Can Robots Inspire Us To Be Better Humans?

Ken Goldberg presented at TEDxBerkeley, an independently organized TED event, an interesting lecture called 4 lessons from robots about being human, where he shared four very human lessons that he's learned from working with robots. Ken Goldberg is a Professor of Industrial Engineering and Operations Research in Robotics, Automation, and New Media at UC Berkeley and his work reflects the intersection of robotics, social media, and art. Watch and enjoy.

Dilbert Quantum Project Management

Dilbert by Scott Adams-Dilbert ©2012, Universal Uclick
Published at April 17, 2012 in dilbert.com

Celebrating 4 years of blogging

My blog is completing 4 years today. Many Thanks all of you for reading my blog for the last four years. Your readership is really appreciated.

Dilbert on Management's Understanding of Technology

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Dilbert by Scott Adams-Dilbert ©2012, Universal Uclick
Published at March 11, 2012 in dilbert.com

Welcome to the Social Enterprise

Good presentation at Cloudforce San Francisco 2012 by Marc Benioff on how the technology industry is shifting towards social. The SalesForce.com is doing a good job on it, and Marc Benioff is really a great keynote speaker.

A Big Data Imperative: Driving Big Action

Avinash Kaushik did a great presentation on Big Data at Strata Conference 2012. It's worth watching!



Avinash also wrote a post in his blog, talking about the presentation. He defines big data as "the collection of massive databases of structured and unstructured data. The data sources include traditional (now considered puny) sources like corporate ERP/CRM systems and non-traditional (massive) sources like every technical ping from every human or mechanical sensor, all web behavior by everyone across the entire Internet, increasingly digital data from analog sources like hospitals or the atmosphere, and (good lord!) our collective tweeted wisdom."

In the post, he listed the Six Rules That Should Govern Your Big Data Existence:

1. Don't buy the hype of big data and throw millions of dollars away. But don't stand still
Structure your big data efforts, at least initially, to fail faster while failing forward. Don't build the biggest, baddest big data environment over 32 months, only to realize it was your biggest, baddest mistake.

2. Big thinking about what big data should be solving for is supremely important
I can't think of any other time in our lives where we could literally swim endlessly in an ocean of data, without having anything to show for it. Big data is that world. If you don't know where you are going, you will get there and you'll be miserable

3. The 10/90 rule for magnificent data success still holds true
For every $100 you have available to invest in making smart decisions, invest $10 in tools and vendor services, and invest $90 in big brains (aka people, aka analysis ninjas, aka you!).

4. Shoot for right time data, not real time data
Real time data is almost insane to shoot for because even for the smallest decisions, you'll have to do a lot of analysis first (5 hours), then present it to your superior (1 hour), who will add two bullet items and send it to a team of people (20 hours), who will in turn argue about priorities and how much the data is wrong (16 days), but ultimately come to an agreement because the deadline to make the decision passed 7 days ago (20 seconds), and send the data to the big boss who'll read just the first part of the executive summary (3 days), and decide that the data is telling her something counter to what she has always known works, and she'll make a decision based on her gut feel (5 seconds), and some action will be taken (14 days).

5. "Data quality sucks, just get over it"
Data on the web will never get to 95% clean and it will have big holes and it will be sparse in some areas. We should aim to collect, process and store data as cleanly as humanly possible, but after that we should move on to using the data, because we will still have more data about the web than what God's blessed any other channel with.

6. Eliminating noise is even more important than finding a signal
Thus far in the history data analysis the objective for our queries has been trying to find the signal amongst all the noise in the data. That has worked very well. We had clean business questions. The data size was smaller and the data set was more complete and we often knew what we were looking for. Known knowns and known unknowns.

Dilbert on Customer Experience Management

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Dilbert by Scott Adams-Dilbert ©2012, Universal Uclick
Published at February 19, 2012 in dilbert.com

Top Trends That Will Impact Business Intelligence in 2012

The Business Intelligence scenario has evolved greatly in recent times, with new trends, approaches, concepts and tools. Early this month, I already shared my thoughts for the TIBCO Spotfire's Business Intelligence Blog, in a post entitled Top BI Resolutions and Trends for 2012 from Industry Experts, where Julie B. Hunt, Gregory Piatetsky-Shapiro and I talked about the BI Resolution for Companies and the Top 3 Trends for Industry. It's worth reading.

Here are my BI Resolution and a more comprehensive list of the top trends, that in my opinion, will impact Business Intelligence in 2012:

BI Resolution for Companies

Take advantage of the emerging BI trends to make organizations actually become a democracy of information, with business intelligence being used for everyone, anywhere, anytime.

Top Trends That Will Impact Business Intelligence in 2012

Mobile BI – The proliferation of smartphones and tablets in the enterprise, and the need to make decisions anywhere/anytime accessing real-time analytics make mobile BI remain a hot topic. There are several companies that specialize in developing mobile BI applications and most BI vendors also have developed a mobile version. Howard Dresner, who recently published the latest version of the Mobile Business Intelligence Market Study, considers that mobile BI becomes fundamentally the new platform for business intelligence.

Cloud BI – The cloud-based BI will boost the use of BI. The cloud model allows the companies to save money, with faster implementation without substantial investments. It is also ease of use. Although there are many concerns about implementing BI on the cloud (mainly security), the vendors and the market as a whole have matured. It is important to remember that it’s necessary to have a well-defined data integration strategy to implement a successful cloud-based BI.

Big Data – The amount of data in our world has been growing exponentially, and the uses of big data in the BI scenario will allow companies to put data to work more efficiently. They could really turn into data-driven enterprises.

Self-Service BI - With BI tools even easier to use, and the results easy to consume, enabling more flexibility and analytics capabilities to nontechnical users, the business users are less dependent on the IT.

In-Memory Analytics - The growing need of companies for high-performance analytics applications, with the ability to provide high speed of access in a big amount of data, make the interest in In-Memory Analytics platforms increases more and more.

Open Source BI - The Open-source BI has evolved significantly with more complete and sofisticated tools. They are being implemented by large, medium and small companies, moving toward mainstream.

Text Analytics and Sentiment Analysis - The growing use of social media for the companies (several companies have Twitter account and Facebook page) along with the need of collect and analyze many kinds of unstructured data are becoming the text analytics and sentiment analysis increasingly important.

Collaborative BI - BI tools with embedded collaboration capabilities allow BI users to share information and work together more easily and efficiently. People like and are familiar with the use of social media tools, thus facilitating the widespread use of Social and Collaborative BI.