think2019

Agreed - a key component of data management is anonymizing it!

We run into this concern which IMO is very valid, one of the key reasons why IBM is making a big bet on private cloud and multi-cloud - in addition to government agencies, we see this as being a concern @ banks, financial services, healthcare, insurance and others

I'd argue that in many ways, the security cloud provider apply to their data center infrastructure is better or more mature than what the individual user can achieve. But if you deal with sensitive data, keeping it behind your firewall yet accessible in a hybrid world is key.

Our mantra to secure to the core is part of our Hybrid Cloud...

This is gold to me. Having a catalog of data assets at my disposal without worrying about where data resides. Avoid or minimize data movement to avoid lag and cost is of tremendous value.

As I talk to multiple clients ,access to data especially dark data is critical. It is also important that they have good data virtualization story, meaning you do not always to move your data. Data from IOT devices, unstructured data is imp

the notion of being able to access all data sounds like a dream I had once... then I woke up and remembered I work with people and data is mess. The reality is we can have all the data in the world, but it's useless if it's not accurate or of poor quality.

It is essential to have multiple sources of data around key business processes, products and services. The quality of AI/Advanced Analytics will be dictated by the quality of the data sources.

In Data Science... The key to success is full access to all data.. In my experience, this is hot topic and there is a balance they have to play between Security and Innovation..

Capability to join your traditional data with IOT data, real time streaming data is critical to drive new analytics insights

Here's what Scott Hebner, IBM, had to say recently on theCUBE about the acute need to improve accessibility to enterprise data: Three critical steps in making your data *#AI ready*

My experience is full access to data for your Data Scientist and Data Engineers is critical to your business innovating.

@CAppugliese Is full access to all data across all subject domains that important to data science? Don't data scientists prioritize access to only the most relevant data to build and train their models? How much visibility is required?

@madhu_kochar > What's your favorite visualization example?

In a competitive market, there will always be businesses and both internal and external clients who want their data to be kept private if it provides an advantage. Being able to access the data I need when I need is more important than having access to all of it.

@jameskobielus To learn more about how you can build a proper data architecture to improve data accessibility, don’t miss The Road to AI—A Journey to Modernize Your Data Architecture session on Wednesday, February 13th at 3:30pmPST on the Data & AI Campus.

@jennjshin ...or too old!

here is a recent Forrester wave which goes into the value prop of an integrated platform: https://www.ibm.com/account/reg/us-en/signup?formi...

thanks for the link. just to clarify, I wasn't saying there isn't value in analytics platforms. I'm just pointing out that not every team will find it valuable.

One can get started using Excel or some open source tooling.. but once you scale in the organization or want to foster collaboration and operationalize a process or model, it becomes tricky.

True, but that's only the case if you have a user of a model that's created. the development team creates and improves these models, the deployment is a separate process in an enterprise setting and often implemented by a different team.

Very much so. Questions of AI agency are not being addressed at all well by the industry. https://siliconangle.com/2018/10/23/artificial-age...

AI, at the outset sounds scary, right? When we talk to customers, they are worried about losing sight of the "why" and feel that AI will build black boxes..

Absolutely critical that Data quality standards are set, Data governance is established and trust is infused in AI models so people can understand any bias and can explain WHY insights/decisions were made

In order to achieve this, you truly have to trust your data to increase the consumption. Having clarity of data lineage is critical..Do you agree?

That's a tough one as it is more than just a question of technology, but also requires a culture that facilitates rapid changes, empowers users and democratizes access to data and analytics.

Here's what Hemanth Manda, IBM and James Wade, Guidewell had to say recently on theCUBE about cloud providing flexibility, agility, and workload acceleration in the analytics ecosystem: IBM Thought Leader Hemanth Manda: Blending Agility, Acceleration, and Ecosystem to Catalyze Customer Value.

Let me list a few characteristics on top of my head: Cloud native design properties on the engine or technology to scale independently according to workloads needs. Be elastic, not just scale out, but back down.

From a #datascience perspective, you can increase the workload and consumption flexibility of any analytics system by improving your code to run your models more efficiently.

Minimize dependencies and complex data architectures where one needs to join different data sets, juggling various copies of the same data, or different versions.

The key to increasing flexibility in your AI and other analytics systems is to deploy it on a hybrid data architecture that fits data platforms (Hadoop, NoSQL, RDBMS, streaming, etc.) to diverse workloads, access, and consumption profiles.

.@plburris Our customers are shifting to cloud native architectures for their existing deployments of analytic systems. By modernizing the overall deployment, customers can take advantage of elasticity offered by the new cloud architectures.

For Workload, many clients now on AI journey are embarking on Deep Learning projects, these require a lot of compute. You need to understand the use case, setup the right architecture/ platform and just go.

@madhu_kochar Right. Clarity on data lineage is conducive to trusting the analytics for decision support. But trust alone is not sufficient for improving consumption of the analytics if it's irrelevant data. Data curation needs to be built into the pipeline as well to assure that