Multicloud and AI everywhere – Watson: Chapter 2 of Digital Transformation
Today’s IT environments are a mish-mash of legacy tools intertwined with new tools, each requiring devoted staff to monitor and manage them through individual panes of glass.
IBM Think 2019 accelerated the enterprise customer conversations about ‘chapter 2 of digital transformation.’ This new chapter picks up the digital transformation story with the majority of enterprises using multicloud deployments, and who now need the ability to modernize core /mission-critical business applications and leverage AI across the business.
The themes outlined by IBM CEO Ginni Rometty in her keynote neatly dovetail into chapter 2. Notably, AI everywhere – Watson running in any cloud, and the RedHat acquisition, which is poised to accelerate core business application modernization with containerized workloads running across both private and public clouds.
Three trendlines I observed during the conference reinforce the immediacy and importance of AIOps.
- Multicloud is here today and the future.
The ‘Lift and Shift’ approach to application cloud migration has proven challenging for many enterprise organizations. There is a shift taking place from moving everything to the cloud, to moving workloads between clouds and traditional platforms where they make the most sense from a business management and cost/benefit perspective.
In the multicloud world, we’re working with a non-deterministic state where systems turn up and down. Multicloud management plays a crucial role to know what service, cloud or container component is involved to determine what your IT and DevOps teams are responsible for and what your cloud vendors are each responsible for delivering.
- IT Operations must modernize or consolidate their toolsets.
One of the most-asked questions that I heard walking around the floor was “How do I get efficiencies of cloud at a price point that makes sense for me, and how do I start to migrate what I have today to this environment?” It’s a common question that centers around two things: cost and transition quickness.
The simple answer is to accelerate or modernize your efforts, but doing that is harder than it seems.
Today’s IT environments are a mish-mash of legacy tools intertwined with new tools, each requiring devoted staff to monitor and manage them through individual panes of glass. Adding to the complexity, you have legacy technologies that rely on a series of certifications following a command-based approach where a lot of today’s technologies are API driven and software driven and developer driven.
To fix the managerial nightmare, you have to get your staff up to speed quickly because you can’t hire enough people who have a complete understanding of these wide-ranging technologies. By updating or consolidating your toolset, it’s easier to accelerate the team’s efficiencies because processes are simpler and there’s a single pane of glass that monitors your ecosystem.
- Multicloud is all about the data.
IBM recognizes that integration of public and private clouds requires visibility and insight into cross-platform infrastructure and applications, which in turn requires a modern performance monitoring and management platform.
IBM Services point of view is best summed up in this SiliconANGLE theCUBE broadcast interview of IBM VP Joe Damassa and ScienceLogic CMO Murali Nemali. In their discussion, Joe Damassa articulates the importance of data by saying, “…software is great, but it’s all about software that collects the data, analyzes the data, and gives you the insights so that you can actually automate and create value for your clients.”
How to fix the data problem – IBM & ScienceLogic:
Data, by itself, is fine. But data that’s clean and actionable is even better – and that’s what we provide to IBM.
To support their analytics engines and Watson, IBM looks to ScienceLogic to build the data lake (in real-time) that collects and prepares the data to drive automation. ScienceLogic reduces raw operational data volumes through intelligent baselining and topology inference, upon which digital transformation initiatives can succeed. For deeper insights, read our whitepaper, AIOps: A Guide to Operational Readiness – It’s All About the Data.
As Murali Nemani said in the same interview, “When you think about predictive models and the way data can be applied to do things like anomaly detection that ultimately accelerate and automate operations, that’s where the relationship really takes hold.”
These three themes reflect why the IBM Services teams that are responsible for delivering Services for Multicloud Management chose ScienceLogic’s SL1 as their delivery platform for IT operations for hybrid and multicloud environments. As enterprises continue to accelerate their digital transformation, it’s increasingly probable that their plans will include using AI to move at a faster rate and not skip a beat.
Think 2019 has been an exciting event. The need for partnership ecosystems is only going to continue growing into the future, and we at ScienceLogic are excited to continue our work with IBM.