AI And Cognitive Computing — What's The Hype All About?

That is exactly what Forrester wants to find out - is there something behind the AI and Cognitive Computing hype?  What my research directors ask, "Is there a there there?"

AI and Cognitive Computing have captured the imagination and interest of organization large and small but does anyone really know how to bring this new capability in and get value from it? Will AI and Cognitive really change businesses and consumer experiences? And the bigger question - WHEN will this happen?

It is time to roll-up the sleeves and look beyond conversations, vendor pitches and media coverage to really define what AI and Cognitive Computing mean for businesses, are businesses ready, where they will invest, and who they will turn to to build these innovated solutions, and what benefits will result.  As such, Forrester launched its Global Artificial Intelligence Survey and is reaching out to you - executives, data scientists, data analysts, developers, architects and researchers - to put a finger on the pulse.  We would appreciate you take a little time out of your day to tell us your point of view.  

Simply click on this like to participate.  https://forrester.co1.qualtrics.com/SE/?SID=SV_3K02TU4Q9934Z1j

As a thank you, you will receive a complimentary summary report of the findings.

If you have a great story to share that provides a perspective on what AI and Cogntivive can do, what benefits is has provided your company, and can share you learnings and best practices, we are also recruiting for interviews. 

Simply contact our rock star researcher, Elizabeth Cullen, to schedule 30 minutes.  ecullen@forrester.com

We hope to hear from you!

Deep Learning Will Blow Up Your Data Strategy

Day one of the GPU Technology Conference in San Jose and I'm still glowing from watching Steve Wozniak "travel to Mars" through NVIDIA's photo real virtual reality.  Or, holding my stomach as Jen Hsun Huang, CEO of NVIDIA took us soaring over Everest.  Or cringing, as I watch the early attempts at a car teaching itself to drive and being reminded of how my 16 year old daughter is learning to drive (there were a few similarities...). Each emotion illustrates what everyone will experience shortly on NVIDIA's next gen compute platform with announcement for AI, VR, self-driving, SDK and new deep learning appliance.  

This is not your traditional or even big data analytic platform.  It's a complete overhaul of the computing architecture.  It's a complete rethink of data management. It will also change how you think about analytics.  

Stepping back from what may seem like hype and examples steeped in robotics, VR and infrastructure, the truth is, the announcements today show that deep learning in action is at most a year away, and as soon as now.  In addition, the innovation coming out of robotics, VR and infrastructure will allow introduction of new form factors and channels to engage with customers and shape our workforce. In the end, it is a data challenge for the very reason that for every channel we use and add, it always ends up being a data challenge.

The implications for how you manage data are radical. Here is what you need to think about:

  • Deep learning systems are voracious eaters of data. If you think you have volume issues now, it will only get worse. Traditional integration won't cut it.  You need bigger compute on GPUs not CPUs for speed, performance, and efficiency. Don't you want to train your data in 2 hours vs. 2 weeks?
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The Forrester Wave Master Data Management: Which MDM Tool Is Right For You?

The Forrester Wave for Master Data Management went live today. The results may surprise you.  

MDM tools today don't look like your father's MDM. No longer an integration hub between applications and DBMSs, today's tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.  

Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:

  • Business-defined views of data: For graph-based vendors such as Reltio and Pitney Bowes, master domains are shaped by business use cases. For example, customer master can be defined beyond the bounds of a household, identity, and account. Customer behavioral characteristics can be the starting points for taxonomies and hierarchies. Integration of master domains is based on physical, logical, linkage, and semantic schemas for a more seamless navigation and querying of master data to align with the explosion of data views created by analytics, applications, and microservices.
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The Top 6 Questions To Succeed At Artificial Intelligence

You can't turn anywhere without bumping into artificial intelligence, machine learning, or cognitive computing jumping out at you. Our cars brake for us, park for us, and some are even driving us. Our movie lists are filled with Ex Machina, Her, and Lucy. The news tells about the latest vendor and cool use of technology, minute by minute. Vendors are filling our voicemail and email with enticements. It's all so very cool!  

But cool doesn't build a business. Results do.

Which brings me to the biggest barrier companies have in adopting artificial intelligence. Companies are asking the wrong questions:

  • What is artificial intelligence (or insert: machine learning or cognitive computing)?
  • Where can I use artificial intelligence?
  • What tool can I buy?
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How Bad Data Management Kills Revenue

I'm not one to normally publicly gripe on a vendor, but a recent customer experience with an online purchase is a great example of why organizations can't ignore data management investments.

I have been a regular user of a note-taking app for several years. All my discussions with clients, vendors, and even notes from conferences wind up here. I put in pictures, screen shots, upload presentations, and capture web pages. So it isn't a surprise that this note-taking vendor wants to move me up into a premium version. And for $50 a year, it's not a big deal for me to do even if it just means I'm paying for more space rather than using all the features in the premium package.

So, this morning, I click the upgrade button and voila! My order is taken and shows up in my iTunes account order history.

As this app is web-, desktop-, and device-based and the vendor is born out of the app age, the expectation is that my account status should just automatically convert. I mean, every other business app I have does this. Why shouldn't this one?

As it turns out, my purchased premium service is nowhere to be found. To get immediate support, as only offered in premium service, you need to be able to log in as a premium customer. So instead of an easy and quick fix, I spend over an hour trying to get answers through a support site that shows the issue but an answer that doesn't work. I also see that this is an issue going back for over a year. I try entering in my issue through "contact us" only to find that I get routed back to the support forum and can't even log a ticket. I find an obscure post where the vendor's Twitter handle for support is listed and fire off a frustrated tweet (which goes out to my followers as well, which I'm assuming is not something this vendor would prefer).

So let's break down the data management issue:

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Semantic Technology Is Not Only For Data Geeks

You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.

It begins with a seemingly innocent set of questions:

"Is there a better way to master my data?"

"Is there a better way to understand the data I have?"

"Is there a better way to bring data and content together?"

"Is there a better way to personalize data and insight to be relevant?"

Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized. 

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Agile Development And Data Management Do Coexist

A frequent question I get from data management and governance teams is how to stay ahead of or on top of the Agile development process that app dev pros swear by. New capabilities are spinning out faster and faster, with little adherence to ensuring compliance with data standards and policies. 

Well, if you can't beat them, join them . . . and that's what your data management pros are doing, jumping into Agile development for data. 

Forrester's survey of 118 organizations shows that just a little over half of organizations have implemented Agile development in some manner, shape, or form to deliver on data capabilities. While they lag about one to two years behind app dev's adoption, the results are already beginning to show in terms of getting a better handle on their design and architectural decisions, improved data management collaboration, and better alignment of developer skills to tasks at hand. 

But we have a long way to go. The first reason to adopt Agile development is to speed up the release of data capabilities. And the problem is, Agile development is adopted to speed up the release of data capabilities. In the interest of speed, the key value of Agile development is quality. So, while data management is getting it done, they may be sacrificing the value new capabilities are bringing to the business.

Let's take an example. Where Agile makes sense to start is where teams can quickly spin up data models and integration points in support of analytics. Unfortunately, this capability delivery may be restricted to a small group of analysts that need access to data. Score "1" for moving a request off the list, score "0" for scaling insights widely to where action will be taking quickly.

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Are Data Preparation Tools Changing Data Governance?

First there was Hadoop. Then there were data scientists. Then came Agile BI on big data. Drum roll, please . . . bum, bum, bum, bum . . .

Now we have data preparation!

If you are as passionate about data quality and governance and I am, then the 5+-year wait for a scalable capability to take on data trust is amazingly validating. The era for "good enough" when it comes to big data is giving way to an understanding that the way analysts have gotten away with "good enough" was through a significant amount of manual data wrangling. As an analyst, it must have felt like your parents saying you can't see your friends and play outside until you cleaned your room (and if it's anything like my kids' rooms, that's a tall order).

There is no denying that analysts are the first to benefit from data preparation tools such as Altyrex, Paxata, and Trifacta. It's a matter of time to value for insight. What is still unrecognized in the broader data management and governance strategy is that these early forays are laying the foundation for data citizenry and the cultural shift toward a truly data-driven organization.

Today's data reality is that consumers of data are like any other consumers; they want to shop for what they need. This data consumer journey begins by looking in their own spreadsheets, databases, and warehouses. When they can't find what they want there, data consumers turn to external sources such as partners, third parties, and the Web. Their tool to define the value of data, and ultimately if they will procure it and possibly pay for it, is what data preparation tools help with. The other outcome of this data-shopping experience is that they are taking on the risk and accountability for the value of the data as it is introduced into analysis, decision-making, and automation.

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Data Governance and Data Management Are Not Interchangeable

Since when did data management and data governance become interchangeable?

This is a question that has both confounded and frustrated me.  The pursuit of data management vendors to connect with business stakeholders, because of the increasing role business units have had in decison making and holding the purse strings to technology purchases, means data governance as a term was hijacked to snuff out the bad taste of IT data projects gone sour. 

The funny thing is, vendors actually began drinking their own marketing Kool-aid and think of their MDM, quality, security, and lifecycle management products as data governance tools/solutions.  Storage and virtualizations vendors are even starting to grock on to this claiming they govern data. Big data vendors jumped over data management altogether and just call their catalogs, security, and lineage capabilities data governance.  

Yes, this is a pet peeve of mine - just as data integration is now called blending, and data cleansing and transformation is now called wrangling or data preparation. But more on that is another blog...

First, you (vendor or data professional) cannot simply sweep the history of legacy data investments that were limited in results and painful to implement under the MadMen carpet. Own it and address the challenges through technology innovation rather than words.

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Let's Break All The Data Rules!

When I think about data, I can't help but think about hockey. As a passionate hockey mom, it's hard to separate my conversations about data all week with clients from the practices and games I sit through, screaming encouragement to my son and his team (sometimes to the embarrassment of my husband!). So when I recently saw a documentary on the building of the Russian hockey team that our miracle US hockey team beat at the 1980 Olympics, the story of Anatoli Tarsov stuck with me. 

Before the 1960s, Russia didn't have a hockey team. Then the Communist party determined that it was critical that Russia build one — and compete on the world stage. They selected Anatoli Tarsov to build the team and coach. He couldn't see films on hockey. He couldn't watch teams play. There was no reference on how to play the game. And yet, he built a world-class hockey club that not only beat the great Nordic teams but went on to crush the Canadian teams that were the standard for hockey excellence.

This is a lesson for us all when it comes to data. Do we stick with our standards and recipes from Inmon and Kimball? Do we follow check-box assessments from CMMI, DM-BOK, or TOGAF's information architecture framework? Do we rely on governance compliance to police our data?

Or do we break the rules and create our own that are based on outcomes and results? This might be the scarier path. This might be the riskier path. But do you want data to be where your business needs it, or do you want to predefine, constrain, and bias the insight?

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