was Mark Jeffries closing keynote. Mark talked on the importance of soft skills and effective communication as critical to success. He offered some excellent tips including:

• sales - we’re all in sales
• scales - tip them in your favour
• acknowledge - thanking those who have helped you is important

Listening is important:

• listen
• watch
• anticipate
• react

Three ‘Cs’ of communication:

• consistency
• clarity
• confidence

He also talked about the importance of networking, and explained name amnesia. Was abit sad to see someone talking to him straight afterwards who completely ignored most of his advice though!

Opening keynote

I only saw the first bit of the opening keynote, but I was surprised by some of what was said. For example Bill Hostmann said that costs were increasing (as was demand), but on almost any measure, cost is decreasing (all else equal) and this is primarily driven by technology. Must have missed the point on that one…

A panel by any other name

I enjoyed the panel discussion where SAP’s Timo Elliott, QlikTech’s Henry Seddon and someone from CommBank pitched their views against the audience.

From the trenches

Doug Laney provided some interesting insights into Big Data, and showed some analysis of the relationship between the ‘info-centricness’ of a company and its market valuation.

The essential proposition is that information centric companies are more valued by the market when normalized for their tangible assets (using Tobin's q). This is of course true by definition, but I thought it was a neat summary of the value of information.

Some other fascinating insight from Doug’s presentation was that only 12% of organizations see process automation as the biggest opportunity for big data in their organisations. To me, process automation is the holy grail of big data - but I think of process automation as including as much operational decision making as possible, while I suspect most analysts would put that sort of analytics into the other categories (decision support and insight discovery).

I put decision support and process automation into the same category - removing the need for manual intervention in operational processes. Strategic decisions are of course another matter.

Dark data

Another chart I found interesting from Doug’s presentation was from the same (May 2012 web survey, which admittedly had a smallish sample of ~160-170 responses) related to the sources of data seen as representing the greatest immediate opportunities for enterprise.

• 38% dark data
• 38% more detail from customers, suppliers, etc
• 16% social media content
• 4% commercially available data
• 4% publicly available data

Dark data is a reference to the existing data held by the organisation (for example, archived emails) which is not being used.

Obama’s Analytics Election Win

Doug also (apologetically) made reference to the Obama Campaign’s extensive use of Analytics. I asked him what could be learned from their use - it was a leading question as I have my own views on what we can learn from their success. At first he suggested that their use of cloud infrastructure meant they had a small footprint, and then added that their experimental nature meant they were running models daily.

After the presentation was finished, one of the other attendees came up to me and added that they thought the campaign’s use of gamification had also played a big part in their success.

Best realtime analytics example yet?

Doug mentioned, by way of an example, a fast-food company in the US that was making use of realtime analytics in an innovative way - to quality assess their potato chips. Each chip was launched into the air, where its image was analysed and then was blown either to a discard pile or a keep pile by jets of compressed air!

Now that’s realtime Analytics!

Day two

I’m looking forward to

• To the point: sentiment analysis
• Business Analytics Center of Excellence or IT Center of Exclusion?
• To the point: data sciences, arts and crafts
• Building trust in your analytics - data quality trends and best practices

And of course the keynotes:

• Addressing the data skills competencies
• Infonomics - understanding the economics of information