IBM DataOps in Action Panel | IBM DataOps 2020
Channel: SiliconANGLE theCUBE
Date: 28/05/2020

Caitlin Halferty, Victoria Stasiewicz, and Steven Lueck talk with Dave Vellante for IBM DataOps in Action.

#IBM #DataOps #theCUBE

https://siliconangle.com/2020/05/28/expert-panel-embraces-dataops-as-key-initiative-for-collecting-analyzing-critical-information-dataops/

Expert panel embraces DataOps as key initiative for collecting, analyzing critical information

It’s unusual for surveys to report that 100% of respondents were all following the same path. Yet when 300 companies were recently asked by 451 Research LLC about converting data into business insight or DataOps, all 300 were either currently planning or actively pursuing DataOps initiatives.

The results are further confirmation that DataOps is a top priority in the enterprise and the need for more agile and automated data management has never been greater.

“There’s redundant, disparate data all over the place,” said Victoria Stasiewicz (pictured, center), data engineer at The Northwestern Mutual Life Insurance Co. “I need to tell you where the data is, tell you what’s trusted, and that way you can quickly access the information and bring back answers to business questions that is one answer not many. That’s the biggest challenge.”

Stasiewicz spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the IBM DataOps in Action event. She was joined by Caitlin Halferty (left), director of AI Accelerator and client success at IBM Corp., and Steven Lueck (right), senior vice president and director of data management at Associated Bank. They discussed IBM’s artificial intelligence-driven DataOps initiatives, a changing corporate culture mindset around accessing information, the importance of speed to insight, and overcoming inertia to achieve enterprise scale. (* Disclosure below.)

Value of infusing AI
One company that has built a DataOps practice and integrated its principles into product offerings is IBM. The company’s Global Chief Data Office is empowered to build a trusted data source and artificial-intelligence framework throughout the organization.

“We’ve been on this journey for about four years now,” Halferty said. “At the time we wrote our data strategy, it was about the cognitive enterprise. We quickly have pivoted to see the real opportunity and value of infusing AI across all of our major workflows.”

IBM pursued its DataOps strategy by essentially serving as its own guinea pig, developing tools for metadata curation while gathering feedback from employees. This experience led IBM to realize that when data managers were pulling in structured and unstructured data, there was a great deal of work involved to catalog it properly.

“Our data stewards were spending a lot of time manually tagging and creating business metadata about that data,” Halferty said. “We identified that it was a real pain point and costing us a lot of money and valuable resources. So, we started to automate the metadata and we pushed that capability out into Cloud Pak for Data.”

Cloud Pak for Data has emerged over the past two years as a platform designed to modernize how businesses collect and analyze information. The company recently updated the tool with new DataOps capabilities for automated planning, budgeting and forecasting.

“Cloud Pak for Data is really a one-stop shop,” Stasiewicz said. “That’s allowing quick, seamless access for a business user versus having them go into some of the previous versions that IBM had rolled out.”

Operationalizing with speed and scale
Access to the right tools is important, but data management leaders are also finding that DataOps is about creating a new cultural mindset as well. Associated Bank found that once the data has been virtualized or stored in a data lake, users could begin to get their arms around key information for building applications without embarking on a search mission to find it.

“That’s been the biggest culture shift for us, having that availability of data to provide insights as opposed to having the business or application owners ask for that data,” Lueck explained. “What we’re finding is that by providing the platform and the foundational layers, we’re getting the use cases to evolve. Let’s start to explore. That’s a data science mentality and culture.”

Another key outcome of DataOps is speed. For analysts to be told it might be months before important datasets are available is not conducive to the notion of an agile business.

Operationalizing data into a framework where it can be rapidly accessed in a usable structure highlights a key driver behind the DataOps movement as revealed in one survey: velocity of innovation.

...

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the IBM DataOps in Action event. (* Disclosure: TheCUBE is a paid media partner for the IBM DataOps in Action event. Neither IBM, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)