#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

LeaderBoard

Stream Ended

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

[+] Show Hidden Comments

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.

(edited)

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.

Sarbjeet Johal1

What are the best practices for executing a successful Data Governance Program? Please share recommendations/resources from IBM in this regard!

[+] Show Hidden Comments

Come join us at THINK for the following sessions https://myibm.ibm.com/events/think/all-sessions/se...
Data Governance & Data Lineage at Hartford: https://myibm.ibm.com/events/think/all-sessions/se...

Anantha Narasimhan

One more - Cognitive Data Governance: https://myibm.ibm.com/events/think/all-sessions/se...

Anantha Narasimhan

Here's a blog you might find useful: https://www.ibmbigdatahub.com/blog/5-data-governan... . Please let us know if we can help in any way

5 data governance lessons from gardening
If you don’t know what data you have, how can you manage it effectively and generate value from it?

How are your analytics users using data visualization and low-code development tooling? https://www.crowdchat.net/s/55rj2

[+] Show Hidden Comments

Anantha Narasimhan

based on prior experience, when we want to accelerate self-service analytics, low code/no code become important

Increasingly, analytics developers are using declarative, visual, low-code tooling to program AI/ML, with the tooling leveraging auto-ML to compile models for optimized execution on target platforms.

I see two categories of analytics users: Data Scientists using dev tooling like jupyter notebooks and OSS visualization libraries, vs LoB users using canned reports and dashboards.

Analytics business users are also using self-service, visual tooling to build predictive and other advanced analytics for decision support--eg Cognos.

I tried using Tableaux, but gave up after a few days. Nothing beats Cognos especially after the latest improvements in 11.1

Anantha Narasimhan

with Cognos Analytics 11.1, Business Users can use natural language queries to get insights into data.. and stunning visualization to clearly state trends or issues (sorry - shameless plug in) :)

ML-driven augmented programming, leveraging low-code visual front-ends, is a huge research focus here at Wikibon. See my report from a year ago: https://wikibon.com/augmented-programming-brings-m...

I find more teams are using #datavisualization across an organization ranging from creating a realtime dashboard for the c suite to using it as a a tracking tool for day to dat operations.

Carlo Appugliese

One my favorite visuals is chord plots... :)

(edited)

Here's a great session that will showcase the new capabilities in IBM Cognos Analytics 11.1 and how it uses AI to provides smarter self-service analytics: https://myibm.ibm.com/events/think/all-sessions/se...

https://myibm.ibm.com/events/think/all-sessions/session/3651A

What companies in your opinion are great @ monetizing their data ( e.g. Uber, Facebook ) .. As economist rightly pointed out, Data is the new oil https://www.crowdchat.net/s/75riu

(edited)

[+] Show Hidden Comments

I believe it’s Google and Amazon! Facebook blew it! Facebook is an example of what not to do. When you democratize data through API you want proper mechanisms to control the misuse.

@sarbjeetjohal I agree on FB .. they got greedy & blew it big time

(edited)

Anantha Narasimhan0

Good blog by @AliyeErgulen on how 3 organizations are using Business-ready data to drive transformation: https://www.ibmbigdatahub.com/blog/3-business-expe...

3 business experts, 3 use cases on business-ready data
Most businesses, independent of their business model, are concerned with compliance and profit. The business must comply with the law, regulations and conduct guidelines, and to be sustainable, the...

[+] Show Hidden Comments

Carlo Appugliese0

https://myibm.ibm.com/events/think/all-sessions/se...
Here is sessions at #IBMThink where Banco Macro will go into detail about their success leveraging AI to anticipate their customer’s needs.

https://www.ibm.com/blogs/business-analytics/rock-star-ibm-data-science-elite-team/

[+] Show Hidden Comments

What resources help your enterprise deploy models anywhere, securely? https://www.crowdchat.net/s/35ri8

[+] Show Hidden Comments

The core platform that enables enterprises to deploy models anywhere is a data-science CI/CD toolchain that can serve to any target device, node, hardware, container, and runtime environment. The "securely" requires tight access and integrity controls throughout.

the best resource for deploying models anywhere, securely is a IT or technology team that is knowledgable, experienced and responsive!

John Thomas, IBM, had a good discussion of operationalized deployment of ML models recently on theCUBE: #MachineLearning use case: augmentation of call center operations

John Thomas, IBM | Change the Game: Winning With AI
"So think of this, if you have machine learning models, supervised models that can predict the intent, the reasons, et cetera, you can have them deployed operationalize them, so that when a call come…

End-to-end security from development, deployment, and updating is important, and not yet at all common!

A built in governance for these models is critical as well.. so you really need data engineers, data scientest, data stewards need to colloborate

Carlo Appugliese

Using Watson Machine learning really gives you ability to train. deploy and monitor your models.. This really gives you model portability so you can train and deploy anywhere..

Data Governance Policies + Data Governance Skills + Stated Policies. That covers all people processes and tech aspects.

well said Sarbjeet

@madhu_kochar In my experience, IT and operations teams are very important when you need to confirm that certain governance is in place within an #analytics system or need a new policy to be put in place

Carlo Appugliese0

If your looking to build a new Data Science Team??
Here is a blog I put out on how to build a rock star Data Science Team!
https://www.ibm.com/blogs/business-analytics/rock-...

[+] Show Hidden Comments

How does your organization administer profiling, cleansing and cataloging of data? https://www.crowdchat.net/s/55rhr

[+] Show Hidden Comments

Anantha Narasimhan

this is perhaps the core of organization's journey to AI or even to a successful Data Lake, Data Science

Carlo Appugliese

In area of Data Science, typically we include a Data Engineer who work side by side with Data Scientist and are critical to take findings and put into Catalog as well as provide key features needed to modeling phase.

Anantha Narasimhan

there is an excellent session at THINK, hosted by Jay @jaylimburn - https://myibm.ibm.com/events/think/all-sessions/se...

it’s mainly done at LOB level in most of the companies I have worked with in advisory capacity. Central tools, policies and procedures need to be built for data governance. I believe the WHAT of data cleansing and cataloging must stay with LOB and HOW with IT.

as usual, there are multiple solutions too handle this, but ICP for Data is a platform that includes and enforces these capabilities by default .. Learn more @ this THINK session : https://myibm.ibm.com/events/think/all-sessions/se...

I have yet to see a organization that has this process streamlined. Most established companies have many, many meetings about how data set is going to be used internally and the logistics around it.

This an important requirement in the maturing of AI/advanced analytics. Solutions should support distributed and multi-cloud data, and ideally support orchestration and optimization of moving code to data or vice versa.

Carlo Appugliese

You need a combination of a cross frictional team, the right access to data and tools to build your AI foundation.

Anantha Narasimhan

some organizations refer to this as Data Preparation or Data Curation..

one of the advantages of building cutting edge tech and creating new data products/services is that this is dealt with further down the line

Besides Profiling, cleansing, cataloging, Data classification is another critical attribute. Here is where Ml automation can go a long way. IBM Information Server provides complete solution

Carlo Appugliese0

One the big areas we see in AI is ability to explain what your predictive models are doing and do you trust them.. Let me ask everyone, Do you trust the decision made by an AI/ML model?

[+] Show Hidden Comments

Carlo Appugliese

Model bias is something we are very focused on, especially from a dev ops perspective. Understanding this is important and critical to your organizations future as you incorporate key decisions using AI. So Trust AI but verify :)

Matthias Funke1

I'd really like to know what people see as their current most important challenge in leveraging analytics to drive business value. Can you share?

[+] Show Hidden Comments