The first comes from TechMASH about data science being the next big thing.  The primary nugget of note is that the supply of employees with the needed skills as data scientists – those people who really understand how to pull relevant information out of data reliably – is going to have a tough time meeting demand.  Here’s an interesting infographic on the current disconnects – for example, while 37% of “business intelligence” professional studied business in school, 42% of today’s “data scientists” studied computer science, engineering, and natural sciences.  This highlights the increasing demand for students that have solid mathematics backgrounds – it’s becoming more about knowing how you pull information from data, regardless of application.

Don’t get me wrong – to be effective applying data science, you need two things:  a subject matter expert that understands what makes sense and what doesn’t, and someone who really understands data to pull out the information.  Sometimes that can reside within one person, but it’s rare and takes many years of training to acquire the necessary excellence in both fields.   And as the demands for data analysis grow, these two areas will likely form into distinct disciplines with interesting partnership opportunities being created.

The definition of data science is still being defined, but I’m convinced it will have huge impact in the next five years.  And while the science aspects of data are starting to be defined, the engineering aspects of data and analytics are truly in their infancy…

On the same thread, here’s a Forbes article by Tom Groenfeldt on the need for data scientists, or Excel jockeys, or whatever they will be called in the future.  For some companies, the move to “data science” is quite apparent, but for others, the current assemblance of business professionals that have figured out the ins-and-outs of Excel spreadsheets work quite well.  This is likely a snapshot of where things are today, but I do believe that as the questions we ask of the data get more complicated, we will clearly see the need for a more rigorous science-based discipline to data wrangling…

The last tidbit is from the Wall Street Journal about the healthcare field being the next big area for Big Data.  I do think that healthcare is ripe for leveraging data, and I’ve written other posts on the subject.  One former Chief Medical Officer that I spoke with mentioned that one of the big problems is just getting the data useable in the first place.  He said that, as of today, 85% of all medical records are still in paper form.  The figure seems a bit high to me, but I don’t really know how many patient records in various individual doctor’s offices are still sitting in folders on shelves. 

There has been a big push lately, spurred by financial support from the U.S government, for upgrading to electronic health records (EHR).   This will help to solve the data collection problem – if you can’t get data into an electronic format, you can’t utilize information technologies to pull information out of the data.

Algorithms themselves are a form of creativity. The problem is the illusion that they’re free-standing. If you start to think that information isn’t just a mask behind which people are hiding, if you forget that, you’ll pay a price for that way of thinking. It will cause you to be less creative.

If you show me an algorithm that dehumanises, impoverishes, manipulates or spies upon people,” he continues, “that same core maths can be applied differently. In every case. Take Facebook’s new Timeline feature [a diary-style way of displaying personal information]. It’s an idea that has been proposed since the 1980s [by Lanier himself]. But there are two problems with it. One, it’s owned by Facebook; what happens if Facebook goes bankrupt? Your life disappears – that’s weird. And two, it becomes fodder for advertisers to manipulate you. That’s creepy. But its underlying algorithms, if packaged in a different way, could be wonderful because they address a human cognitive need.

I think this is a really great read for anyone who’s interested in data, algorithms, and their impact on society – there’s a lot of really good stuff to take in.  You can read the entire article here…

Well, back in September, William Shatner, who portrayed Captain Kirk on the original Star Trek series, posted a YouTube video about how Star Trek was really better than Star Wars – how Lucas’ space opera was really “derivative” of Gene Roddenberry’s creation by 10-20 years. 

Well, a couple of weeks ago, Carrie Fisher, who played Princess Leia in the original Star Wars trilogy of movies, had to call Shatner out, posting her own video rebutting the critique.

Below are the videos to both Shatner’s original post, and Fisher’s reply.  Personally, I’d rather see the original Kirk and Leia duke it out, but I guess we’ll have to settle for their earthly thespians…

I think Hill is onto something, especially with this insight:

What will healthcare look like in the year 2020?  One thing is certain: we can’t afford its current trajectory.  Left unchecked, our $2.6 trillion in annual spending will grow to $4.6 trillion by 2020, one-fifth of GDP.  With almost 80 million Baby Boomers approaching retirement, economists forecast these trends will likely bankrupt Medicare and Medicaid in the near future.  And while healthcare reform ignites a number of important changes, alone it does not resolve our issues.  It’s critical we fix our system now.

Something’s got to give, and better decisions from better data can yield significant healthcare savings if done right.  Saving lives and reducing costs dramatically in healthcare would qualify as one of those hard problems where disciplined approaches can yield significant results.  Here is Hill’s post on Forbes…

What do companies miss out on if they don’t have a data strategy?

Potentially a lot. If you say the way our products and services are constituted, how we determine our strategy and maintain a competitive edge against other folks–if data is a very strong element of each of these, and you’re not doing anything, it’s like trying to run a business without business intelligence. I’m not sure I have a broad enough view that I would say every company needs to have a data strategy. But I would say many companies do. I certainly think that any company that is over 20 people needs to have a technology strategy, and data is essential to where technology is going.

LinkedIn has already been on record as not worrying about Facebook taking over their business.  According to Hoffman, “People with advanced degrees are three times more likely to use LinkedIn.”

You can read the Fast Company interview here…

Forbes has a couple of posts that peek into businesses use of “big data”.  The first article talks about the race to build new analytics to solve challenges of large volumes of data.  Here’s a snippet from Tom Groenfeldt‘s post, quoting Scott Gnau, head of research and development at Teradata:

Thought leaders in a number of industries are starting to leverage the additional analytic content from big data and combine it with what they have in large volume data stores as well. It is interesting to understand social media and consumer sentiment, but when that information is analyzed in combination with traditional consumer data it provides new, rich intelligence helping companies to identify trends and react to immediate business conditions.

According to another Forbes article, there are a number of studies that show that companies that characterize themselves as ”data driven” as the best corporate performers.  Now, when we’re talking “data driven”, we mean in how the company operates, not necessarily in what it produces as technology.  Top performing companies are determined to use the data that they have (especially about themselves) to improve what they do and how they do it. 

Also, banks are on the lookout for changes that could affect how they do business with their customers, and of course, their bottom line:

Banks, for example, worry about their customers divorcing, because divorce causes a change in credit-worthiness. No problem. They can now see a divorce coming before the couple does. All from the data.

As part of the “Computer Science or Data Science” panel at Techonomy 2011 in Tucson, AZ this week, the panel explored how data science has taken its place next to computer science as a fundamental element of information technology.  New technologies are coming out seemingly every day, not only to handle big data, but to understand how to extract relevant information from the ocean of data we’re swimming in.

A company in Silicon Valley, ai-one, announced today that they have “a breakthrough method to graphically represent knowledge enables software developers to easily build intelligent agents such as Apple’s SIRI and IBM Watson”.  The technology, ai-Fingerprint, is geared toward natural language programming, allowing developers to create new technologies that use natural language as input data.  

Apple’s Siri and IBM’s Watson are definitely heading in the right direction for this type of technology.  I just bought an iPhone 4S and I’ve tested Siri out a number of times.  While Siri doesn’t get everything right (it keeps thinking my name is “Nick” when I say “Mic”), it does get more right than I expected.  I was able to send texts and e-mails to people without keystrokes, and I took some notes using the voice feature, getting nearly every word correct.  Pretty amazing stuff!…

Watson is the supercomputer that beat two longtime Jeopardy! champions, and it uses a technology approach that looks for the best answer for the questions being asked (or in this case, the best question for the answer being presented – it is Jeopardy! after all…).  These are definitely the models that should be emulated; although, ai-one’s announcement is a press release so before we see the results, let’s chalk this up at the moment as good marketing…

I wrote previously about Columbia’s effort to expand its Manhattan campus to build a data science and engineering center.  However, Stanford and Cornell are also in the race. 

Much of this comes from a desire by the City of New York to become home to a leading engineering and applied science campus.   NYC is willing to invest $100 million into infrastructure improvements for the winner.

Stanford and Cornell have put in bids for the project to use the land on Roosevelt Island, while Columbia will be expanding its Manhattanville campus.  Other schools are looking to expand in NYC as well - Carnegie Mellon, which is looking at the Brooklyn Navy Yard, and NYU, which wants to move into Downtown Brooklyn.

J. Edgar

The movie was directed by Clint Eastwood, who won Academy Awards for Unforgiven and Million Dollar Baby, and J. Edgar Hoover was played quite well by Leonardo DiCaprio, who has been toyed with by the Academy in receiving three nominations for acting, but is yet to receive his deserving award.

[Aside:  DiCaprio is a fantastic actor, and I enjoy watching him on the screen.  He's received Oscar nominations for Who's Eating Gilbert Grape?, The Aviator, and Blood Diamond.  However, he starred in Titanic, which won the most Oscars ever, yet he didn't received a nomination?!  He was great in Catch Me If You Can, Inception, Shutter Island, and particularly The Departed, but no Oscar nods.  Letter to the Academy:  Honor the man, pronto - don't make him wait 25+ years like you made Martin Scorsece wait...  OK, stepping down from soapbox...]

Through Eastwood’s telling of the story, we find that there were three main people in Hoover’s life:  his mother Annie Hoover, Clyde Tolson, his number two in the Bureau and his life companion, and Helen Gandy, Hoover’s personal secretary.  These three people really did comprise the entirety of Hoover in who defined him, who nurtured him, and who protected him.

What’s clear from the picture is that J. Edgar Hoover had an incredibly logical mind (he even invented a card catalog system when he worked at the Library of Congress) and was a true innovator in the area of criminal and forensic science.  His recognition of the use of fingerprint forensics to solve crimes was genius, and he certainly was constructive in fighting communism as a radical force in the United States, and in fighting organized crime elements in the big cities.

However, the part of Hoover for which he will be most remembered is his surveillance (in some cases, illegal) of public figures such as President John F. Kennedy and Dr. Martin Luther King Jr., and the secret “personal and confidential” files that he kept on people in order to coersce high-ranking U.S. officials to get his way.

Hoover had elements of genius, but some real shortcomings.  Certainly the times in which he lived didn’t allow him to live his life as transparently as he might have liked.  Yet, other flawed parts of his character showed through quite clearly regardless of the times.

Eastwood did as good of a job (I think) as he could with Hoover’s life (and the screenplay), and I personally think that DiCaprio did a great acting job in portraying Hoover as a human being, even though he ran the FBI with an iron fist during his 48-year tenure, intimidating Attorneys General and Presidents in the process.  We do get the sense of the strong personal bonds Hoover had with his mother, Tolson, and Gandy, even though he didn’t always treat them well.  The acting is very solid – Naomi Watts is very good as Helen Gandy, Dame Judi Dench plays Hoover’s mother amazingly well, and Armie Hammer is quite good as Hoover’s companion Clyde Tolson.

However, while I like nonlinear storylines with flashbacks to fill in the timeline, the timeline for this movie goes back and forth a bit too much for my taste – it actually made it hard to figure out where I was in the story.  Plus, the story itself was somewhat slow at times, which made the overall length of the movie seem longer than it really was.

Overall, I enjoyed learning about Hoover and his life (both public and private), but I probably would have enjoyed a one-hour documentary instead of Eastwood’s two-and-a-half hour drama.  If you want to see good actors, J. Edgar might be good (as a rental), but you want to know more about J. Edgar Hoover, there’s probably a good documentary out there.

Here is a really good insight from Dumbill and how data science applies to business:

Why is the scientific method applicable to business and data?

Every company’s business is complex in itself, and they operate in a complex world. The financial, economic and societal structures we live and do business in are complex. Because of this complexity and interactions, businesses can be viewed in the same light as organic, biological systems. They are complex entities within a complex system.

This is where science comes into play. Even assuming you could come up with a top-down mathematical model of your business, there’s too much interaction and randomness with complex systems for your model to be practical. Thus, the exploratory approach of science becomes useful to a business.

Your world and business is a giant laboratory, and ever more so as the world becomes more networked. By employing data scientists you can discover better how your business works, how it can be improved, and find new things you can do that you didn’t know of before. To do this, you must connect up three kinds of people: the business folk, the data scientists and the data engineers.

I do like Dumbill’s take here and there is absolute merit with applying the scientific method to business activities.   Peter Wang of Streamitive commented on Dumbill’s post as well, and has some interesting points…

Ultimately, data doesn’t mean anything without trying to answer questions.  To get actionable information, you need data and you need to be asking the right questions. That’s why the scientific method is so important – it’s all about posing a hypothesis or asking a question, and then squeezing the right information out of the data in order to answer it.

Gartner uses a Magic Quadrant to rate companies as leaders, challengers, niche players and visionaries based on several criteria including “completeness of vision” and “ability to execute.”  From Gartner’s website:

• Leaders execute well against their current vision and are well positioned for tomorrow.
• Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.
• Niche Players focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others.
• Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.

A post by Mark Brunelli, Senior News Editor, at SeniorDataManagement has a more detailed analysis of the Gartner report.  Here’s what Brunelli wrote, detailing some of the thoughts of Ted Friedman, a Gartner vice president and information management analyst and co-author of the report:

“You’re hearing a lot about big data and analytics around big data,” Friedman said. “To do that kind of stuff you’ve got to collect the data that you want to analyze and put it somewhere. [That] in effect is a job for data integration tools.”

It does seems that the main focus right now in this space is on data handling and data management.  A lot of work is being done by companies to create data visualization tools to gain insight from the data, but as the problems get much harder, better analytics approaches will need to be brought to bear.  The real key over the next few years will be on the smart analysis of all this data, turning the data into reliable actionable information.