Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI). Pulling down the barrier between human and machine has been a decades long holy grail pursuit. Right now, it is a novelty. In the near future, the implications are immense.
Which brings us to why should you care.
Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat. No one predicted this - the polls were wrong, by a long shot. Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters. He asserts that while data collection was limited to discrete survey questions, what it lacked was context. Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.
The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale. Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings. But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.
Clients now have a report that helps them make more informed choices about selecting a PIM solutions. PIM is not always a well understood master data solution option for Enterprise Architects. Questions arise about, do I need PIM or MDM or do both? Aren't PIM and Product MDM the same? How does this fit in my architecture? This report takes away the confusion, answers these questions. It gives insight into how vendors satisfy PIM demands, differentiate from MDM and operate in hybrid scenarios.
The first Forrester Wave collaboration across the Business Technology and Marketing and Strategy groups. In the age of the customer, tighter collaboration between business decision makers and technology management professionals is critical. This wave addresses both perspectives providing the business requirements for marketing and product professionals while also addressing the technical questions that are important when selecting tools. Yes, business and technology management can work together, be on the same page, and produce great results!
Master data management is a hot topic. And, this is at times surprising to me because the noise of big data is deafening. Big data is certainly sexier. MDM is like mom nagging to clean up the room - necessary, but a total buzz kill.
Here is some of the anecdotal evidence that is raising my eyebrows:
Our Forrester Wave for MDM was at the top of most read reports during Q1.
MDM inquiries from clients keep me very busy.
Vendors see MDM as a key growth area in their portfolios.
Consultancies are consistently pointing to client gaps in data governance and data architectures that point toward a master data problem.
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.
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.
“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.
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.
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.
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.