Cognitive is not a tool

Cognitive computing is not a tool.  It's not a series of tools.  Cognitive computing is a journey that starts with a first step.  Cognitive is a journey that once begun, you can't help but see the world through a cognitive computing lens.

Here in Las Vegas at IBM's World of Watson conference this week Ginni Rometty remarked that a programmed system loses value over time while a trained cognitive system increases in value over time.  Cognitive tools like Watson are going to upend the value proposition of technology investments.

Watson - Making the World a Better Place, one App at a Time

I was in Austin, Texas recently for some high level Watson meetings.  The purpose was to catch up on my cognitive project and make sure it was on track, which it is.  While I was there, I hung out with some of my friends from Cerebri AI and attended the launch of 

Austin 211, a new social services app that will make the world a better, more cognitive place to live.

 

The app, Austin 211, was created by Cerebri AI to connect people with the services offered by the United Way of Austin.  The difference is that this is completely smart phone enabled, and with a few clicks can answer questions and, based on location, direct the user to the resources they need to improve their quality of life.  There's even a list of most common questions asked, so users can quickly find the solution to what ails them.  The coolest part of this is Watson is cotinuously learning about what users needs are, and can use that information to make suggestions for the user.

I'm fortunate that I have never been in a position to need any United Way services, but the fact that anyone can get tailored answers immediately from a mobile device in a time of need makes me excited that technology is being used to make lives better in this way.  

iPhone screen capture from Austin 211 app

 

Meeting Your Heroes

I think a hero is someone really intent on making this a better place for all people- Maya Angelou

I was in Armonk, New York this week, visiting the IBM Corporate offices.  You're probably puzzled as to why I was there.  Let me puzzle you further, I was invited to sit down for an interview with Ginni Rometty.

You read that right.

Ginni Rometty.  THE Ginni Rometty. IBM's CEO, President and Chairman of the Board.   I was interviewed for IBM's THINK Friday Academy, an internal program that focuses on various parts of the organization.  One of the parts of the program is a customer interview, where Ginni sits down with a customer and discusses how they are using IBM's products.

I was there to talk about cognitive technologies, something I'm obviously very passionate about.  I discussed my use and opinion of analytics, cognitive analytics, and artificial intelligence.

This is what I come away with from the meeting:

1.  IBM is run by a passionate leader in Ginni.  I use that word because you can see in her eyes and hear in her voice the passion it takes to lead a company like IBM through the world of Technology today.  She is an impressive person, someone you would want to follow when trying to navigate a difficult transition.  She wants to make a difference in this world, making it for the better.  No doubt in my mind.
2.  IBM employs passionate people to manage the organization.  I've worked with people from various parts of IBM.  At each turn I encounter people wanting to execute Ginni's vision and working to make a difference.  The people in the room when I was interviewed asked thoughtful  and probing questions.  I truly believe they wanted to know how they can make their company and their products and services more relevant to people and companies like mine.

I don't come from a Fortune 50 background.  But I am a customer of IBM, and was treated like I was the most important person they had ever met.  That not only impresses me, it says a lot of the organization and their willingness to listen to any customer and what they have to say.

Last month Warren Buffet increased his holdings of IBM, despite the stock's slide since the beginning of the year.  After meeting Ginni and her team, I'm willing to do the same.

Data Science, It's a Cultural Thing

'It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.'

- Sherlock Holmes,  A Scandal in Bohemia

I’ve always loved this quote from Sherlock Holmes.  I used to battle this every day (much less now) when I was starting my data analytics journey.  Old assumptions die hard.  Just because it happened before, or we observed a fact, it didn’t mean that was the answer forever.  New data and new facts can change assumptions, and if we’re lucky, change our direction if we can spot it early enough.

Luckily I’m several years into my data journey, and don’t have to press (as much) when I say we need to look at the data.  I believe it’s because I’m at the center of an organization that has successfully endured a cultural shift in regards to understanding the importance of interpreting the data, using it to make good decisions.

I recently came across a study published by IBM's Center for Applied Insights titled: Breakthrough experiments in data science: Practical lessons for success.  I wish they had published this when I began my journey, it might have made it a bit less bumpy.  I’ve decided to look at the main points and give my comments on the practical advice from the researchers. 

1.    Infuse data science into the culture

This wasn’t easy, but I agree is of paramount importance.  Your organization has to come to a point where the trust in the data is essential because they use it to make decisions for their business.  Having the mindset that “how will we capture the data on this new process so we can learn from it?” will take you a long way to success.

2.    Design a data science capability

     

     This seems like a no-brainer, but it is so important to success. How are you capturing data?  What sorts of governance have you applied?  Have you started with the end in mind?  Data is so valuable, and so important to good outcomes, that having a good foundation program capability will set you up for later breakthroughs.

3.    Equip with the right technology

I love spreadsheets.  I cut my analytics teeth using Excel (okay, Lotus 1-2-3) and various add-ins. They work well, but aren’t the best tool in all cases.  They require a lot from the user in order to manipulate the data for the desired outcome.  I’ve found that statistical tools like SPSS and Watson Analytics can do a lot of heavy lifting when trying to analyze data without a lot of (comparatively speaking) work.

4.    Showcase your results

Who doesn’t like to say “I told you so?”  Maybe not the best attitude to win over converts, but you get the idea.  There are so many anecdotal examples that companies run for long times on that it becomes something not to question.  One thing I’ve learned is that big prizes are to be won when we question orthodoxy.  What better way to do it than with data?  People don’t know what they don’t know.  It’s the data scientist’s job to do just that.

 Link to Breakthrough experiments in data science: Practical lessons for success

About to start a genuine Watson Project

I talk about Watson l lot.  So far, except for using Watson Analytics for structured data analysis, I haven't actually used Watson or any of the API's for unstructured data analysis.

I've seen them.  

I've read about them.  

I've studied them.

But I haven't used them.  

All that is about to change.  Very soon I'm embarking on a true "cognitive" project.  Its both exciting and frightening at the same time.  

Exciting because I LOVE new technologies, especially ones that change our world.  Watson's ability to analyze paragraphs of text and return answers to questions, learning as it goes along, is nothing short of amazing.  

Frightening because it represents change.  Unknown change.  I read articles every day about robots taking our jobs, replacing the functions we humans perform every day.  

I've reconciled my thoughts by realizing that Watson can help us do what we don't do well, and support us in the things we do well.   I cant quote war and piece, but Watson can do it for me.  Watson  can hold a conversation, but it doesn't have the charm and ability to connect with people the way I do.

Time will tell how this works out, but for one thing, it will be an amazing ride.

Who I'll be reporting to, if I'm not replaced

Analytics in the Cognitive Era

IBM did an excellent job keeping me busy this week, not that I mind at all.  I spoke to analysts, customers, gave presentations, and was included in a keynote session with IBM Analytics GM Alistair Rennie during the general session on Watson Analytics and Cognos Analytics.

I've been in the analytics space for a very long time, and in regards to IBM have seen the platform evolve over the last decade or so.  What's interesting to note is that not all of the progressions were instant hits (powerplay to analysis studio anyone?) hits, but they all eventually became great products that increased the business intelligence of organizations across the globe.

Here's a rundown of what I was asked to input on as customer and a thought leader.  Fast forward the link to 1:08 to get to the introduction and hear my thoughts on analytics in the cognitive era.

Link to day 2 at IBM Insight

Me and Alistair Rennie, GM of IBM Analytics

The Cognitive Era

IBM’s CEO, Ginni Rometty declared this week that we are in the “Cognitive Era”.  According to IBM’s release, we are in a new era of technology, a new era of business, a new era of thinking. What does this all mean?  How can we apply it?

To give some context to the cognitive era we have to look at the business computer “eras” that have come before this.  The “Tabulation Era” was the first.  Calculating and accounting for all of the transactions that occurred were used by super computers to keep track of the statistics.  Issuing inventory was kept in a ledger, and then the data was entered into the GL systems for tracing and accounting.

The second era came with the advances in technology to automate processes, or the “Automation Era”.  Think of computer programming and the programs that combined multiple functions together.  ERP systems combined operations processes such as inventory issuance with accounting systems eliminating the need for multiple processes in order to record the transactions.  The system is “rules based” and rigid based on what was programmed.

That brings us to what is being described as the “Cognitive Era”.  Dictionary.com defines “Cognitive” as “relating to the mental processes of perception, memory, judgment and reasoning”.  So the “cognitive era” in our example means that the systems will use all of the information available to manage the supply chain of inventory as it is required based on cognition.  The difference here is that the computer “learns” the system, and makes decisions based on what it concludes, much like a human does. 

So, what does this mean for the new era?  According to IBM, 80% of all data is invisible to computers.  The data they are referring to is “unstructured data”, which are paragraphs of text.  The other 20% has been the subject of what they call “big data”, a term used for all of the massive amounts of data that is being created every time we turn on our smart phones, post a tweet on Twitter, or enter a credit card number for a purchase on Amazon.com.  Decisions across the globe are made every second based on analytics arising from the 20% of structured data that’s analyzed.  Combine these decision paths with the 80% of structured data into a system that “thinks” and you’ll get better outcomes to very difficult problems.

I was recently given a powerful example of the combination of structured and unstructured data in decision making that will change how quickly a company can respond to changing product demands.   In this scenario, a marketer searches keyword terms (unstructured data) on a popular upcoming kid’s movie across all social media.  The keywords are analyzed by the system in a cognitive fashion for the most popular terms associated with the original terms.  That is, the system “thinks ahead” for the marketer using all of the unstructured data at its disposal.  It also refines and learns patters in the data to come up with new techniques in understanding that is being searched for.  The unstructured data is combined with sales data, location information and demographics (structured data) to determine where the product is most needed, by whom, and when.  Data is transformed cognitively into actionable information, in just a few clicks. 

The real difference here from a typical program is that the learning piece is added to the equation.  For computers to think and learn patterns from data is not new, in my opinion.  What is trans formative into this new area is the cognitive system to process all of the data, structured and unstructured, at the velocity and volumes that are being created today.  It’s not just making a decision on the available data that makes this era so unique.  It’s making decisions using ALL of the data.

I once was fortunate enough to have lunch with several others with Steve Mills, Executive Vice President, IBM Software and Systems back in 2011.  He was asked a question by one of the people attending, “How do we know what data to capture?” His answer, in light of this cognitive era was poignant 4 years ago.  “All of it” he answered.

I think he was right.