Artificial Intelligence Needs More Than A Name, It Needs Personality

IBM's acquisition of Cognea, a startup that creates virtual assistants of multiple personalities, further reinforces that voice is not enough for artificial intelligence.  You need personality.

I for one cheer IBM's investment, because to be honest, IBM Watson's Jeopardy voice was a bit creepy.  What has made Apple's Siri intriguing and personable, even if not always an effective capability, is the sultry sound of her voice and at times the hilarity of Siri's responses.  However, if you were like me and changed from the female to male voice because you were curious, the personality of male Siri was disturbing (the first time I heard it I jumped).  Personality is what you relate to. 

The impression of intelligence is a factor of what is said and how it is delivered.  Think about how accents influence our perception of people.  It is why news media personalities work hard to refine and master a Mid-west accent.  And, how one presents themselves in professional situations says a lot about whether you can trust their judgment.  As much as I love my home town of Boston, our native accent and sometimes cold personalities have much to be desired by the rest of the country.  And we have Harvard and MIT!  Oh so smart maybe, but some feel we are not always easy to connect with. 

Read more

Welcome To The Future Of Data Management

 The demand for data has never been greater.  The expectations are even grander.  On the other hand, what the business wants has never been more ambiguous.  

Welcome to the future of data management.  

According to recent Forrester research, most of us are ill prepared.

  • The business is placing the ownership on data professionals for data needs they don't have the full knowledge to enable: security, quality, business intelligence, and data strategy. 
  • Pressure to contain cost causes data professionals to focus on bottom line efficiency goals and de-emphasize top line business growth goals.
  • Investment in data  is still grounded in bespoke systems that lack scale, flexibility, and agility
Read more

Categories:

Big Data Quality: Certify or Govern?

We've been having an intersting conversation with clients and internally about the baggage associated with Data Governance.  As much as we (the data people) try, the business thinks it is a necessary, but the commitment, participation, and application of it is considered a burden worth avoiding.  They wonder, "Is this really helping me?"  Even CIOs roll their eyes and have to be chased down when the data governance topic comes up.  They can't even sell it to the business.  

So, the question came up - Do we need to rebrand this? Or worse, do you abandon data governance?

Well, I don't know that I'm convinced that Data Governance needs a new name or brand.  And, with regulatory and security risks it can't be abandoned.  However, what organizaitons need is a framework that is business oriented, not data oriented.  Today, Data Governance is still stuck in the data, even with strong business participation.

Big data is the catalyst.  If you thought your data was challenging before, chaos and messiness takes on a whole other meaning with big data.  Scale now forces us to rethink what we govern, how we govern, and yes, if we govern.  This is to both better manage and govern process-wise, but it also drives us to ask the questions we didn't ask before. Questions about meeting expectations for data over meeting expectations to fit data into systems.

What this means...orient data governance toward data certification.

Read more

Why Every Data Architect Should Be An Analyst First

“Context” is the new buzz-word for data.  Jeffery Hammond talks about it in Systems Of Automation Will Enrich Customer Engagement, Robert Scoble and Shel Israel talk about it in their book “Age of Context”, and you can’t ignore it when it comes to a discussion for Cognitive Computing and the Internet of Things.  We’ve live in a world where data was rationalized, structured, and put into standardized single definition models.  The world was logical.  Today, we live in a world where the digital revolution has introduced context, the semantic language of data, and it has disrupted how we manage data. 

Big data technologies were created not because of volume and cost.  They were created to manage the multi-faceted model that data takes on when you have to link it to how regular consumers and business people see the world.  Performance and cost are only factors that had to be considered to scale in order to support the objective.  Search, recommendations, personalized web experiences, and next best action could not be possible in a structured single definition environment.  Why we know this is that the sculpted purpose built environments that supporting business applications collapsed when analytics to discover causation in relationships and correlations at scale was applied.

That is the tipping point for data architects.

Read more

Data Governance: Did We Make The Right Choices?

Coming back from the SAS Industry Analyst Event left me with one big question - Are we taking into account the recommendations or insights provided through analysis and see if they actually produced positive or negative results?

It's a big question for data governance that I'm not hearing discussed around the table.  We often emphsize how data is supplied, but how it performs in it's consumed state is fogotten.  

When leading business intelligence and analytics teams I always pushed to create reports and analysis that ultimately incented action.  What you know should influence behavior and decisions, even if the influence was to say, "Don't change, keep up the good work!"  This should be a fundamental function of data govenance.  We need to care not only that the data is in the right form factor but also review what the data tells us/or how we interpret the data and did it make us better?

I've talked about the closed-loop from a master data management perspective - what you learn about customers will alter and enrich the customer master.  The connection to data governance is pretty clear in this case.  However, we shouldn't stop at raw data and master definitions.  Our attention needs to include the data business users receive and if it is trusted and accurate.  This goes back to the fact that how the business defines data is more than what exists in a database or application.  Data is a total, a percentage, an index.  This derived data is what the business expects to govern - and if derived data isn't supporting business objectives, that has to be incorporated into the data governance discussion.

Read more

Top 4 Things to Keep In Mind When Evaluating MDM Vendors

The Forrester Wave for Multi-Platform MDM is out!

The last Forrester Wave for MDM was released in 2008 and focused on the Customer Hub.  Well, things have certainly changed since then.  Organizations need enterprise scale to break down data silos.  Data Governance is quickly becoming part of an organization's operating model.  And, don't forget, the big elephant in the room, Big Data.  

From 2008 to now there have been multiple analyst firm evaluations of MDM vendors.  Vendors come, go or are acquired.  But, the leaders are almost always the same.  We also see inquiries and implementations tracking to the leaders.  Our market overview report helped to identify the distinct segments of MDM vendors and found that MDM leaders were going big, leveraging a strategic perspective of data management, a suite of products, and pushing to support and create modern data management environments.  What needed to be addressed, how do you make a decision between these vendors? 

The Forrester Wave for the Multi-Platform MDM market segment gets to the heart of this question by pushing top vendors to differentiate amongst themselves and evaluating them at the highest levels of MDM strategy.  There were things we learned that surprised us as well as where the line was drawn between marketing messaging and positioning and real capabilities.  This was done by positioning the Wave process the way our clients would evaluate vendors, rigorously questioning and fact checking responses and demos. 

Read more

Artificial Intelligence - What You Really Need to Know

It looks like the beginning of a new technology hype for artificial intelligence (AI). The media has started flooding the news with product announcements, acquisitions, and investments. The story is how AI is capturing the attention of tech firm and investor giants such as Google, Microsoft, IBM. Add to that the release of the movie ‘Her’, about a man falling for his virtual assistant modeled after Apple’s Siri (think they got the idea from Big Bang Theory when Raj falls in love with Siri), and you know we have begun the journey of geek-dom going mainstream and cool.  The buzz words are great too: cognitive computing, deep learning, AI2.

For those who started their careers in AI and left in disillusionment (Andrew Ng confessed to this, yet jumped back in) or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics.  They point to the reality of Apple’s Siri to listen and respond to requests as adequate but more often frustrating.  Or, IBM Watson’s win on Jeopardy as data loading and brute force programming.  Their perspective, real value is the pragmatic logic of the predictive analytics we have.

But, is this fair?  No.

First, let’s set aside what you heard about financial puts and takes. Don’t try to decipher the geek speak of what new AI is compared to old AI.  Let’s talk about what is on the horizon that will impact your business.

New AI breaks the current rule that machines must be better than humans: they must be smarter, faster analysts, or they manufacturing things better and cheaper. 

New AI says:

Read more

The Seven Deadly Sins of Data Management Investment and Planning

When it comes to data investment, data management is still asking the wrong questions and positioning the wrong value.  The mantra of - It's About the Business - is still a hard lesson to learn.  It translates into what I see as the 7 Deadly Sins of Data Management.  Here are the are - not in any particular order - and an example:

  1. Hubris: "Business value? Yeah, I know.  Tell me something I don't know."  
  2. Blindness: "We do align to business needs.  See, we are building a customer master for a 360 degree view of the customer." 
  3. Vanity: "How can I optimize cost and efficiency to manage and develop data solutions?"
  4. Gluttony: "If I build this cool solutions the business is gonna love it!"
  5. Alien: "We need to develop an in-memory system to virtualize data and insight that materializes through business services with our application systems...[blah, blah, blah]"
  6. Begger: "If only we were able to implement a business glossary, all our consistency issues are solved!"
  7. Educator: "If only the business understood!  I need to better educate them!."
Read more

Can Machines Be Our Friend? IBM Watson Thinks So.

IBM launched on January 9, 2014 its first business unit in 19 years to bring Watson, the machine that beat two Jeopardy champions in 2011, to the rest of us. IBM posits that Watson is the start of a third era in computing that started with manual tabulation, progressed to programmable, and now has become cognitive. Cognitive computing listens, learns, converses, and makes recommendations based on evidence.

IBM is placing big bets and big money, $1 billion, on transforming computer interaction from tabulation and programming to deep engagement.  If they succeed, our interaction with technology will truly be personal through interactions and natural conversations that are suggestive, supportive, and as Terry Jones of Kayak explained, "makes you feel good" about the experience.

There are still hurdles for IBM and organizations, such as expense, complexity, information access, coping with ambiguity and context, the supervision of learning, and the implications of suggestions that are  unrecognized today. To work, the ecosystem has to be open and communal. Investment is needed beyond the platform for applications and devices to deliver on Watson value.  IBM's commitment and leadership are in place. The question is if IBM and its partners can scale Watson to be something more than a complex custom solution to become a truly transformative approach to businesses and our way of life. 

Forrester believes that cognitive computing has the potential to address important problems that are unmet with today’s advanced analytics solutions. Though the road ahead is unmapped, IBM has now elevated its commitment to bring cognitive computing to life through this new business unit and the help of one third of its research organization, an ecosystem of partners, and pioneer companies willing to teach their private Watsons.

Read more

Big Data Governance Protect And Serve Are Equals

I had the opportunity to speak and participate in a panel on data governance as it pertained to big data. My presentation was based on recently completed research sponsored by IBM to understand, what does data governance look like by firms embarking/executing on big data? The overarching theme was that data governance is about protect and serve. Manage security and privacy while delivering trusted data.

Yet, when you look at data governance and what it means to the data practice, not the technology, protect and serve is also a credo. In business terms it represents:

  • Protect the reputation and mitigate risk associated with inappropriate use or dirty data.
  • Serve information needs of the business to have information fast and stay agile to market conditions.
Read more