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At a time when the global energy industry is undergoing dramatic change, South Australia could very well be the epicenter of innovation.

The South Australian government just announced plans for a solar power plant that will generate up to 150MW of electricity by using thousands of giant mirrors to direct sunlight to the top of a 227-meter thermal tower.

And earlier this year, Elon Musk made headlines with the news that his company will help build the world’s largest lithium ion battery to store renewable energy in the state.

You can also see this kind of innovative thinking at SA Power Networks – the utility responsible for operating and maintaining the electricity distribution network for the entire state of South Australia.

How Do You Energize a Mobile Workforce?

“The Australian energy market is going through a huge amount of change,” says Chris Ford, Chief Information Officer at SA Power Networks. “And as a result, every element of our company is being transformed.” This transformation includes taking advantage of mobile technologies and real-time analytics to help meet not only the demands of an evolving energy market, but also the challenges of a very large service area.

SA Power Networks employees maintain a territory that encompasses 178,000 square kilometers of some of Australia’s driest and remotest landscapes. Summers often bring scorching heat and damaging wildfires to the region.

“Some of our people won’t be in the office for five or six days on the trot,” notes Charlie Hollis, Manager of Procurement at SA Power Networks.

But SA Power Networks is using mobile technology to simplify daily operations for these remote personnel and the home office employees who support them. A good example is how SA Power Networks approves purchase requisitions and the 30,000 purchase orders the utility generates every year.

“We use a mobile app that streamlines the entire procure-to-pay process,” says Melissa Maio, SA Power Networks’ Purchasing Team Lead. “Our managers and field personnel can approve requisitions and purchase orders from anywhere using their mobile device.”

Delivering High-Energy Data

SA Power Networks also customized a mobile app based on SAP Work Manager to allow field personnel to gather service interval information and data from asset inspections in real time. These assets include 400 zone substations, 73,000 street transformers, 720,000 power poles, and 200,000 km of wires.

Previously, this information was captured at the site on a pad of paper and then entered into SA Power Networks’ business systems when field personnel returned to the office. Only then could the data be analyzed and acted upon.

In fact, providing all employees with the information they need to make quicker and more accurate decisions is a hallmark of SA Power Networks’ IT strategy. The utility, for example, is now using the SAP HANA platform to perform advanced analytics on live data as well as historic and demographic data. Company leadership believes this insight will help them respond better to emergencies such as brushfires and storms and manage the overall network more effectively.

“Predictive analytics is really key to us moving forward,” says Ford. “We currently have pilots underway designed to help us predict exactly where we will need to repair lines in certain storm situations.”

A Look Ahead

Innovation continues to be a priority in the energy industry down under.

Australian news reports there are currently more than 50 large-scale renewable energy projects under construction across the country. And in South Australia, SA Power Networks is determined to keep pace with the progress.

“We have been looking at robotics and machine learning to identify areas where we might improve the efficiency of certain tasks even further,” says Ford, citing one final example.

One thing, however, isn’t changing.

Regardless of how the energy is generated, delivering electricity to the 1.7 million people of South Australia will remain the singular mission of SA Power Networks.

Hear more from Chris Ford and the leaders of SA Power Networks in this video.

Please follow me on Twitter @JohnGWard3.

This story originally appeared on SAPVoice on Forbes. http://bit.ly/2zAhEUK #SAP #SAPCloud #AI

Source: SAP

This was an ASUG BITI webcast given by SAP

Figure 2: Source: SAP
Recommend modeling iwth PAL – algorithms are implemented in server, where data resides, see much better performance

Figure 3: Source: SAP

PAL is for data scientists

Credit card data, which customer is credit worthy (classification)

Predicting house prices based on characteristics such as # of rooms, using regression

Cluster similar customers to do targeted marketing campaigns

Time dependent models, sequential pattern modeling to issue coupons

Figure 4: Source: SAP
Color coding indicates investments

Figure 5: Source: SAP
Training the model for random forest modeling

Figure 6: Source: SAP
Model scoring PAL code with confidence level

Figure 7: Source: SAP
Other options include integration with R
Connect from R studio, connect from ODBC

Figure 8: Source: SAP

R integration looks like a stored procedure

Figure 9: Source: SAP
Build models in TensorFlow and call in HANA – like a stored procedure

On the HANA side, you have the Application Function Library

With SPS02 – create the EML/AFL – interfaces between HANA and TensorFlow server

On the right side, build and train TensorFlow and upload to TensorFlow server, and then consume from HANA side

Connect through a Google Remote Function call

TensorFlow Serving Server can run in same box in HANA in development; should be separate in production

Scope is for scoring

Figure 10: Source: SAP

Train the model in TensorFlow

Figure 11: Source: SAP
Step 1 create remote source, host and port of TensorFlow server

Next map the model to the remote source; insert test model

Next – any config changes get applied immediately

Then check all connections are working before start using in the application

Figure 12: Source: SAP

Generate the EML in HANA and then call procedure using the input and output table

Figure 13: Source: SAP

Machine learning in HANA end to end; depends on type of use case

Machine learning is not just developing models, but how do these models get optimized and in a scalable real-time way

Figure 14: Source: SAP
Customer churn prediction with PAL to build a model

You can grow decision trees, output is the class

In PAL, you have fine grained control

Figure 15: Source: SAP

First step is to train the model, populate the parameter table

Figure 16: Source: SAP

Create table to store model

Capture variable importance with a table

Store the out of bag erorr

Store confusion matrix of the model

Figure 17: Source: SAP
Train the model, call the function

Decision trees are stored in PMML format

Out of bag error, the variable importance

Confusion Matrix is also output

Only 3 out of 14 cases did the model predict inaccurately

Figure 18: Source: SAP

Now built the model, want to predict the churn, and will customer to be retained

Create the parameter table
Create the results table for the results of scoring

Prediction and confidence

Used PAL to train the model and then use it to predict scenarios

Figure 19: Source: SAP

Could also build using Web IDE

Figure 20: Source: SAP

Push execution close to data

Figure 21: Source: SAP

Think of performance in terms of batch and real-time

SP02 enhancements

Real time prediction with SPS01

Figure 22: Source: SAP

Decisions need to happen in real time

Figure 23: Source: SAP

The model can remain in memory

Figure 24: Source: SAP

Partitioning – score in parallel

Ability to do large batch style processing in parallel

Figure 25: Source: SAP

Streaming analytics engine to take input from a variety of sources

Figure 26: Source: SAP

Train data as they arrive
Predict in real time

Figure 27: Source: SAP
A summary of a jam-packed webcast

Upcoming ASUG webcasts:

Oct 19 An exciting new future for SAP BusinessObjects Web Intelligence

Oct 26 Overall SAP Analytics strategy and direction 

Oct 31 What’s new in SAP BI Mobile and Roadmap

Nov 7 Cross-Solution Analytics with SAP Analytics Cloud

Nov 9 What’s New in SAP Lumira 2.0

Nov 20 What’s new in Analysis Office 2.6?

Nov 28 – Introduction to SAP Data Hub 

Nov 30 Refresh Expert Streams on Predictive Factory

Dec 5 – Update on Conversion Tools for SAP BW/4HANA

Dec 12 Best Practices for Automated Analytic

  http://bit.ly/2zAck3H #SAP #SAPCloud #AI

Welcome to the October update of the SAP Learning Hub News, keeping you up do date with the latest  developments, achievements of SAP Learning Hub!

Check out the SAP Learning Hub News archive if you have missed previous updates. You can also sign up for our email newsletter here to get the news delivered directly to your inbox. Download the latest content catalogue to get a complete overview of our available courses, content and SAP Learning Rooms. We have quite a lot of content for you again this month:

SAP S/4HANA, Release 1709: Available Learning Maps

SAP announced the SAP S/4HANA, 1709 release, the latest enhancement to SAP’s next-generation ERP business suite. More than 30 respective learning maps are available on SAP Learning Hub to support learners in keeping their knowledge up-to-date of innovations shipped with this release of SAP S/4HANA. To find all learning maps on SAP Learning Hub, you can also use the following search string in the learning content catalog: EKT_S4H1709.

In addition to the learning content, a comprehensive schedule of live-expert sessions continue throughout October 2017. To complement this offering, a sandbox environment is available exclusively for subscribers to the professional edition of SAP Learning Hub, at no additional cost, until end of 2017. Systems access is limited to the first 100 registrations per week, registrations can be done here.*

To view the complete SAP enablement offering for SAP S/4HANA, please refer to the Learning Journeys. The Early Knowledge Transfer learning maps are part of the stay-current scenarios.

Please note that content with * is accessible only when logged into SAP Learning Hub.

Learning Journey for SAP Learning Hub Available

Now there is also an interactive Learning Journey guide available in SAP Learning Hub. Designed to get you up to speed with everything you need to know to get the most out of SAP Learning Hub. From explaining SAP Learning Rooms to SAP certification or the SAP Live Access portal – the introductory materials you need to gain an understanding of the platform and how to use it is now in one place.

Check out the Learning Journey: 1st steps in SAP Learning Hub*, or go to the Learning Content section in the top left dropdown menu within SAP Learning Hub to find your learning journey offerings there.

Please note that content with * is accessible only when logged into SAP Learning Hub.

SAP Learning Rooms – Live Events

SAP S/4HANA Cloud Implementation Learning Room:
In these interactive live sessions, you will explore SAP S/4HANA Cloud from the perspective of different business processes:

* SAP S/4HANA Cloud Live Session – Finance: October 16-20, 16:00 – 18:00 (CET)
* SAP S/4HANA Cloud Live Session – Manufacturing: October 16-20, 16:00 – 18:00 (CET)
* SAP S/4HANA Cloud Live Session – Sales: October 23-27, 14:00 – 16:00 (CET)

Welcome Room:

* SAP Learning Hub – Navigation Tips and Tricks:
October 24, 17:00-18:00 (CET) – Live session in the Welcome Room on SAP Learning Hub
* SAP Learning Journeys Live Session:
November 1, 15:00-15:30 (CET) – Live session on how to find Learning Journeys in SAP Learning Hub

SAP Learning Room Update

New SAP HANA 2.0 SPS01 Learning Map is now available in the SAP HANA Modeling Learning Room, designed to support the preparation for SAP-certified application associate – SAP HANA certification exam (C_HANAIMP_13).

SAP Learning Rooms Report

More than 145,000 learners have joined the community of SAP Learning Room members so far in 2017, an increase of 45% compared with the previous year.

Close the Skills Gap for Successful Digital Transformation

Enhance your digital transformation journey with a defined skill development strategy and supportive learning environments.

Check out a recent report by the European Research Center for Information Systems and SAP that introduces a maturity model for skill development and best practices for assessing your current state and defining additional activities. Gain further insights in the related blog post or watch the presentation on 2017 SAP TechEd Las Vegas to learn about the skills that has shown to be critical in digital transformation and how you can develop them.

Additional Courses Available for SAP Live Access

New courses have been added to the SAP Live Access  portfolio recently, including:

* SQL and SQL Script Basics for SAP HANA (HA150)
* SAP HANA Modeling (HA300)
* SAP Business One – Logistics (TB1000)
* SAP Business One – Implementation and Support (TB1100)
* SAP Business One – Accounting (TB1200)
* Management Accounting in SAP S/4HANA – Academy (TS4F04)
* Database Administration (DB2 LUW) (ADM535)
* Trade Management with SAP CRM (ICP610)

Access the entire catalog for SAP Live Access and check out the courses  to practice and expand your SAP skills.

Demo of SAP SuccessFactors Training Systems

Learn how to navigate within SAP Learning Hub to find SAP Live Access offerings to practice with an SAP training system. SAP Live Access provides a self-service to deploy and use SAP training landscapes and is embedded in Learning Journeys. In addition, we will make the connection to the Learning Rooms as central place to collaborate with experts and peers. Recording of this demo is available here*.

Please note that content with * is accessible only when logged into SAP Learning Hub http://bit.ly/2zAceZT #SAP #SAPCloud #AI

This is the third and final blog in a series of three about enriching SAP BW with SAP Predictive Analytics:

* Introduction
* Technical Integration
* Steps of a Project (this page)

Steps of a Project

In the earlier articles of this series we have looked at the value that SAP Predictive Analytics is adding to SAP BW and how the two components can be technically integrated. In this article we look at the typical steps of a project.

Methodology

Especially in a predictive project it is extremely helpful to follow a methodology to ensure that a clear business need is discovered and appropriately supported through predictions. The Cross Industry Standard Process for Data Mining (CRISP-DM) is very popular and we use it extensively at SAP. In this article we will not cover the details of this methodology, but you could read up more about it on this Wikipedia article for instance.

Technical Steps

As it is usually the case, the technical steps break down into two phases:

* Initial setup and configuration
* Project (or use case) specific work

 

Step 1: Initial Installation

The following components have to be installed. Please check with your SAP Account Executive if you are not sure, whether these components are available to you:

SAP BW / SAP HANA:

* SAP BW has to be running on SAP HANA. This is a requirement, since the content will be accessed through SAP HANA Calculation Views. See the Product Availability Matrix of SAP Predictive Analytics for details on the supported environments. You can find the link to the current version on the Predictive Analytics Community.
* You should install the Automated Predictive Library (APL) on SAP HANA. The APL provides the framework that creates the predictive models automatically inside SAP HANA. Having this installed, allows models to be trained in-memory without having to extract data out of SAP HANA. Strictly speaking this is not a technical must, but I cannot think of a reason why you wouldn’t want to.

Windows Laptop of the person creating new predictive models:

* Install SAP Predictive Analytics (Desktop), which provides the graphical interface to structure your data and to create predictive models. You will spend most of the time in this environment.
* Set up an ODBC Source to SAP HANA, which is the interface used by SAP Predictive Analytics. You can obtain the ODBC drivers by installing the SAP HANA Client.

Server, to maintain models automatically:

* SAP Predictive Analytics Server and SAP Predictive Factory, which can maintain models independently. Predictive models can be retrained when needed and new forecasts can be produced on a specified schedule. For ongoing use of predictive models these components are crucial as they provide the full automation, independent of any manual steps. Should you just want to experiment with creating a model now and then, these components are not necessarily required.
* If you are using the SAP Predictive Analytics Server / SAP Predictive Factory you also need to set up an ODBC source to SAP HANA.

Step 2: Initial Configuration

It is technically possible to write the predictions into the existing data schema of SAP BW. Should you want to keep the predictions in a separate area, you can create a dedicated database schema. Please verify whether this is allowed under your licenses.

You will probably have some content in SAP BW of which you are certain that it will be used in a predictive project. Expose these objects as SAP HANA Calculation Views.

If you have installed the SAP Predictive Analytics Server / SAP Predictive Factory, you need to configure these. The ODBC source needs to be imported to the Factory for example. You can also set up users and access rights within the Factory.

After this step, you have an infrastructure in place to start creating your models!

Step 3: Create Predictive Model

Every time you want to create a new predictive model, you will come back to this step.

* If needed, expose any further required SAP BW content as SAP HANA Calculation View.
* If needed, create a semantic layer on top of the Calculation Views in the Data Manager of SAP Predictive Analytics.
* Once the data is structured as required, create a predictive model in SAP Predictive Analytics. If you have the APL installed, the model will be trained directly in SAP HANA, even if the data was semantically restructured in the Data Manager.
* From SAP Predictive Analytics you can also apply the model and write forecasts to a SAP HANA table. The target table can be created automatically from SAP Predictive Analytics. Again, creating these forecasts and writing them into a table happens without having to extract the data.

If you just want to experiment with predictive models, you might be done at this point!

Step 4: Deploy Predictive Model

If you want to put the model into productive use, you have to ensure that it is regularly retrained and that predictions are created when needed. This is taken care of by the SAP Predictive Analytics Server and SAP Predictive Analytics Factory.

* Import the metadata from the Windows Desktop into the Factory. Make sure to move both the metadata of the data structure from Data Manager, as well as the metadata of the predictive model.
* Set up a schedule in the Factory to retrain the model and to write predictions into the SAP HANA table.
* Now any application with access to this table can benefit from the predictive insight. SAP BW for instance can access that data and join the predictions with existing SAP BW content. SAP BW has been enriched with predictive insight!

You have closed the loop! SAP BW is not only showing the historic past, but also provide predictions about the future. A wide range of business process can be improved with that insight, whether through an automated decision process or by delivering additional insight to the end user.

Well, I hope you enjoyed these blogs. Thank you for reading! You can find more material in the SAP Predictive Analytics Community. http://bit.ly/2zAbDaB #SAP #SAPCloud #AI