>>everybody, this is Dave Vellante. May 27th were hosting a crowd chat going on crowdchat dot net slash data ops. Data ops is all about automating the data pipeline infusing AI and operationalize ing ai and the Data Pipeline and your organizations, which has been a real challenge for companies over the last several years in most of the decade. With me is aljaz cannoli. What's changed? That companies can now succeed at automating and operationalize in the data pipeline. >>You're so right, David. As's faras. I remember myself in this industry data challenges that the bottlenecks are the bottlenecks. So why now? I think we can answer that one from three angles. People process technology. What changing people? What changes process will change with technology. Let me start with the technology part on the technology front. Right now. The compute power is they were rare and the cloud multi cloud artificial intelligence, Social mobile all connected and giving the power to the organizations to deal with these problems, especially, I want to highlight the artificial intelligence part, and I will highlight it with how IBM is leveraging artificial intelligence to solve some of the dormant data problems. One of the major major doorman problem is on boarding data. If you're unable to onboard your data fast, however beautiful factory the all the factor lines shining, waiting for data if you cannot. Onboard data fast, all dress is waiting. But what IBM did automated made metadata generation capabilities which is on boarding data leveraging artificial intelligence models so that it is not only on boarding the data but on boarding the data in a way that everyone can understand it. When data scientist looks at the data, look at the data. They don't stare at the data but they understand what that data means because it >>is >>interpreted into business taxonomy into business language in the fast fashion that is one the technology, the second part people and process parts so important in the process part the methodology. Now we have the methodologies, the first methodology that I would just say as a change. Sometimes we we call that as a legal I don't know whether you heard about it in an agile So these legal methodologies now asking us to how alterations fail >>fast, Try fast, fail fast, Try fast >>and these agile methodologies are now being applied to data pipelines in weeks, off iterations, we can look at the most important business challenge with the KP eyes that you're trying to achieve and then map those KP eyes to data sources needed to answer those KP eyes and then streamline everything in between passed. So that renders a change like this the market that we are in. Then all those data flows are streamlined and optimize. And during the Cube interview during the Cube program that we put together, you will see some of the organizations will mention that is agile practice they put in place in every geography is now even getting them closer and closer, because now we all depend on and >>live on digital. So I'm very excited because ah, interviewing Standard Bank Associated Bank. Harley Davidson, IBM chief data officer into public. Sorry to talk about how IBM is sort of drunk, its own champagne eating. It's own dog food. Whatever you prefer. This is not the the mumbo jumbo marketing. This is practitioners who are gonna talk about how they succeeded, how they funded these initiatives, how they did the business case, some of the challenges that they face, how they dealt with classification and metadata and some of the outcomes that they have. So join us on the crowd. Chat crowdchat dot net slash data ops on May 27th. Go there at your calendar. We'll see you in the crowdchat.