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.
Innovative organizations rely on content to make informed decisions about their customers, products, and go-to-market plans. Accurate information needs to get to the right prospect, partner or client at the right time. Large companies often have multiple content management systems, particularly in industries that grow via acquisitions. Busy information workers need to make decisions, and this can get complicated if multiple systems from multiple vendors are in place.
Standards have the potential to help organizations stay agile and responsive to change. Good standards help companies streamline routine requirements and avoid re-inventing the wheel. Bad standards get ignored, fall out of date and become barriers to innovation.
CMIS (Content Management Interoperability Services) has been a much-discussed standard in the ECM world, even before its formal ratification in 2010. In our 2013 ECM survey, just 13% of content management decision-makers put CMIS front and center as part of their strategy. What I wanted to understand:
Who is using CMIS in the real world?
How are architects using it to deliver valuable content to their busy front line workers?
How are software vendors using it to respond to their customer demands to bring content into a bigger information ecosystem?
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.
My wife would say that the cold weather has me watching too many "waste of time" sporting events. She is correct of course, but sports and life have many paralells and here's my current favorite. I am believing more and more in the importance of Karma where good intent and deeds contribute to future happiness, and bad intent deeds contribute to future suffering. Hence, there is only one explanation for the dismal Denver performance yesterday. Denver had simply way too much bad Karma. And here's why. They denied Patriot fans the opportunity for any tickets (not one) to the AFC championship game in Denver. This was a selfish, low class, and just down right mean. It created a tremendous reserve of negative Karma that could not be overcome Sunday. As a Pats fan, I was thrilled to see not just a loss but a record setting devastation.
When I stumbled across Bitcoin (or Bit-O-Coin, as my wife likes to call it) a few years back, my spidey sense started tingling. Since that time, I’ve made a few off hand remarks about the future of crypto-currency and received the expected “it’s another Dutch Tulip thing”. While I’m not an expert on the financial markets, I do have an excellent track record for identifying disruptive technology changes and I’ve concluded that crypto-currency is here to stay.
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.
Investment in clean energy in South Africa increased more in 2012 than in any other country, rising 206-fold to $5.5 billion, according to Bloomberg New Energy Finance. South Africa generates 85% of its electricity from coal, but chronic power shortages may have been the catalyst to look to solar (a low point in 2008 closed mines for five days). It’s making up for that gap with solar energy — and now it’s the only African nation among the top 20 solar markets, with installations comparable to South Korea, Thailand, and Israel.
The 360 days a year of sunshine certainly help, and it’s great to see the clean energy push work so well. But what is interesting to me is the amount of change in the overall economy the solar boom has caused. Wages are up, new jobs are available; hotels are adding more rooms, restaurants are changing menus to be more suitable for Europeans, and sales volumes are increasing. So I’m adding “changes to the energy infrastructure” to my list of events that require business agility. Changes in customer expectation, digital disruption, and shortening product life cycles get the most attention as change events that drive the need for companies to be agile, but as shown here change can rapidly come from infrastructure shifts. And South Africa is just starting its transformation. There are plans to invest in other forms of renewable energy: wind, concentrated and photovoltaic solar, landfill gas, and biomass power. And it looks like South African businesses are up to the challenge and are responding to the market. For more info, click here.
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:
Hubris: "Business value? Yeah, I know. Tell me something I don't know."
Blindness: "We do align to business needs. See, we are building a customer master for a 360 degree view of the customer."
Vanity: "How can I optimize cost and efficiency to manage and develop data solutions?"
Gluttony: "If I build this cool solutions the business is gonna love it!"
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]"
Begger: "If only we were able to implement a business glossary, all our consistency issues are solved!"
Educator: "If only the business understood! I need to better educate them!."
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.
There is a great deal of wildly divergent and sometimes seemingly fabricated information on the size of the US and global healthcare market. For 2014, here are the numbers that I will be using, with my sources, and assumptions and notes.1