Welcome!

@CloudExpo Authors: Liz McMillan, Zakia Bouachraoui, Yeshim Deniz, Pat Romanski, Elizabeth White

Related Topics: Microservices Expo, Java IoT, PowerBuilder, Artificial Intelligence, @CloudExpo, SDN Journal

Microservices Expo: Article

The Other Shoe Drops: SAP Puts ERP on HANA

Taking HANA to Broadway

This guest post comes courtesy of Tony Baer's OnStrategies blog. Tony is senior analyst at Ovum.

By Tony Baer

It was never a question of whether SAP would bring it flagship product, Business Suite to HANA, but when. And when I saw this while parking the car at my physical therapist over the holidays, I should’ve suspected that something was up: SAP at long last was about to announce … this.

From the start, SAP has made clear that its vision for HANA was not a technical curiosity, positioned as some high-end niche product or sideshow. In the long run, SAP was going to take HANA to Broadway.

SAP product rollouts on HANA have proceeded in logical, deliberate fashion. Start with the lowest hanging fruit, analytics, because that is the sweet spot of the embryonic market for in-memory data platforms. Then work up the food chain, with the CRM introduction in the middle of last year – there’s an implicit value proposition for having a customer database on a real-time system, especially while your call-center reps are on the phone and would like to either soothe, cross-sell, or upsell the prospect. Get some initial customer references with a special purpose transactional product in preparation for taking it to the big time.

There’s no question that in-memory can have real impact, from simplifying deployment to speeding up processes and enabling more real-time agility. Your data integration architecture is much simpler and the amount of data you physically must store is smaller. SAP provides a cute video that shows how HANA cuts through the clutter.

For starters, when data is in memory, you don’t have to denormalize or resort to tricks like sharding or striping of data to enhance access to “hot” data. You also don’t have to create staging servers to perform ETL of you want to load transaction data into a data warehouse. Instead, you submit commands or routines that, thanks to processing speeds that are up to what SAP claims to be 1000x faster than disk, convert the data almost instantly to the form in which you need to consume it. And when you have data in memory, you can now perform more ad hoc analyses. In the case of production and inventory planning (a.k.a., the MRP portion of ERP), you could run simulations when weighing the impact of changing or submitting new customer orders, or judging the impact of changing sourcing strategies when commodity process fluctuate. For beta customer John Deere, they achieved positive ROI based solely on the benefits of implementing it for pricing optimization (SAP has roughly a dozen customers in ramp up for Business Suite on HANA).

Supply chain lag time

It’s not a question of whether you can run ERP in real time. No matter how fast you construct or deconstruct your business planning, there is still a supply chain that introduces its own lag time. Instead, the focus is how to make enterprise planning more flexible, enhanced with built-in analytics.

But how hungry are enterprises for such improvements? To date, SAP has roughly 500 HANA installs, primarily for Business Warehouse (BW) where the in-memory data store was a logical upgrade for analytics, where demand for in-memory is more established. But on the transactional side, it’s a more uphill battle as enterprises are not clamoring to conduct forklift replacements of their ERP systems, not to mention their databases as well. Changing both is no trivial matter, and in fact, changing databases is even rarer because of the specialized knowledge that is required. Swap out your database, and you might as well swap out your DBAs.

There’s no question that in-memory can have real impact, from simplifying deployment to speeding up processes and enabling more real-time agility.

The best precedent is Oracle, which introduced Fusion Applications two years ago. Oracle didn’t necessarily see Fusion as replacement for E-Business Suite, JD Edwards, or PeopleSoft. Instead it viewed Fusion Apps as a gap filler for new opportunities among its installed base or the rare case of greenfield enterprise install. We’d expect no less from SAP.

Yet in the exuberance of rollout day, SAP was speaking of the transformative nature of HANA, claiming it “Reinvents the Real-Time Enterprise.” It’s not the first time that SAP has positioned HANA in such terms.

Yes, HANA is transformative when it comes to how you manage data and run applications, but let’s not get caught down another path to enterprise transformation. We’ve seen that movie before, and few of us want to sit through it again.

This guest post comes courtesy of Tony Baer's OnStrategies blog. Tony is senior analyst at Ovum.

You may also be interested in:

 

More Stories By Tony Baer

Tony Baer is Principal Analyst with Ovum, leading Ovum’s research on the software lifecycle. Working in concert with other members of Ovum’s software group, his research covers the full lifecycle from design and development to deployment and management. Areas of focus include application lifecycle management, software development methodologies (including agile), SOA, IT service management/ITIL, and IT management/governance.

Baer has been a noted authority on software development platforms and integration architecture for nearly 20 years. Prior to joining Ovum, he was an independent analyst whose company ‘onStrategies’ delivered software development and integration tools to vendors with technology assessment and market positioning services. He also led Computerwire’s CIO Agenda and Computer Finance end-user best practices research services.

Follow him on Twitter @TonyBaer or read his blog site www.onstrategies.com/blog.

CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.