Pure SaaS Vs. Cloud Platforms: Understanding What Cloud Actually Means

Kevin Murray

I frequently find there’s more than a little confusion over what “cloud” actually means. So, before we get into the relative product offerings, I’ll quickly define terms. (Nothing so useful as a guide where you’re not quite sure what is being discussed.)

With pure software-as-a-service (SaaS) platforms, the application or platform itself is a cloud service – by which I mean that the infrastructure is made for the cloud. You can only interact through it with APIs, and rather than owning it, you pay to use the model and the platform sits in the cloud, where you interact or customize it as you wish.

By contrast, a cloud-hosted model takes a preexisting software application and offers a hosting service for it. This means, as with pure SaaS platforms, there is no need to set up the infrastructure. However, the platforms are modified for the cloud versions of an existing software.

Pure SaaS platforms

  • Hosting options: The platform is version-less. The version of the platform that’s up there is it. Rather than changing versions, the software vendor continually upgrades this single platform. This means, however, that new features are pushed to you, even if you didn’t request them or decide that you don’t need them. It might be that features are incorporated at the wrong time for your business.
  • Maintenance: Although new features are rolled out to you automatically, you still need to check that these new features don’t clash with your existing site. The need to regression test the changes still exists, and you have no choice about what time to undertake this effort, as you are not in control of when the new features are pushed.
  • New features: The roadmap delivery depth may not be as extensive. Although new features are released (typically) each quarter, these go out to a live environment, so there is a greater need to consider backward compatibility. The vendors also have to ensure that all their customers are not affected, which means that the rate of evolution may not be as progressive.
  • Hosting costs: Hosting costs are included. This is great for clarity, but can mean that you are paying for extra server capacity that you’re not using.
  • Capacity: Any peak utilization is managed by the cloud. You don’t need to worry about capacity.
  • Hosting options: Here you have flexible options about your hosting. Your platform is probably hosted on a private cloud, which means that you can tier different software hosting to suit your needs. The deployment architecture of your software can be customized more.
  • Maintenance: Some level of maintenance is now going to be required. This might be done through third-party application support, but potential issues, such as capacity planning, disaster planning, and so on, need to be considered.
  • New features: You can skip versions but you have to be wary – skip too many versions and the gaps between versions become too big and any future upgrade will be tough.
  • Hosting costs: You pay for whatever hosting you actually use – although this does mean that your hosting costs need to be considered. With this type of platform comes commercial considerations of what your hosting costs are going to look like.
  • Capacity: Infrastructure can provide more servers dynamically to provide capacity.
  • Customization: Software is fully customizable, as this type of platform allows for wider changes from the provider. Your cloud is your cloud, so you can make whatever changes to the software you want to suit your business.
  • Upgrades: Here, you manage when and if you want to upgrade. However, effort is still required on your side to ensure that there are no regressions or clashes if an upgrade is installed. You choose when to upgrade, but you still need to undergo a proper upgrade process to correctly onboard your new features.

Which platform is right for you?

Both pure SaaS platforms and cloud-hosted platforms have an impressive range of features. Depending on your business needs, either could work. The key is to make sure that you are informed about the choices that you’re making and the commercial considerations on the software offering. As with all impactful decisions, it is vital to have all of the information available and on hand when making your choice.

Learn more about application programming interfaces in Unleash the Killer API.


How To Get The Best Out Of Automation

Dr. Markus Noga and Sebastian Schroetel

In 2016, the management consulting firm McKinsey predicted that up to 70% of all tasks are potentially automatable with so-called next-generation technologies. Companies worldwide jumped on that bandwagon and invested heavily in one of the hottest innovations when it comes to automation – robotics.

Today, only a few months later, some experts claim that robotic process automation (RPA) has only been a fast-paced trend based on the excitement of industry leaders, and it is not the predicted “panacea” for all the challenges enterprises face regarding automation. Recently, another blog post by McKinsey tackled this topic and qualified the initial enthusiasm for bots and their supposed potential to incur all sorts of back-office processes. In fact, the rapid adaptation of robotization waived the consideration of its potential downsides.

According to McKinsey, “installing thousands of bots has taken a lot longer and is more complex than most had hoped it would be” and “not unlike humans, thousands of bots need care and attention – in the form of maintenance, upgrades, and cybersecurity protocols, introducing additional costs and demanding ongoing focus for executives.” All in all, the authors state that the economic results of RPA underperformed the estimations, especially with regard to cost reduction. The impression has been strengthened that “people do many different things, and bots may only address some of them.”

The next level of automation

Considering the latest trends in robotics that came along with unexpected complexity, little flexibility, and additional maintenance, automation is still pushing forward. The aim is to help enterprises realize their potential and switch focus from just “keeping the lights on” through human manpower to growth generation triggered by automation technology. A dedicated automation approach to achieve the intelligent enterprise involves three interacting levels:

  • Software components, or “engines,” that provide automation relying on highly specific process knowledge
  • Machine learning that involves teaching a computer how to spot patterns and make connections by showing it a massive volume of data – algorithms that can learn from experience without having to be explicitly programmed
  • Robotic process automation software that operates another application without the support of a human user, helping to run repetitive, rule-based monotonous tasks and bridging temporary gaps

In contrast to the mere RPA approach many companies have pursued in the past, only the integration of all three layers lifts the enterprise to the next automation level. Engines are the basis of enterprise automation activities. They enable companies to shape their processes by making decisions on where to direct incoming inquiries at subsequent steps. But engines have a fixed logic and limited configuration possibility. Therefore, they cannot cover all facets of the business processes and have the potential to only facilitate automation in up to 60% of all cases.

In credit management, for example, credit-rules engines can help evaluate personal creditworthiness and process credit limit applications in a structured way. This is done by automatically categorizing them based on defined scoring rules and assigning a specific credit limit to the customer after the examination is completed.

Applications for machine learning

But what happens when a scenario occurs that wasn’t encountered by the operator? By adding intelligent automation technologies to the automation portfolio, processes become noticeably intelligent. Machine learning can upgrade the automation level of a process up to 98%. How? By setting up general guidelines without telling the system exactly what to do. The underlying algorithm learns from the operator’s previous actions and takes all available data into account to deliver the most relevant response to an occurrence.

Applying this to credit management, machine learning is useful in those cases where a customer lacks a dedicated credit history. Here, machine learning fills in with more accurate forecasting models based on people’s overall payment history, on information related to the borrower’s interaction behavior on the lender’s website, and other unstructured data sets.

Robotics, as the third automation layer, can help automate the remaining two percent of repetitive, monotonous tasks in a process. But due to its lower integration level, RPA is limited in its reach and adds the percentage on top on much higher costs. In financial risk management processes like bank lending, robotics can deal with requests for overdraft protection or credit card approvals.

A genuine alternative to mere bot systems

Related to the downsides of bot systems, a multi-automation-layer approach is the way to set up a stable and holistic automation concept as an alternative to pure RPA to flatten or avoid the disadvantages bot systems entail.

The McKinsey authors support the thesis that robotics should be used in exceptional cases, instead of being applied as the universal remedy to deal with repetitive tasks.

All in all, enterprises are actively searching for ways to shape their processes and automate parts of their work. Robots are perceived as being too inflexible, expensive, and complex in their maintenance to accomplish these goals in a satisfactory manner. By expanding the automation portfolio with engines and machine learning, a meshing system of automation technology can address these concerns and force a holistic implementation of automation throughout the enterprise.

Currently, companies and CIOs are resetting their bot programs. Figuring out the desired goal of automation might help to steer it into the right direction.

SAP’s automation strategy in general, and our cloud-based machine learning portfolio and related services in particular, are ready to step in and to fill the automation gaps that bots leave.


Dr. Markus Noga

About Dr. Markus Noga

Dr. Markus Noga is vice president of Machine Learning at SAP. Machine learning (ML) applies deep learning, machine learning, and advanced data science to solve business challenges. The ML team aspires to building SAP’s next growth business in intelligent solutions, and works closely with existing product units and platform teams to deliver business value to their customers. Part of the SAP Innovation Center Network (ICN), the Machine Learning team operates as a lean startup within SAP with sites in Germany, Israel, Singapore, and Palo Alto.

Sebastian Schroetel

About Sebastian Schroetel

Sebastian Schroetel is a director at SAP for machine learning in the digital core. In this role, Sebastian and his global team shape and create machine learning solutions for SAP's core ERP products. Sebastian has 10 years of experience in innovation software development, with focus on automation, analytics, and data processing.

A Defining Moment: The Internet Of Things And Edge Computing

Chuck Pharris

Part 1 in the 3-part “Edge Computing Series”

When it comes to defining IT terms, I say the simpler the better. Let’s start with the Internet of Things (IoT).  The IoT is the network of connected things—like industrial machines or coffeemakers, things with sensors and APIs that enable connectivity and data exchange. Simple enough.

Before defining edge computing, however, we need to understand that the “edge” gets its name from its relation to the core. The core is simply the collection of technologies (housed in a data center or distributed in the cloud) that make up the critical IT and business functionality for any organization. When a business deploys an IoT scenario—say a series of HVAC machines throughout a college campus—the machines are, by definition, deployed at the edge.

Another term bandied about is “edge processing”—which is basically data processing that happens at the edge rather that at the core. This brings us to the question: Why process at the edge?

An answer to a problem

The idea of processing data at the edge is a solution to a practical engineering problem. IoT as a concept has always assumed that connected things would exchange data with the core via the cloud. Problem is, obstacles stand in the way. Some of these involve:

  • Bandwidth: For many IoT deployments, the bandwidth required to transmit data from edge devices is cost-prohibitive.
  • Connectivity: For moving deployments (such as connected vehicles) or for deployments in remote locales (such as an oil rig in the ocean), connectivity may not be reliable.
  • Latency: In situations where real-time data is required—say construction equipment designed to detect and avoid potential collisions—the data latency of the cloud is unacceptable.
  • Power consumption: Many sensors in edge devices cannot live up to the power-consumption demands required for transmitting data to the cloud.
  • Security: Most sensors—often limited in functionality—cannot provide the kind of security required in a digital economy with an expanding threat landscape.

Edge computing overcomes these obstacles with the use of an IoT gateway. Think of the gateway as a hub of sorts that lives in close proximity to the edge devices within a local area network (LAN). This hub—a full-blown server or something more purpose-built—can help conserve bandwidth by running an analytical algorithm to determine the business value of incoming sensor data and transmitting to the core only what makes sense. The hub also addresses the issue of intermittent connectivity by housing software and functionality that can be used to make decisions on the ground without access to the core. Similarly, latency and power-consumption issues are addressed through the hub, which communicates quickly and efficiently with sensors through fast, low-energy protocols such as Bluetooth or ZigBee.

On the security front, an edge computing hub can provide secure tunnels back to the digital core IT infrastructure. Remember that the October 2016, a denial of service attack—which brought down the Internet in North America and Europe—was executed with a botnet of unsecured IoT devices. As far as use cases for edge computing go, let’s call this one a slam dunk.

The role of microservices

Microservices as loosely coupled, independently deployed nuggets of application functionality. Communicating through APIs and running as unique processes, microservices are ideally suited for IoT scenarios. Why? Because they’re deployed in isolated containers so that if they fail, they don’t take down the entire network or interrupt an entire business process.

The idea is that microservices are created at the core and then delivered to the hub at the edge. The hub then makes them available to each device on the edge as needed. The algorithms that determine the business value of sensor data? These run in microservices. Predictive analytics—let’s say to predict the failure of an HVAC machine on the college campus? Also delivered via microservices. Indeed, microservices are what make the IoT a practical reality. Without them, the IoT would still be only a concept.

There’s a lot more to discuss on the topic of edge computing, but I’ll stop here for now. To dive in further, see this paper on the “4 Ps” of intelligent edge processing: Excelling at the Edge: Achieving Business Outcomes in a Connected World. Also look for my next blog in this series: “The IoT Data Explosion, IPv6, and the Need to Process at the Edge.”


Chuck Pharris

About Chuck Pharris

Chuck Pharris has over 25 years of experience in the manufacturing industries, driving sales and marketing at a large controls systems company, overseeing plant automation and supply chain management, and conducting energy analysis for the U.S. Department of Energy’s research laboratory. He has worked in all areas of plant automation, including process control, energy management, production management, and supply chain management. He currently is the global director of IoT marketing for SAP. Contact him at chuck.pharris@sap.com.

Diving Deep Into Digital Experiences

Kai Goerlich


Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.

What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”

Download the executive brief Diving Deep Into Digital Experiences.

Read the full article Swimming in the Immersive Digital Experience.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu


Why Artificial Intelligence Is Not Really Artificial – It Is Very Tangible

Sven Denecken

The topic of artificial intelligence (AI) is buzzing through academic conferences, dominating business strategy sessions, and making waves in the public discussion. Every presentation I see includes it, even if it’s only used as a buzzword – its frequency is rivaling the use of “Uber for X” that’s been so popular in recent years.

While AI is a trending topic, it’s not mere buzz. It is already deeply ingrained into the strategy and design of our products – well beyond a mere shout-out in presentations. As we strive to optimize our products to better serve our customers and partners, it is worth taking AI seriously because of its unique role in product innovation.

AI will be inherently disruptive. Now that it has left the realm of academic projects and theoretical discussion – now that it is directly driving speed and hyper-automation in the business world – it is important to start with a review that de-mystifies the serious decisions facing business leaders and clarifies the value for users, customers, and partners. I’ll also share some experiences on how AI is contributing to solutions that run business today.

Let’s first start with the basics: the difference between AI, machine learning, and deep learning.

  • Artificial intelligence (AI) is broadly defined to include any simulation of human intelligence exhibited by machines. This is a growth area that is branching into multiple areas of research, development, and investment. Examples of AI include autonomous robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), and more.
  • Machine learning (ML) is a subfield of AI that aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming. In enterprise software, ML is currently the best method to approach the goals of AI.
  • Deep learning (DL) is a subfield of ML describing the application of (typically multilayer) artificial neural networks. Neural networks take inspiration from the human brain, with processors consisting of small neuron-like computing units connected in ways that resemble biological structures. These networks can learn complex, non-linear problems from input data. The layering of the networks allows cascaded learning and abstraction levels. This can accomplish tasks like: starting with line recognition, progressing to identifications of shapes, then objects, then full scene. In recent years, DL has led to breakthroughs in a series of AI tasks including speech, vision, and language processing.

AI applications for cloud ERP solutions

Industry 4.0 describes the trend of automation and data exchange in manufacturing. This comprises cyber-physical systems, the Internet of Things (IoT), cloud computing, and cognitive computing – everything that adds up to create a “smart factory.” There is a parallel in the world beyond manufacturing, where data- and service-based sectors need to capture and analyze more data quickly and act on that information for competitive advantage.

By serving as the digital core of the organization, enterprise resource planning (ERP) solutions play a key role in business transformation for companies adapting to the emerging reality of Industry 4.0. AI solutions powered by ML will be a broad, high-impact class of technologies that serve as a key pillar of more responsive business capabilities – both in manufacturing and all the sectors beyond. As such, ERP must embrace AI to deliver the vision for the future: smarter, more efficient, more flexible, more automated operations.

Enterprise applications powered by AI and ML will drive massive productivity gains via automation. This is not automation in the sense of repetitive, preprogrammed processes, but rather capabilities for software to handle administrative tasks and learn from user behavior to anticipate what every individual in the company might need next.

Cloud-based ERP is ideal for companies looking to accelerate transformation with AI and ML because it delivers innovation faster and more reliably than any onsite deployment. Users can take advantage of rapid iterations and optimize their processes around outcomes rather than upkeep.

Case in point: intelligent ERP applications need to include a digital assistant. This should be context-aware, designed to make business processes more efficient and automated. By providing information or suggestions based on the business context of the user and the situation, the digital assistant will allow every user to spend more time to concentrate on higher-value thinking instead of on repetitive tasks. Combined with built-in collaboration tools, this upgrade will speed reaction to changing conditions and create more time for innovation.

Imagine a system that, like a highly capable assistant, can greet you in the morning with a helpful insight: “Hello Sven, I have assessed your situation and the most recent data – here are the areas you should focus on first.” This approach to contextualized analysis of real-time data is far more effective than a hard-programmed workflow or dump of information that leaves you to sort through outdated information.

Personal assistants have been around in the consumer space for some time now, but it takes an ML-based approach to bring that experience, and all its benefits, to the enterprise. Based on the pace of change in ML, a cloud-based ERP can best deliver the latest innovations to users in a form that has immediate business applications.

An early application of ML in the enterprise will be intelligence derived from past patterns. The system will capture much richer detail of customer- and use-case-specific behavior, without the costs of manually defining hard rule sets. ML can apply predictive detection methods, which are trained to support specific business use cases. And unlike pre-programmed rules, ML updates regularly as strategies – not monthly or weekly – but by the day, hour, and minute.

How ML and AI are making cloud ERP increasingly more intelligent

Digital has disrupted the world and changed the way businesses operate, creating a new level of complexity and speed. To stay competitive, businesses must transform to achieve a new level of agility. At the same time, advances in consumer technology (Siri, Alexa, and Google Now in the personal assistant space, and countless mobile apps beyond that) have created a desire and need for intuitive user interfaces that anticipate the user’s needs. Building powerful tools that are easy to interact with will rely on ML and predictive analytics solutions – all of which are uniquely suited to cloud deployment.

The next wave of innovation in enterprise solutions will integrate IoT, ML, and AI into daily operations. The tools will operate on every type of device and will apply native-device capabilities, especially around natural language processing and natural language interfaces. Augment this interface with machine learning, and you’ll see a system that deeply understands users and supports them with incredible speed.

What are some use cases for this intelligent ERP?

Digital assistants already help users keep better notes and take intelligent screenshots. They also link notes to the apps users were working on when they were created. Intelligent screenshots allow users to navigate to the app where the screenshot was taken and apply the same filter parameters. They recognize business objects within the application context and allow you to add them to your collection of notes and screenshots. Users can chat right from the business application without entering a separate collaboration room. Because the digital assistants are powered by ML, they help you move faster the more you use them.

In the future, intelligent cloud ERP with ML will deliver value in many ways. To name just a few examples (just scratching the surface):

  1. Finance accruals. Finance teams use a highly manual and speculative process to determine bonus accruals. Applying ML to these calculations could instead generate a set of unbiased accrual figures, so finance teams have more time during closing periods for activities that require review and judgment.
  1. Project bidding. Companies rely heavily on personal experience when deciding to bid for commercial projects. ML would give sales and project teams access to decades-worth of projects from around the world at the touch of a button. This capability would help firms decide whether to bid, how much to bid, and how to plan projects for greatest profitability.
  1. Procurement negotiation. Procurement involves a wide range of information and continuous supplier communication. Because costs go directly to the bottom line, anything that improves efficiencies and reduces inventory will make a real difference. ML can mine historical data to predict contract lifecycles and forecast when a purchasing contract is expected so that you can renegotiate to suit actual needs, rather than basing decisions on a hunch.

What does the near future hold?

An intelligent ERP puts the customer at the center of the solution. It delivers flexible automation using AI, ML, IoT, and predictive analytics to drive digital transformation of the business. It delivers a better experience for end users by providing live information in context and learning what the user needs in every scenario. It eliminates decisions made on incomplete or outdated reports.

Digitization continues to disrupt the world and change the way businesses operate, creating a new level of complexity and speed that companies must navigate to stay competitive. Powering business innovation in the digital age will be possible by building and deploying the latest in AI-powered capabilities. We intend to stay deeply engaged with our most innovative partners, our trusted customers, and end users to achieve the promises of the digital age – and we will judge our success by the extent to which everyone who uses our system can drive innovation.

Learn how SAP is helping customers deploy new capabilities based on AI, ML, and IoT to deliver the latest technology seamlessly within their systems


Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at sven.denecken@sap.com.