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Today’s enterprises are multi-cloud–with hidden costs.
The multi-cloud world offers innumerable benefits for enterprises, but only if they are able to manage it and prevent undue complexity and unexpected costs.
Anyone paying attention to enterprise technology in recent years knows well that cloud computing has been one of the most transformative innovations for businesses today. According to the 2018 IDG Cloud Computing Survey, nine out of ten companies will have some part of their applications or infrastructure in the cloud by 2019, and the rest will be there by 2021.
This highlight implied that this phenomenon was surprising or somehow unknown; however, this is not the case for ScienceLogic or me.
Over the past few years, we’ve seen a marked shift in strategy among our customers and an increased call from our partners that shows cloud is not only growing, but accelerating.
The study also found that enterprises are using on average four SaaS vendors, three PaaS vendors, and two IaaS vendors. In the next 18 months, IDG expected those numbers to grow to an average of five, three, and three vendors, respectively. Again, interesting but also unsurprising. Many of our customers have been multi-cloud for a while now – using say, AWS for application scale and storage, Microsoft for productivity, Google or IBM for ML.
However, while multi-cloud SaaS, PaaS, and IaaS deployments come with real cost savings and agility gains beneficial to the enterprise, they also frequently come with unrealized costs to monitoring and management that, if left uncontrolled, can wipe out many of those savings and efficiency gains. In the worst cases, complex multi-cloud deployments can introduce unwanted complexity that threatens the business’s very ability to function.
Regular readers of this blog may recall that a few weeks ago my colleague Russ Elsner dove into this issue before VMworld, detailing an impressive SL1 success story and use case with General Dynamics IT (GDIT).
In it, he outlined how GDIT consolidated the data from 23 tools into a single clean data lake, drastically reducing the time it takes to understand complex, multi-cloud problems and then find and implement automated solutions. The ability to run this type of in-depth analysis has helped GDIT move beyond a break-fix model, and towards one of preventative maintenance, reducing mean-time-to-resolution from four hours to just 15 minutes.
Furthermore, using SL1 for this consolidation granted operations teams the ability to better assess what and who is impacted by a change in just 15 seconds, a process that previously took 8 hours and 100 people. The multi-cloud world offers innumerable benefits for enterprises, but only if they are able to manage it and prevent undue complexity and unexpected costs.
If you’d like to learn more about how SL1 can help organizations grapple with the multi-cloud world, I encourage you to watch this webinar>.