Win With AI_ IBM Cloud Private for Data & Watson Studio

The IBM Win With AI Summit was a powerful event for IBM Business Partners and AI practitioners. Watch the video to learn what’s new with IBM Watson Studio and the all-new IBM Cloud Private for Data. If you’ve ever wanted one view of your customer or data management with strong governance built in, you’ll want to learn more.

Try Cloud Private for Data for free.

Try Watson Studio for free.

FTC Disclosure: I am an IBM Champion and my company, Trust Insights, is a registered IBM Business Partner. Should you purchase something from IBM, I will receive indirect financial benefit.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode it look back at the win with AI summit full disclosure, I am champion of my company is a IBM bridge to business partners. So we do receive financial benefit. If you do happen to buy something from IBM,

the one with AI summit was a combination event. The first half of it was for IBM business partners to understand what was coming down the pike.

And then the second half was sort of a public event where people could could see and hear about some of the use cases. There were two product lines that IBM focused on. And I think both of them have profound positive implications for anyone who is a data driven marketer or well, frankly, for anyone who’s data driven period. The first product line is

Watson studio there have been a number of advances since studio deployed in March. I’ve been using it since March when it was announced at IBM think. And I like studio. It’s a good environment, especially if you want to be using modelers rather than writing code. So one of the main advantages of studio is that if you know the architecture that you’re going after, whether it’s statistical in nature, and we’re using the SPSS modeler, or it is machine learning and deep learning in nature, we using the neural network model as long as you know what you’re doing conceptually, you can drag and drop the pieces of the statistics you want to do

right in the interface. You don’t have to write code and then you just plop the output into the system and feed your data in and it does its thing and you can’t with results, which again is a great way for somebody who doesn’t necessarily know how to

To write code and our or Python to be productive in machine learning, deep learning statistics, etc.

But

most marketers don’t need the full power of public cloud, which is the cloud environment in which Watson studio lives. And, and there are a few quirks in the system here and there, but particularly when you’re moving files in and out, or you’re moving in large amounts of unstructured data.

But one of the major announcements was at Watson’s to us coming to the desktop as a desktop application,

and it will function as a native app, which means that it’s not just a window that goes to the browser version, which is fine with the browser version, but it will actually be full native application with the ability to use files locally, which makes me very happy because again, that’s much faster. You can use it like you with the same ease of use that you’d use a spreadsheet application.

Or that you would use your email on your desktop. And that that’s phenomenal. Because I look forward to being able to roll that out to

to customers to the team at trust insights and to

partners to say, Hey, we built this model using this combination of techniques.

And while the model me proprietary, if everyone’s on the same platform, we can pick the model up and move it around. And even if a customer doesn’t necessarily have studio desktop, because it’s it’s all one code base

will be able to take that model and just give it to them to run in the cloud environment.

The net benefit of this is faster algorithms, faster machine learning models, faster deep learning models, it’s

fewer errors because again, you’re not slinging rock hold you slinging.

abstracting code in in models and

for portability, being a being able to get to answers and and let other people who are not hardcore coders get two answers to. So I think it’s gonna be a major leap forward for the platform. It’s going to also normalize stuff like the data science experience and and make it all one uniform experience which will be good.

The other thing that was really interesting about the event from the Watson studio perspective is that I actually got to sit down with the Distinguished Engineer who built satchel port part of it, which is something that doesn’t often happen and it’s one of the things I love about about working with IBM is that you get to talk to the people who are actually making the product and say, Well why does it do this and

showed the the engineer one thing less the last night and he’s like, Ha.

I shouldn’t do that, give me an email said a book report on this,

which is fantastic. Because we all we want the product to be better. We want the product to be usable by everybody. We want our customers and our clients to be able to use the product as well and and iterate on what we do. So Watson studio a lot to look forward to there. And

again, I’m really looking forward to that desktop app. The ability to to share my work and my ideas with my team and have them makes knowledge just make suggestions but actually be able to jump in and reorder concepts

that way they can spend their time on the strategy and the implementation and not the How do I code this? What’s the function for this? Am I missing a semi colon here?

things that get in the way of right now

the second major announcement

Was

it really was around the they call cloud private for data

which is an abstraction layer for those for those folks who have been in the IBM ecosystem for a while, as developers

and no blue, what we should call blue mix is now just called IBM Cloud. There was a service debuted

two years ago, I think called Blue mix Lyft which said, Let’s take your databases wherever they are, and pick them up and move them into the cloud. And it was a relatively quiet product. And not too many people knew about it.

But what IBM has done since then is convert this thing to this thing called Cloud private for data, which is an abstraction layer that

allows you to have a cloud like environment for your databases, but it doesn’t require you to pick up the move of the data.

basis because for a lot of companies, particularly ones in highly regulated industries, you can’t move your database into the cloud, you are forbidden to do. So

there may be geographic restrictions, they may be security restrictions, and so on and so forth. So wouldn’t it be great to be able to, to have an environment in which you simulate that the benefits of it, the being able to access all of your data at once in one spot, but not have to

not have to deal with the regulations and that’s what cloud private for data promises in the marketing world. There’s this concept and there’s some companies that that do this called a customer data platform. And the idea being that your customer data is everywhere, right? You’ve got social media data, that email data, you’ve got CRM, data, marketing, automation data, you’ve got all this stuff in all these different systems all over the place. And because all the systems are

scattered everywhere summer on premise like your CRM or your P system some of them are in the cloud some of them are not even really databases like you know your Twitter analytics yet Twitter has the base but you don’t

because everything is everywhere you don’t really have a good sense of

your customer right you the the promises called like one view of the customer or a 360 degree view of the customer which has been a popular thing for a number of years

these companies you CBP platforms promised to bring all that in, use their proprietary mix of machine learning data whatever to try and and unify those customer records and

and give you a like, Hey, here’s the thing but

there are issues with obviously regulation with compliance.

With governance with data governance around these systems, and they’re not, they’re not cheap for the ones that are hosted services. And they’re not easy for the ones that are on premise services or, or private cloud services.

I was working with one customer not too long ago, looking at the inner workings of their platform they built themselves

it was a little messy

and it wasn’t working really well. And

what they needed was strong data governance,

the ability to say like, let’s,

let’s set some rules about this data, where it came from, how it can be used, how it should be used, and what the logical outputs are.

What cloud private for data does is essentially that it’s a it’s an essentially it’s an ingestion engine.

It is a unification service that you configure the rules for us a this is related to this

powered by machine learning and and some deep learning because one of the things it does is it tries to do use deep learning to to build semantic relationships between tables so if one table has Chris pan and the other table has Christopher pen

he understands is a semantic relationship between the two they’re they’re closely related and will auto suggest Hayden you should probably link these things together

and

it will also brings in natural language querying. So

if you instead of having to write sequel statements all there’s nothing wrong with sequel statements. I like them.

Your average non technical user is not someone who is fluent in in the sequel language, the structured query language they are good at asking.

Regular questions like how many people signed up for our services yesterday? That would be the kind of question that a business user would ask. And cloud private for data allows you to ask regular normal questions as well as, as see all your data in real time.

What is I think is so powerful about it is the governance you write rules in the system

that govern things like data sovereignty? Where did this data come from? And where is it allowed to go?

What is or is not protected health information, personally identifiable information, sensitive personal information, all the different categories that we have to deal with as marketers and as business people to say, like, yes, we can use this data. No, we can’t use this data. Where can the data transit so

if you are still working on your GDPR compliance, you need to know like Yes, I can.

will permit the state to move to hear from here? No, I will not permit the state of to go here. So for example, if you are working with you folks, people within the European Economic Area, you have a very specific set of rules about what you may and may not do with that data.

And cloud private for data allows you to write those rules. But then it converts those rules into into system policies that you can auto manipulate the data with. For example,

if you are using data for machine learning to better understand the customer and do forecasting and you’ve got this huge data set from the EU

unless the consumer explicitly gave you permission to use that data for machine learning. You have to de identify you have to anonymize it

right now. I can’t think of a single

A customer data platform service that will do that in compliance with the rules but still permit you access to the models in cloud prior for data. You set up the policy, you set the rule. And the rule triggers the software to D anonymize all the personal identifiable columns. And

even with permission list may or may not even allow certain people within your organization, even access to the data. But your data science team or your marketing data team would get the de identified data set. They would not get the raw data set.

And then they could do machine living predictions on stuff that is not personally identifiable like time on page or number of tweet replies, whatever, but they have no idea who the person is. And so the data is then being used in compliance with GDPR and

and still not inhibiting your ability to do business. One of the quotes I heard yesterday which I thought was phenomenal was

the more

sensitive and personal the information is the less useful it is from Sheila me because you’re not you’re not able to draw strong trends and forecasts and predictions on uniquely individual things.

If you had my

my social security number and there is zero predictive power in that number, but knowing that I am

identify as male, that’s a much bigger broader aspect and that is probably a better predictor and my social security number so the less personal data you have

you’re probably better off removing that data anyways so having cloud pirate for data either mascot DNR de identified or or prohibit access to it is going to be a good thing.

And then of course being IBM you can take all the data that you do build in cloud private for data and pass it to things like Watson studio for

That modeling for that advanced machine learning and deep learning, and so

having the to work together, but still, the quote was yesterday move your AI to your data rather than trying to move your data to your AI, move your data, your data, make the I live close to the data.

That’s a important idea because

it lets you and it lets the people who work in your organization, do the analysis where the data lives, not screw up the the data and

really apply that strong governance to it, but

all the identification, the quality of the data,

and so

wrapping up

these two products cloud prior for data, which unifies your data and it’s going to take you some work it’s not a magic wand it will take you some work to get it to get it implemented well, but cloud private data and then Watson studio

Then the Watson machine learning platform and sort of the machine learning engine

really paint a picture of

you being able to take your data

cleanup, manipulate it, prepare it for machine learning, pass it to Watson studio, and then you being able to drag and drop your way to working models. As long as you know you’re doing conceptually, it will save you a substantial amount of time in and then be able to immediately hand that off for business usage.

It’s

the best analogy I can think of is that IBM has significantly declutter your kitchen, right? Instead of having 44 different types of frying pan and that’s 37 spatula. You’ve now got maybe a half dozen of each and

it’s easier to find things it’s easy to do things you do still need to know how to cook

but the kitchen is much neater

The kitchen is much easier to use than it used to be.

So

I extend my thanks to the business partner team to the social VIP Team into the champion team at IBM for having me at the event and for letting me and my my fellow partners and stuff, work with the software

actually do hands on and

let us talk to the people who are building the thing and asked very, very technical questions of them.

And I would strongly recommend that if you are interested in

data unification

if you are interested in machine learning that you try out these services as a registered business partner trust insights is happy to

make whatever introductions need to be made

to an IBM team and will help you build the models if it’s appropriate to do so.

But there’s so much to be optimistic about. There’s so much to be excited about with

where machine learning is going, where AI is going and how much easier it’s getting and how much more accessible it’s getting for everyone, not just for the the super technical folks. So I hope you give it a try. And as always, if you have questions or comments about

this, leave it in the comments on on wherever it is you singles video or reading the transcript to listen to the audio and please subscribe to the YouTube channel, my newsletter and I’ll talk to you soon. Take care

if you want help with your company’s data and analytics visit Trust Insights calm today and let us know how we can help you


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