Michele Goetz

The Top 6 Questions To Succeed At Artificial Intelligence

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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?

These questions put artificial intelligence into the traditional analytic processes and technology adoption box. These questions assume you will begin from the same starting point as you did for big data. You are wrong: Artificial intelligence starts with the problem to solve and works backward.

To succeed at artificial intelligence you need to ask the right questions:

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How Bad Data Management Kills Revenue

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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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How Bad Data Management Kills Revenue

by

Not one to normally publically 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 dicussions 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 intp 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 viola! 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 no where 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 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

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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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Semantic Technology Is Not Only For Data Geeks

by

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

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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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Agile Development And Data Management Do Coexist

by

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?

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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

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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 busi…

Let’s Break All The Data Rules!

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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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Is Zombie Data Taking Over?

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It is easy to get ahead of ourselves with all the innovation happening with data and analytics. I wouldn’t call it hype, as that would imply no value or competency has been achieved. But I would say that what is bright, shiny, and new is always more interesting than the ordinary.

And, to be frank, there is still a lot of ordinary in our data management world.

In fact, over the past couple of weeks, discussions with companies have uncommonly focused on the ordinary. This in some ways appeared to be unusual because questions focused on the basic foundational aspects of data management and governance — and for companies that I have seen talk publicly about their data management successes.

“Where do I clean the data?”

“How do I get the business to invest in data?”

“How do I get a single customer view of my customer for marketing?”

What this tells me is that companies are under siege by zombie data.

Data is living in our business under outdated data policies and rules. Data processes and systems are persisting single-purpose data. As data pros turn over application rocks and navigate through the database bogs to centralize data for analytics and virtualize views for new data capabilities, zombie data is lurching out to consume more of the environment, blocking other potential insight to keep the status quo.

The questions you and your data professional cohorts are asking, as illustrated above, are anything but basic. The fact that these foundational building blocks have to be assessed once again demonstrates that organizations are on a path to crush the zombie data siege, democratize data and insight, and advance the business.

Keep asking basic questions — if you aren’t, zombie data will eventually take over, and you and your organization will become part of the walking dead.

To defend your business from zombie data, read:

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