- SAP states that they have advanced predictive analytics for machine learning.
- In this article, we review the predictive analytics that SAP offers.
SAP offers the following predictive analytics for machine learning.
Anomaly Detection with Principal Component Analysis
Here is the example SAP provides.
“A railway operator uses sensors in locomotives. Four motors each have four temperature sensors. If the motors are working correctly, all 16 sensors send data about a synchronous increase or decrease of temperature. PCA notes when this behavior changes. You would use this algorithm to monitor this behavior and to detect if sensors send temperature data that differ from other sensors, which might indicate that a motor is damaged and needs to be maintained.”
We are trying to think under what circumstances this ML algorithm would make sense to use.
Distance-Based Failure Analysis Using Earth Mover’s Distance
“An airplane contains electric devices that have batteries inside. These electric devices are equipped with at least two sensors that send data. Sensor A sends data about measurements of electric current, sensor B sends data about voltage measurements. An electric device could also have a sensor C that sends data about temperature measurements. The data sent by the three sensors not only depends on the electric device itself, but also on other factors that affect the electric device and its batteries. These factors could be the weather conditions at heights of several kilometers, how often the device is used in the cockpit, under which conditions the pilot uses the device, and so on. It is therefore normal that data sent from the three sensors might vary around a certain mean score. The data from each sensor can be visualized in a one-dimensional histogram. For multidimensional visualizations, scatterplots are used. This visualization is like a fingerprint of each battery in the airplane. To compare the sensor data of different batteries without looking at and comparing each visualization, a distance measure for probability distributions is needed. One of these measures is the Wasserstein metric, or EMD. It can be used to measure deviation from a known good reference fingerprint of a battery, or to measure differences between several batteries of the same type, for example.”
Same thing applies for this algorithm.
Remaining Useful Life Prediction Using Weibull
“The Weibull algorithm can be used to calculate the expected remaining useful life (RUL) of an asset, and to calculate the probability of failure of an asset.”
Anomaly Detection Using Multivariate Autoregression
“An example might be the changes in the outflow temperature of a system, which after a while is also reflected in the inflow temperature of a downstream system. MAR can handle different kinds of sensor values, and autonomously ranks their influence on each other. The algorithm can therefore handle noisy or random signals.”
This is another strange choice for a machine learning algorithm.
Strange ML Algorithms
SAP’s choice of ML algorithms is quite strange. Common ML algorithms include the following:
- Support Vector Machines
- Learning Vector Quantization
- Naive Bayes
- Classification and Regression Trees
- Linear Discriminant Analysis
- Logistic Regression
- Linear Regression
Interestingly we can’t see the ML algorithms that were selected by SAP being used.
ASUG on SAP Predictive Analytics
“So how does one actually buy Predictive Analysis? There are, it turns out, several different methods, and I’ll do my best to make it clear:
1. It’s bundled into SAP Lumira, within the Lumira code base, Gadalla says. But there’s a catch: You have to buy a key code to “light it up,” he says.
2. But if you buy Predictive Analysis, you get Lumira for free. (Got it?)
3. Predictive Analysis is embedded within about 20 different HANA apps (such as CRM, SCM, Partner Relationship Management and Liquidity Forecaster).
4. Predictive Analysis is also bundled with a HANA box, “where I’m using HANA as a server, where I’m doing my analysis,” Gadalla says.
Current pricing on SAP Predictive Analysis is $20,000 per seat with a minimum purchase of 5 seats, according to Gadalla.
No discussion of a new SAP product would be complete without that requisite HANA mention, of course. Gadalla reports that HANA went head to head with top competitors in doing clustering analysis, and he claims that competitors’ wares took 40 hours to do the job whereas HANA took just 2 minutes. “It was the same exact equation and same exact data,” he says.””
First, all of the algorithms used by SAP are in the public domain. SAP had nothing to do with creating them. So why are customers being changed to use them? Secondly, the HANA database will do nearly nothing to help anyone run predictive analytics faster. We run predictive analytics ML on a laptop using MySQL and it normally takes less than 20 minutes for them to run, a time we can switch to another task. And the time in running ML is in analyzing the output not in running the algorithm.
SAP is lying to customers with respect to its ML.
- The ML algorithms selected by SAP don’t make much sense for what customers would normally use them for. The use case examples show how odd it would be to use them. Only the Weibull algorithm appears usable (for predicting service parts failure).
- The database does not matter that much for running algorithms so that connecting to HANA is inaccurate. And the comparison of 40 hours to 2 minutes is asinine. That did not happen. And HANA’s is really only designed around optimizing analytics.
- The ML algorithms are not SAP’s, they are public domain. Secondly, the algorithms that SAP selected don’t appear to be ones that customers would want to use. Anyone can run these ML algorithms on data in their system without having to pay SAP anything, and HANA is irrelevant running ML algorithms. But why would any customer limit themselves to SAP’s incorrectly selected algorithms? Anyone can run a public domain algorithm on any data set without SAP being involved in any way.
- SAP is lying from 4 different dimensions all in one, and trying to take advantage of customer’s lack of knowledge around ML.
Financial Bias Disclosure
This article and no other article on the Brightwork website is paid for by a software vendor, including Oracle and SAP. Brightwork does offer competitive intelligence work to vendors as part of its business, but no published research or articles are written with any financial consideration. As part of Brightwork’s commitment to publishing independent, unbiased research, the company’s business model is driven by consulting services; no paid media placements are accepted.
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