#think2019 - CrowdChat

Learn from Cloud & AI Experts

IBM experts discuss a faster, more secure journey to cloud, how to accelerate your path to AI, and what you can learn at the upcoming Think 2019 event.

#think2019 @ericminick @ralphfinos @franklybriana @plburris @rreinitz @lawtonbill @jim_farrow @ramvennam @lori_pb @jeff_brent @bobspory @ankita03pandey @pwmccaffrey @wikibon @nottycode

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Establishing a secure connection

How is your enterprise modernizing your data and integration architecture to accommodate a growing mix of clouds, SaaS, and traditional data sources on and off premises? https://www.crowdchat.net/s/45sdy

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Peter McCaffrey

by evolving to an "Agile Integration Architecture" that rethinks people, process, and technology. Learn more : https://www.ibm.com/cloud/integration

IBM Cloud Integration
Learn how IBM Cloud Integration — including cloud integration services, hybrid cloud integration and cloud data integration — help you access and use critical data with API, application, message and data integration.

The key is implementing an IA architecture that enables moving code to distributed data.

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guidance available modernizing data & data as a differentiator https://www.ibm.com/cloud/garage/architectures/dat...

https://www.ibm.com/cloud/garage/architectures/dataAnalyticsArchitecture

I'm seeing many companies moving their ECM content into the public cloud as part of their modernization strategy. Check out the Business Automation Content Services on Cloud details in the Digital Business Automation sessions at Think.

There's another factor to consider here, the scores of real-time transactional government tax mandates that require the digitaltransformation of core financial solutions to keep up with the digitization of tax. Hence the first MarketScape on the topic released this week

...MarketScape on the topic this week.

Are protection and compliance regimes built into your analytics systems, or bolted on? Why? https://www.crowdchat.net/s/75rh7

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I see it as a never-ending journey. One is never done. There is a legacy to begin with, but every moment, new data (sources) may get added to your current landscape. Fun!

Here's what Scott Hebner, IBM, had to say recently about applying comprehensive policies, compliance, and protections to data: Three critical steps in making your data *#AI ready*

Scott Hebner, IBM | Change the Game: Winning With AI
"I mean I think it all really starts with making your data simple and accessible. Which is about collecting the data. And doing it in a way you can tap into all types of data, regardless of where it …

Data privacy regulations are coming whether we like them or not. GDPR is already here, CCPA is coming soon. Enterprises, small and large, have to starting thinking about the data being collected and shared.

Anantha Narasimhan

@jameskobielus @SLHebner +1

#analytics systems typically have several layers of protection and compliance regimes. accessing the platform is at a system level whereas anonymizing data depends on the data set (as well any contracts associated with it)

Early days for establishing compliance and protection policies. It will probably need a company to have a Wall Street Journal disaster to focus minds on this issue!

How policy-driven are your data analytics visibility, detection and reporting activities? https://www.crowdchat.net/s/45rgs

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To ensure unbiased AI models, policies on data analytics are more important than ever.

very little to be honest & I think this is huge issue given increased and diverse regulations , GDPR being the latest

How important are good policies if their ratification is not automated? Deep integration across the analytics 'stack' can solve for that

I would love to see poll on this one. Every CDO would want to say YES to this. Need ML/AI based solutions to automate these activities, and we in IBM analytics have solutions to make this EASY (a hard problem)

thee's always a policy, but the restrictions depend on the purpose associated with how the data is being used. When my #datascience team built models for negotiation purposes, even our internal status reports listed our work as confidential.

Policy driven will be a very important portion of a machine driven future. Getting policy down and having machines figure out new policies on the fly address both on demand AI and real time AI

ML and AI are next frontiers in Data Governance Platforms and these models will work in conjunction with policies! So it’s “policy driven ML enabled” approach which seems most practical with the tools we have today!

Here is a session on Data Virtualization @ THINK 2019 that would be very valuable to attend : https://myibm.ibm.com/events/think/all-sessions/se...

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Here's what Daniel Hernandez, IBM VP, had to say recently about data virtualization on theCUBE: https://video.cube365.net/c/911319

Anantha Narasimhan4

@CAppugliese - What is the common misconception you hear about Data Science & AI?

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Carlo Appugliese

One of the biggest I've seen is that companies think they are behind vs other companies.. What companies need to understand is that its a journey and they just need to start. most companies are learning and growing in this space.

it's not a silver bullet and it's only as good as the people, clean data (as input), and tools. Another issue is the notion that it's easy. It's hard

Carlo Appugliese

I recommend, pick the one use case, put small team on the project and start. If it fails, that is normal. just goto next one and for the wins, it will cancel out many failed projects.

@CAppugliese Fully agree, Carlos. One should have the end in mind. But getting started is the first - and most important - step. Then 'hack' yourself fast forward ;-)

Anantha Narasimhan

Thanks, John.. do you see organizations putting emphasis on clean data?

I went to a seminar on IT security. The presenter showed us how easy to hack into the top 3 clouder venders in less than a minute. He said that believing cloud security is just like self-deceiving. Don't let cloud vendors fool you. I guess that his words have some truth. Thoughts

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Whoever ran that seminar could make a fortune selling their knowledge to those CSPs.

Which statement best describes your company’s data strategy?

Which statement best describes your company’s data strategy?

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Does your analytics strategy presume to move data to analytics or analytics to data? Why? https://www.crowdchat.net/s/35rfn

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Data Gravity rules! You bring analytics to data, that is the most optimal

Analytics to data. Any data movement or copying is expensive and leads to all kinds of issues (lineage, quality, latency, higher resource utilization and cost)

always move Analytics to Data .. that's been our mantra . Data gravity should dictate your strategy. Moving against the gravity means you would end up spending a ton of resources / money & is not sustainable

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Anantha Narasimhan

Definitely analytics coming to data - so faster decision can be taken at source or close to it

Especially the world of multi-cloud strategy this is critical that we keep data where it is, thus technology like data virtualization, having governance built in to trust the data drives to trusted AI

Anantha Narasimhan

Trusted.. and business-ready data

Here's what Hemanth Manda, IBM and James Wade, Guidewell had to say on theCUBE recently about moving data from the mainframe to the cloud for analytics: James Wade, Guidewell: Cloud-First Strategy Must Include Mainframe

Hemanth Manda, IBM & James Wade, Guidewell | Change the Game: Winning With AI 2018
"Our mainframe is still sort of a cloud-like infrastructure."

Carlo Appugliese

in my opinion, Do your analytics where the data is if you can.. There is no value in moving lots of data, but there is significant business value in doing more analytics with your data. Its all about rate and pace of AI projects..

Data is growing exponentially within an enterprise. Moving becomes an avoidable expense if you can bring analytics to your data!

distributed data for sure - it's all over the place

In-situ/in-database analytics is a key foundation fo the big data revolution. Data gravity. Now with the edge looming larger as a data source, analytics is moving closer to those nodes and getting more sophisticated there. Distributed AI.

Data in volume is costly to move & takes a lot of time. Data loses value over time. so, it is usually much cheaper to move code to data than data to code. This is especially true for operational AI/analytics, which should be moved close to data source where possible.

Anantha Narasimhan

btw, there is an exciting session on Data Modernization strategy in a Multicloud World - by Madhu Kochar

Anantha Narasimhan

https://myibm.ibm.com/events/think/all-sessions/se...

@Matthias_Funke How much of a trend is IBM seeing toward distributed edge/client-based AI model training? What do your solutions offer to support that practice for mobile, IoT, edge, embedded apps?

Anantha Narasimhan

And another on Data Virtualization: https://myibm.ibm.com/events/think/all-sessions/se...

In my experience, companies already collecting data find value in turning data into analytics, whereas companies developing new products or services find more value in using analytics for data. The best #datascience teams needs to find the balance in doing both

It is interesting to observe that when AI systems are deployed, 90%+ of the code is in operational AI, rather than ML model development.

check this out: https://www.ibm.com/analytics/data-virtualization?...

@hkmanda Agreed. But what is the tipping point when the abundance of high-performance parallel compute in the cloud makes it faster and less costly (even with bandwidth costs/constraints) to do high-powered AI (training etc.) there?

@AnanthRN For more on business-ready data, don’t miss the Digital Transformation: A Business Ready Data Hub for Advanced Analytics session at Think 2019 happening Friday, February 15th at 9:30amPST on the Data & AI Campus.

depends! When doing #ML #AI, for compute intensive scenarios like human genome sequencing take data to compute. For data intensive scenarios (especially input), bring compute to data. #rethink

@madhu_kochar not always, see my response:)

Many #businesses are organized as #functional, #multidivisional, or #matrix. Hence, #data siloes naturally happen and continue to happen. Bringing analytics to data wherever it resides can be more economical. Move data when it is essential like for #protection.

Yes. Impact of GDPR CCPA is huge. More dangerous is about the hacker abuse of AI power.

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Anantha Narasimhan1

When do you think voice-activated Natural Language feature (like Alexa, Google Assistant) will start to move in, into corporate environment?

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It's already there. I've seen an upswell on conversational UI's in business apps, with voice activation a growing piece of that.

Carlo Appugliese4

Here is a video of Sreesha from Niagara Bottling explaining their experience on our approach to kick start your AI Journey.
https://www.youtube.com/embed/hv1QvKuwV7k?start=70...

Sreesha Rao, Niagara Bottling & Seth Dobrin, IBM | Change The Game: Winning With AI 2018 - YouTube

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Carlo Appugliese

AI is so much more than just using ML tools.. or hiring a data scientist.. To win with AI, its a combination of having the right skills, tools and process/ culture.

It is also critical that you are able to TRUST data.

Trust is critical!

Carlo Appugliese

@madhu yes, Trust in your models and that the system is making right decisions is critical.. Good point..