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    SAP Co-Innovation Lab (COIL)

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    Since I am not attending SapphireNow in Orlando this year, I could spend my blog time this week describing some of the things the SAP Co-Innovation Lab is working on with partners that will be featured at the event this year, but since this week is already overwhelmed with such news, I'm going to instead plow into providing some more content on one of several new projects here in COIL Palo Alto that is keeping us super busy this year.

     

    I now have multiple favorite projects among those which have spun up this year in COIL Palo Alto, but let me start by talking about one in particular we are doing with SAP NS2. This was a project first described to us by its requestor, SAP NS2 CTO David Korn as “Big Data Fusion” during last year’s SAP TechEd.  The project was designed from the start to drive deep insight from structured and unstructured Big Data in an on-premise or cloud-based environment that would be of high interest not only to the more popular three-letter agencies but largely to anyone with similar needs in any highly regulated industry.

     

    I’m going to touch on some of the different dimensions of the project in this blog post but you can learn more from some of the key project participants first hand by listening to the COIL Early View Podcast.

     

    One of the project’s main goals is to prototype some new capability to extend an existing SAP RDS solution calling for an integration of multi-source (social media, sensor, location, image, unstructured, structured, etc.) data to perform geospatial event analysis based on the aggregation of: person of interest, location of interest, activity and semantic analysis. 

     

    COIL has enabled other Big Data projects over the past 12-18 months but what I like about this project is that is features rich partner collaboration where Cisco, Critigen and Encryptics are all working with SAP NS2 to develop a solution meant to address a variety of business challenges like:

      

    • A need to analyze large data repositories to answer the 5 W’s (who, what, when, where and why) in a trusted, secure manner
    • To provide an "off-the-shelf" capability that can be attached to any data source(s) to provide this capability. An architecture and set of capabilities which lets
      the client focus be to "build" their analysis instead of building a complex infrastructure and then create the analysis
    • Make this capability something that can be provided in a cloud or on-premise delivery model
    • Deliver this capability as an SAP RDS
    • Allow SAP to demonstrate a complete life-cycle of secure, Big Data information processing and analysis
    • Extend a cloud and big data solution set combining an integrated hardware and software components to solve activity based intelligence requirements

     

                   

    This diagram provides a good overview of the architecture underlying the solution meant to address each of these challenges-

     

    BigDatafusionArch2013.jpg

    The diagram gives us a comprehensive view of the overall architecture but we have a few others which I don't have time to share here that drill into more detail but these will certainly be included in some of the future white papers that will be published over the next couple of months.

     

    This project effort seeks to provide a working capability that implements this reference architecture for secure distributed activity based, geospatial intelligence analysis.

     

    What is Geospatial Intelligence (GEOINT) you might ask? GEOINT data sources include imagery, full motion video (FMV) and mapping data, collected by either commercial or government satellite, aircraft (UAVs, reconnaissance,  commercial aircraft or by service subscription), or by other means, such as maps, demographic databases and open source databases, census information, GPS waypoints, utility schematics, or any data about infrastructure or events on earth.

     

    Human intelligence (HUMINT) gathering means to collect intelligence by means of interpersonal contact.  During WWII, HUMINT was factored in with Signal Intelligence (SIGINT) which as an example, might be an increase in radio communications right before an enemy’s ships left a harbor which might indicate fleet movement or some form of supply chain activity. The person listening in to this traffic may not be able decipher the encrypted transmissions but just the action of so much communication suddenly occurring could suggest an activity of interest. Similarly Communications Intelligence (COMMINT) was woven into the intelligence data gathered and intelligent communities looked for any correlations between all of the different events.

     

    Now speed ahead to the 21st century in an age of continuous creation of petabytes of e-mail, text messaging, audio/video surveillance and now social networking, HUMINT data can be now be gathered, sifted, collated and analyzed to gather useful information to support a mission need.

     

    Why is this so important? Within Homeland Security, the Deartment of Defense Intelligence Community (DoD IC) and across the DoD, there is a perfunctory need for these organizations to securely integrate data for intelligence analysis which can then be securely distributed to the edge where the information is needed most. (the “edge” in this instance being personnel like law enforcement and other field agents).

     

    Today, most of the data is collected via a variety of methods where it is typically relayed back to a central processing and analysis center and then distributed for community use. In many cases it is unfortunate that where the data collected could be processed locally, there is no existing community infrastructure or it is one not capable of supporting such functions. Globally, the DoD and MoDs will spend billions of dollars extending their existing infrastructure capabilities to gradually achieve a truly mobile, distributed capability.

     

    The main goal of the solution envisioned by the COIL project team is to answer a set of very basic questions that requires sifting through petabytes of information;

     

    A person of interest (who)

     

    is going to perform an activity (what)

     

    at a given time (when)

     

    at a given location (where)

     

    for an unknown reason (why)

     

    SAP NS2 has been involved in an R&D activity with one of its customers which invariably led to prototyping a virtual analysis appliance (IQ, BOBJ, Data Services, Text Analysis) integrated with open source applications driven by social media data from the internet that can be applied to any data source(s)
    for intelligence analysis.

     

    SAP NS2 looks to create through this COIL project, an SAP Rapid Deployment Solution (RDS) to address the activity based intelligence platform needs through a coupling of SAP and partner technologies. At the heart of SAP’s architecture for OLTP and OLAP processing is HANA. HANA is an in-memory database appliance that can perform high speed in-memory transaction processing (i.e. SAP Business Suite) and big-data analytics on the same data without the need for an ETL process to load a separate data warehouse and have the ability to scale to petabyte data stores.

     

    When such an appliance further incorporates ESRI to create the concept of geospatial data marts to enrich the analysis abilities of SAP Business Intelligence, this begins to provide a true integrated capability of text analysis, geospatial analysis and traditional BI/BA in a single user interface.

     

    The next step in the evolution of this new reference architecture and one being included as part of what this project expects to yield, is to add a complete secure mobile capability that incorporates position location and the creation of a mobile application factory that can distribute applications to the edge that will allow field analysts to perform intelligence analysis on data that is immediately collected and to process previously captured data locally instead of waiting for some other organization to perform this task.

     

    What the SAP NS2 project leads desire as output from this COIL project is to integrate three related solutions into a single offering that can be used across DoD, Federal Intelligence Community, State/Local Intelligence communities and Aerospace/Defense community.

     

    The integrated solution comprises the following:

       

    1. Activity Based Analysis solution (which has been previously prototyped by NS2) which consists of IQ, Data Services, Text Analysis and Business Objects integrated with ESRI (ArcGis) to provide a geospatial information analysis platform which leverages social media as the primary data source to analyze events and determine a course of action to handle these events.

      This is based on the aggregation of: person(s) of interest (POI) or WHO is involved in the event, definition of the event or the WHAT, date of the pending event or WHEN; what are the locations involved with the POI(s) and events which is the WHERE;  and sentiment analysis of data gleaned from social media or other data system to determine the WHY will the event occur.
    2. RealTime Situational Awareness (RTSA) Rapid Deployment Solution (RDS) which is a command/control appliance based on SAP HANA which has incorporated the NIEM (National Information Exchange Model) object information model into ar elational execution schema which operates within the SAP HANA appliance. The NIEM standard is sponsored by DHS, FBI, DOS, State, Local, tribal and international emergency response and intelligence communities.
    3. Both of the aforementioned solutions have a desktop and mobile component. SAP NS2 wants to integrate its secure mobile platform to the mobile aspect of the solution architecture. This will allow for secure communication between the server and mobile device and protect data at rest for the mobile device. The secure solution includes mobile device management (MDM) and a mobile enterprise application platform MEAP).

     

    Situational Awareness Use Case Scenario:

     

    One of the things about this project which first grabbed my attention was its proposed use case which centered around a capability to assess the activity of food trucks in the Washington DC area based upon classification, frequency of activity, and sentiment of customers with respect to their location(s) over a given period of time.

     

    The use case is interesting because it is plausible that a foreign threat to the homeland could somehow leverage a food truck as a clandestine way to trigger an act of terror against US citizens and public or private property. The other fascinating thing about this use case is how easy it is to demonstrate an effective use of public data in which to help glean new actionable insights.

     

    From gathering this information, an analyst can look to uncover patterns of behavior of food trucks, where they usually are, and “atypical” locations of food trucks. By using the predictive analytics functions within HANA, the analysts can project which food trucks and food truck classifications will yield higher sentiments and more activity using time series analysis (i.e. double, triple exponential smoothing);

    Using an Apriori association algorithm the analyst can detect correlation of food truck proximity to other food trucks or pre-defined geo-fences; using the anomaly detection algorithm to detect when food trucks within a given geo-fence for a long period of time suddenly decides to move to a different location within the city. Through the use of these predictive functions, SAP HANA can create a specific analytics view that can be used to drive a native Netweaver HTML 5 application, a BOBJ dashboard and alert or a geographic overlay to be used by an ArcGIS application.

     

    Given the proposed software architecture and implementation, Data services is used to capture data from web crawling publically available data from both Yelp and Twitter regarding the location of food trucks within Washington DC and the sentiments that the food truck patrons post on these social media sites. The use case then demonstrates how to turn this into actionable information-

         a. Using the text analysis functionality within data services (in the next release, the HANA text analysis function will be used) sentiment analysis is performed on the data captured from Yelp and Twitter and ETL to HANA

         b. In the next iteration of the demonstration scenario, HANA will perform continuous time series analysis (exponential smoothing) within a user defined geo-fence to predict the most popular food truck classifications and the associated food trucks; using the anomaly detection algorithm in tandem with the Apriori algorithm  to detect when a specific food truck has left its “normal” geo-location to do business in a neighborhood that is not associated with the food truck classification (cuisine, i.e. an Asian food truck suddenly moves to a neighborhood where the demographics suggest that Mediterranean cuisine is preferred).

        c. SAP HANA interfaces with ArcGIS to provide a geo-overlay

       d. SAP HANA drives BOBJ dashboard for real-timesituational awareness analytics

        e. SAP     HANA interfaces with the ArcGIS mobile application on the iPAD via web service to allow a field operative to captureand log real-time sentiment events to update the HANA database and bobj analytics

     

    There is so much interest and excitement surrounding this project that itin fact has already spurred follow on project discussions to further exploit future SAP HANA capabilities and to explore in greater depth how geospatial intelligence can be applied to other industries like retail and healthcare. 

     

    While there is always the challenge to identify the valid business case underscoring how this technology can be used, the point being made here is that this team has established a very compelling co-innovated reference architecture that is already proving what’s possible. I know from just this project alone, it is going to be a very interesting year at COIL.

    The SAP Co-Innovation Lab (COIL) continues to globally enable a broad constituency of project requestors originating from both inside of SAP and from firms within its ecosystem wishing to engage in co-innovation projects.  This co-innovation enablement is comprised of a platform of services:

     

    • IT infrastructure- to provision SAP landscapes for all projects
    • IP management- sow-based projects
    • Knowledge brokering- sourcing, connecting subject matter and
      domain experts
    • Project and operations management- resources, capabilities for
      project execution

           

    This platform is of high value to the project originators prefering to stay focused on the collaboration and co-innovation goals of the project itself, and not to spend many hours, days or even weeks seeking out the different resources required and becoming deeply involved in building out the right physical project environment. COIL provides such a platform. This year we ran over 100 projects like this worldwide.

     

    The knowledge brokering dimension of the COIL enablement platform is something that has gradually evolved into a strong capability. This is due to a number of contributing factors, like being among the first to establish SAP platforms like Sybase SUP, SAP Gateway and SAP HANA landscapes to support an array of co-innovation project work which then helped to build our global team’s infrastructure expertise needed to stand up these environments and to
    efficiently provision projects.

    knowledgeBrokering.jpg

     

    With nearly 5 years of co-innovation project work under our belts, COIL project managers, business leads and subject matter experts have become quite adept at thoroughly assessing project proposals to identify potential gaps relevant to required hardware and software resources as well as to recognize when discrete expertise is necessary for the project to succeed. There are a variety of ways in which COIL applies knowledge brokering for a given project. There are four ways of sourcing expertise that have emerged as somewhat cornerstone to this effort:

     

    • Project Requestor
    • Personal Networks
    • Repeat Project Participation
    • Social Networks

     

    Oftentimes the project requestor has already laid sufficient groundwork in obtaining the right people to participate in the project. One observation is that we often find more formal commitments when participants are formally assigned to work on the project via the project requestor's own executive sponsorship. While this is obviously ideal, it is however somewhat rare. At best, we see some or all of the SAP application or platform experience fulfilled by the requestor and yet general experience relative to the entire technology stack or understanding key aspects of the infrastructure of equal importance to a project can be lacking and therefore represent a critical gap. It is similarly so when an SAP technology partner or ecosystem ISV proposes a COIL project. The firm will typically bring vast knowledge of its own technologies and products and perhaps some broader skill sets relative to things like networking, storage or security but may lack the deeper SAP knowledge critical for things like developing a suitable use case.

     

    Nonetheless, from COIL having enabled such a diverse portfolio of projects, our business leads and project managers have built up their own personal network of contacts inside SAP and across its ecosystem to the extent that our success at finding the needed project participants is fairly successful.  We often tap talent for a project by connecting prior project participants to new projects where we see a fit and can leverage the fact the person is already familiar with COIL project processes.

     

    There are of course ongoing challenges like sometimes taking more time to identify a needed expert than what a proposed project timeline can tolerate. Another issue looms where we locate the right expert who is willing to participate only to find that their availability does not coincide with the project timeline or worse, they get redirected due to their own shifting priorities and need to drop from an active project. There are ways in which to mitigate this, but the risk is there in any circumstance where the participants are not formally committed to the project through agreements spanning a project’s executive stakeholders.  

     

    The last and more recent way to source expertise for co-innovation projects is to tap internal social networks by simply broadcasting information about the project and requesting assistance from those who find the proposed project of interest and where it may even align with the goals of others not yet aware of the project. The effort to tap social networks is something we’ve only just stated to explore and may in fact not get complete traction until enough people become interested to continuously follow the SAP Co-Innovation Lab and a stronger awareness that it can be possible to become involved in co-innovation projects.

     

    As someone who pays attention to how co-innovation is best enabled, I am always interested to learn how we can become more efficient at what we do and how our services can be designed to scale. In a previous blog post, I described my prior experience with cross-utilization management as one well-known approach developed years ago, for increasing workforce agility. Since first becoming active with implementing Web 2.0 Technologies for collaboration as early as 2006, I’ve had the sense that there must be scalable ways for project owners to connect to a larger pool of people.  Expanding this reach via social networks may be an effective way to strengthen our overall knowledge brokering capacity but this may or may not prove to be the only way to source project talent. Given the priorities of the day, it is not always possible to nurture the social networks of interest to the degree that it will quickly become a fruitful source for consistently connecting with subject matter experts.

     

    Through my own scholarly efforts to discover how other firms and organizations work to connect people and to try and find the right people at the right time for important innovation project work, I was recently introduced to a network of people who are exploring the topic of Human Capital Management (HCM) and the intersection of management 2.0 and enterprise 2.0. Should you yearn to dive straight into HCM, I can highly recommend a great book on the topic, The New HR Analytics by Jac Fitz-Enz.  As I read this book, I was struck by how prevalent predictive analytics is becoming in HR and I wondered aloud at what point a firm's HR organization would reguarly track innovation project work with respect to how such projects connect with people resources across the firm and what its impact has upon not only revenue and growth but employee motivation, loyalty and job satisfaction.

     

    In much the same way we are fast becoming accustomed to sourcing computing power from the cloud. There are now tools and services emerging in the marketplace empowering the enterprise to optimize knowledge workers. For starters, SAP SuccessFactors offers market leading solutions with its BizX Suite delivering Core HR functionality, collaboration, and Analytics all from the cloud to aid firms with getting the right people aligned to the company strategy and business goals.

     

    Additionally, and what I view as fully complimentary to the aforementioned capabilities are newer and compelling management techniques being developed that rely less on top down decision making and more on distributed worker based decision making. There are a few companies out
    there looking at this today but one particular startup, Collabworks is evolving the notion of the “People Cloud” and describing a new paradigm of Worker-as-a Service.   It didn’t take long for me to become interested in all of this as it seems to directly address some of the knowledge brokering challenges previously mentioned. 

     

    I find the idea that the right type of platform or middleware could serve to scale workers as a service to the extent that jobs can become more like services most intriguing.  It suggests that there are now opportunities to optimize talent by ensuring a firm can get the right people connected to the most important and relevant projects can occur instead of head count and job functions being tied to a single discrete business unit and fundamentally restricting a free flow of talent within an organization.

     

    I’m still very much the newbie to all of this, but am eager to learn more about it. I get my first full immersion into all of it by participating in a workshop and panel discussion hosted by Collabworks at the Computer History Museum in Mountain View, CA on Dec. 11th.  For a few short hours I will be surrounded by a lot of really talented people looking at all this from a variety of perspectives where I hope to find some further insight into how all of this might serve to supercharge our agility and efficiency to find and connect experts to co-innovation projects.  What I value is how this also injects a
    higher degree of ideation and helps foster the open innovation tenet of leveraging tacit knowledge exchange between SAP and ecosystem partners to both accelerate the innovation process and to find shared commercial success with bringing innovation to customers.

    Recently, our Open Innovation team (including David Cruickshank) sat down with two innovators from SAP Research to discuss their approaches to innovating in the real world. Dr. Axel Saleck, VP of SAP Co-Innovation Lab Global Network, and Denis Browne, Senior Vice President of SAP Imagineering Research are featured in a video interview moderated by our own Rocky Ongkowidjojo. Axel and Denis share their motivations and some examples of exploring opportunities for open innovation as well as exploiting technologies and processes in unique ways.

     

    Axel notes that in COIL, “We don’t think that we (SAP) don’t know everything….” This becomes the basis for open collaboration with partners, customers and others, leading to innovative solutions. COIL’s aim is to not look just at SAP landscapes but at the real world situations where SAP technologies and processes applied to solve big problems. One example is drawn from a project addressing disaster/recovery across a complex landscape of technologies and processes from many vendors. This resulted in a highly optimized solution that was successfully implemented, prior to the catastrophic 2011 earthquake and tsunami, with a number of Japanese companies. Axel goes on to describe recent collaboration in the mobility space, as well as Smart Meter Analytics enabling support for renewable energy.

     

    Denis emphasizes the point that Imagineering is intended to be a positive disruptive force. Their motto is, “Jump and find your wings on the way down.” When collaborating with entrepreneurs, start-ups, partners and customers, the Imagineering team desires risk, uncertainty, and requires the willingness to find a creative solution to a business problem. Imagineering looks closely at the challenge of understanding when to ramp investments in order to make the transition from exploration to exploitation.

     

    We look forward to hearing more from COIL and Imagineering as they achieve additional success in Open Innovation.

    The session with Stefan last week was well attended, more than 50 people attended at COIL and even more attended online.

     

    Many of you who missed the session have sent me emails about the recording.  Sorry it took a few days to get the files published in SCN.  Good news is that the online session recording is now available here (http://scn.sap.com/docs/DOC-29295).

     

    For those who want to watch Stefan talking, the video recording for the onsite part is now available in SCN too. Due to its large size, the recording is split into two parts:

     

    A note for SAP colleagues seeking general social media participation guidance

     

    Laure Cetin shared with me a social media resource center where you can find our portfolio of social media guidelines, best practice information, tools and access to the social media account registries. The site is only accesible from inside SAP network.    

    Do you have SAP BusinessObjects BI products running on VMware? What kind of virtualization overhead you have experienced? Any good lessons you have learned and shared with the world?

     

    A long-awaited guidance

     

    Let me know about your experiences. Here is what I understood from my conversations with our product folks - when not configured optimally, performance penalty of running SAP BI in virtual environments can be as high as 20%-40%, whereas with some best practices applied, the overhead can be brought down to  the industry standard expectation of around 5% to 10%.  

     

    That tells how much we can benefit when we share the best practices.

     

    Well, there was no official guidance for running BI in virtual environments, until now. 

     

    COIL started a project with our next door neighbor in Palo Alto (VMware) and our BI colleagues late last year to address this gap, and just rolled out our first paper (the paper is also available from VMWare site here ) focusing on evaluating a subset of Java-specific best practices by applying them to an SAP BusinessObjects BI 4 deployment and analyzing their effects.

     

    Check it out, and let us know what you think.

     

    If you've got questions about the technical details of the paper and/or general virtualizing BI topics, get in touch with Ashish Morzaria. Ashish should be able to provide you an answer or point you to the right contact within the BI teams.

     

    Whom should we thank?

     

    It all started with a meeting at COIL with our friends from VMware, Vas Mitra and Justin Murray. Justin  co-authored the VMware paper "Enterprise Java Applications on VMware - Best Practices Guide" and would like to see how it's applicable to SAP BI application. Over time, we were joined by Jay Thoden van Velzen, Ashish C. Morzaria, and many others. Jay played a critical role in designing and building the BI testing environment with the help of Roehl Obaldo and Sivagopal Modadugula. Ashish was the one who pushed us through the last mile of the paper. Without Ashish's diligent work and close collaboration with other BI colleagues, we won't have the paper as it is published today. 

     

    Many colleagues supported with the setup of the test environment, the execution of the tests, and the review of this paper. To mention a few: Michael Hesse at VMware; and Peter Aeschlimann, Corey Wilkie, Jacques Buchholz, David Cruickshank, Abhay Kale, Irakli Natsvlishvili, David Pascuzzi, Veronique L'Helguen Smahi and Andrew Valega at SAP
     

     

    What next?

     

    The resulting recommendations in this document only provide general guidelines related to Java and do not target any specific size or type of BI deployment.

     

    Is that enough? Definitely not.

     

    We are planning more document(s) to share validated "Best Practices", not only Java, but also include all other aspects,  on how to best deploy SAP BusinessObjects BI in a virtualized (VMware) environment. 

     

    We are in planning phase of the next round of tests, and are open for suggestions. So if you have anything in mind and would like us to validate, now is the time to tell us. We can not grantee we will be able to address each and everything given the resources and time available to us, but we will certainly try our best.

    Many of you who are in or follow the social media and open innovation spaces may already known Stefan Lindegaard.

     

    Stefan is an author, speaker and strategic advisor focusing on the topics of open innovation, social media tools and intrapreneurship. His sharp work has propelled him into being a trusted advisor to many large corporations. Stefan has written two books: Making Open Innovation Work (Oct 2011) and The Open Innovation Revolution (May 2010). His next book, Social Media for Corporate Innovators and Entrepreneurs: Add Power to Your Innovation Efforts, is due fall 2012. 

     

    Get a taste of his blogs at www.15inno.com.

     

    Well, I have got some good news here - Stefan will be a special guest to our next eco-innovation forum session, thanks my colleague David Cruickshank for his connection with Stefan.

     

     

    Title: Social Media for Corporate Innovators and Entrepreneurs - Add Power to Your Innovation Efforts

    Date: Friday, June 8, 2012

    Time: 12:00 PM - 1:00 PM PDT

     

    For those who are in Palo Alto, grab your lunch and join us at building 1, COIL quad.  We will have a professional videophotographer onsite to record this interactive session, so be ready to ask your questions and have your smile taped.  If you have a copy of his book and would like to have it signed by Stefan, bring it with you. We will see if something can be arranged.

     

    If you are remote, we will broadcast the session online. Reserve your webinar seat to get a dial in number.

     

    See you tomorrow.

    I'm thrilled for the SAP Co-Innovation Lab this week to host its Eco-Innovation Forum with our special guest Stefan Lindegaard this Friday, June 8 from Noon to 1pm pacific.

     

    Stefan and I connected sometime last year after I had written a paper examining the intersections of Open Innovation, Co-Innovation and Social Networks. Shortly after publishing this paper to SDN, I participated in a 1 day panel discussion at Santa Clara University hosted by Dr. Terri Griffith, author of the Plugged-In Manager.

     

    As I researched my material prior to the discussion it did not take long for me to find Stefan's very popluar open Innovation site, www.15inno.com. My first observation upon visting his site for the first time was to discover that there were many, many people out there passionate about Open Innovation and trying to figure out what it takes to succeed at it. We subsequently shared some ideas and comments through different topics/postings and after awhile, discussed his upcoming 3rd book that explores open innovation and social media. We exchanged a few more emails which lead to Mark Yolton and I being interviewed for the book and sharing our own views and experiences on such a rich topic. We are looking forward to its publication.

     

    This Friday, Stefan will share with us how a company might use social media to bring out better innovation faster. Working on his next book, Social Media for Corporate Innovators and Entrepreneurs:Add Power to Your Innovation Efforts, Stefan has learned that this question is being asked by many corporate innovators and entrepreneurs around the world.

     

    This intersection is unchartered territory, yet full of interesting opportunities. At the session this Friday, Stefan will cover:

     

    • an understanding on how social media tools can impact innovation efforts
    • an overview of the most important tools and how they can be used by innovation teams
    • examples on how leading-edge companies use social media tools in their innovation efforts
    • advice on how to get started with using social media for innovation

     

    If you are on the SAP Labs Campus this Friday, we hope you can drop in and participate.

    Real time analysis of gross-to-net profitability can offer huge value to business executives. 
    For example, when you roll out a sales incentive program to boost sales, you want to know how much more revenue the incentives actually generated for you, and how it eventually impacts your profitability, especially when all the fees and costs are taken into consideration. Many SAP customers have already been using a solution called "SAP Incentive Administration and SAP Paybacks and Chargebacks by Vistex", which augments the ERP product to provide great insights for gross to net profitability analysis.
    In today's competitive market, the challenge is not only that you want to have accurate profitability data but also that you want it right away.  Given the constraints with traditional business analytics, it may take days or even months to get the insights you need. Getting real-time analysis was impossible until recently.
    Remember the lady HANA that everybody wants to date nowadays as Lars Dalgaard put it in his recent Sapphire keynote? Well, We at COIL made the introduction to HANA for Vistex, and the relation worked out great.  With the hardware provided by IBM, we set up a PoC environment at COIL as described in the diagram below.  While ERP data is constantly replicated to HANA, all the magic things Vistex does can now be done almost real time. Ad hoc reports and drill-downs can be performed while millions of sales orders, sales incentives, sales rebates, and chargebacks are being  processed. 
    Vistex with HANA at COIL.jpg
    To give you a sense, here is an example showing that increased sales incentive indeed significantly boosted sales for the target products
    Vistex with HANA_SItable.jpg
    but actually brought down the net profitability of these products and negatively impacted the company's bottomlines.
    Vistex with HANA_G2Ntable.jpg
    What's really beautiful?  With HANA, you get these insights real time.
    For those who are interested in more details of the PoC, the project team has published a  recorded demo and

    a whitepaper which is featured at COIL homepage this month. 

    Well some time has elapsed since I last wrote. I surely owe and update on the Monsta project since my last post and my sincere appreciation to those commenting and expressing interest in Monsta, but I'm going to hold off once more, in order to bring a new topic into focus; Big Data.

     

    Since early last year, COIL has participated in numerous discussions around how we can help to enable projects and POCs attesting to the value delivered from a Hana/Hadoop deployment. I would say that the first discussions COIL took part in seemed to be just in front of the surge of big data articles that have surfaced over the past several months.

     

    COIL eventually teamed up with solution marketing at the start of 2012, where we looked for a means to enable a big data initiative allowing SAP to work with a variety of internal teams, established partners and newcomers from its ecosystem.   For more insight there was a Big Data press release published at SapphireNow that shares some of the things going on in this space.  We’ve yet to talk to a hardware partner that is not interested in collaboration as most if not all are marketing Hadoop-ready platforms and actively working with the various Hadoop distributors.  Among many of the Hardware vendors (servers, appliances and storage), virtualization software, OS providers, Hadoop distributors, Systems Integrators and ISVs, we’ve talked to, nearly all express interest in working with SAP in its co-innovation lab.

     

    It has been our thought for some time that COIL could serve to orchestrate having multiple hardware partners (some who are either COIL sponsors or project members already) along with other participants from the SAP ecosystem to pursue “big data” project work. The output from such projects would then not only be relevant to helping SAP refine its own Hana Hadoop strategy as neccessary, but the same project work output contributes in the same way to the participating firm supporting its own strategies. The collaboration and tacit knowledge exchanged in these projects serves to enrich the knowledge required to deploy and use Hana/Hadoop as well as to potentially produce spillover effects that can often lead to discovery of both unknown opportunities or to mitigate or avoid risk.

       

    Data just doesn’t remain as an archive anymore. There is a growing interest and need to tap every datastor; public, private, structured, unstructured, semi-structured in a way that will expand and deepen a firm’s core knowledge.  With desire for real time predictive analytics, this means being able to use data from the moment it is collected while simultaneously managing the increasing velocity and variability of each data set. 

     

    SAP can now leverage Hadoop’s distributed file system and Map Reduce Framework for pre-processing a high volume of raw data in variety of formats.   SAP EIM has the capability to perform text data analytics by pushing down the entity extraction operations into Hadoop. For those not so familiar with Hadoop, it uses a MapReduce programming interface that performs two functions; the Map function, which grabs a source of data and then gets applied to all members of a dataset to then be processed across multi-core systems where a result set then goes to the Reduce function.  Hadoop uses MapReduce alongside of the Hadoop Distributed file System (HDFS) which is how data gets stored.

     

    No question that Hadoop is clearly hyped up in the market these days but the interesting thing to see is that it appears to scale well over commodity hardware so firms are certainly exploring how and what big data sources can be tapped and if proved valuable, to determine how it becomes a system to be deployed into an on-premise production environment.  All of this is still so new; with many open questions related to impact on change management, security as well as to balance system performance with operational costs.  Hadoop is not without issues. To begin, a batch oriented system is not likely to meet the needs of every big data challenge. MapReduce recieves plenty of attention today, but it too possesses known limitations that may need to be grappled with for a while in trying to make things work.  Given that Hadoop and Hana both benefit from application development done over top of them, seeing a collaborative effort among SAP and partners to explore a range of things from the creation of new applications over Hana and Hadoop to optimizing the architecture and infrastructure needed for large production deployments is the right approach.  Dan Woods has looked at this in even more depth:

     

    From a COIL perspective, we see enabling SAP and multiple partners to qucikly and efficiently engage in such project work allows for a variety of important questions to be examined through different use cases relative to systems management, scalability, high availability, security as well as even green it considerations. Any or all of these things can become key to ensuring successful deployments, and yet not all things can be explored from just within the Hadoop community or only within the Hana community. COIL projects can create touch points allowing project teams to then draw from both communities as knowledge flows that can serve to strengthen collaboration and co-innovation efforts to yield successful project results.

     

    It arguably makes good sense for SAP to develop a strategy that shows how Hana fits into a big data landscape.  It is also quite useful to have a viable means for exploring what technologies, configurations and reference architectures are most valuable for optimizing how Hana can take as input, a variety of large data sets requiring lighting fast computation and ready for being fully visualized and analyzed.  To date, there are two projects explored where SAP BI and Analytics experts collaborated with internal teams, Cloudera and IBM, to establish the architecture blueprint for an SAP Hana/Hadoop solution useful to a variety of use case scenarios that bring structured and unstructured data together.


    In the projects running up to SapphireNow this year,  two demos were created as well as some new ones still in progress at COIL working with Hitachi and Cognilytics.  Of the two exhibited at Sapphire Now 2012 they were:

     

    1.) Retail POS data showing HANA + Hadoop complimentary solutions


    2.) Sentiment Analysis used in HANA+ Hadoop by leveraging DS 4.1 text analytics

     

    The project team was comprised of members from Solution Marketing, Ecosystem and Channels, EIM, SAP prototyping, Cloudera, IBM and COIL. Each participant contributed hardware, software and subject matter expertise as needed to build out a landscape capable of addressing useful business analysis and decision support.

     

    The project demo was designed to show how Hana and Hadoop allows for real time data exploration using SAP Hana and SAP BOE to make sense of a data set comprised of over a billion sales records. It demonstrates how you can gain insights beyond just what an indicator such as sales revenue can tell you about how well a firm’s product sales are doing and where customers are most interested. The demo describes going a step further by trying to assess what products can potentially sell better. By assessing product data like that which can represesent customers who look at items of interest but don’t buy, so that a firm can use this data to concentrate selling to a customer segment that has high potential to purchase. Combining unstructured and/or low value data such as web logs and customer sentiment text from Hadoop with structured product data in Hana to do real time correlation and analysis is a pairing of technologies that is technically and economically feasible to implement and brings new value to the how to draw benefit froma variety of large data sets.

     

    COIL looks forward to seeing SAP and more participants from its ecosystem of customers and partners pursue big data projects. I will save it for a later post, but COIL is also helping to enable the SAP Startup Focus Program where we now have a number of exciting startups working in COIL to develop Hana into various new solutions they are bringing to market. Some of these new firms are without question also tackling big data problems so when we talk about potential for spillover effects as more participants pursue big data projects in COIL, we are not kidding.  Since SapphireNow, we are already aware of discussions and proposals for new projects that will bring some of these startups toward working with both Hana and Hadoop.  As the project work continues, we will share results along the way. For those interested in big data and active in some fashion as to how Hana and Hadoop factor, we would welcome your comments, input and even interest in becoming active in this important initiative.

    The COIL team in PAL has been working with a number of colleagues spanning solution marketing, EIM and our Ecosystem and Channels partner managers to explore an optimal way to explore how to enable co-innovation with partners in response to the rise in demand for Big Data Hadoop deployments in 2012.

    Before getting too deep into focusing on how best to enable co-innovation project work as it relates to Big Data and how Hana factors into a hadoop deployment, I would first redirect anyone not yet too familiar with hadoop to check out the scads of resources on the Internet discussing the topic across many dimensions. You can begin here. If you want to know more about Big Data in general, then you can maybe start with Wikipedia before spending the next 6 months then googling the term and tryng to discern what everyone means when they refer to Big Data; Caution: it's similar to trying to grock "Cloud". What you should first come to understand perhaps is that it is generally accepted that big data is thought of across 3 dimensions- amount of data, input and output speed of data and variety of data.

    From a COIL perspective, our goal is to not simply support a single, definitive project, but create a platform capable of eanbling and sustaining a number of big data projects that will best support SAP Big Data strategy.  This will mean to accomodate an exploration of the applicable technologies neccessary for crafting a solution architecture featuring SAP and SAP Sybase products and to hit upon an optimal solution architecture that can enhance and simply a hadoop deployment. There is a need to work with partners to both. A platform approach therefore considers the differentiation possible for a given solution that considers reporting needs (e.g. visualization), Analytics Modeling and then the platform itself be it distributed are metal and/or virtual computing and local storage resources and bandwidth.

    The virtual project team here in COIL is already actively developing projects and looking to engage with a variety of Hadoop software distribution firms as well as our different alliance technology partners. We are already developing use cases featuring the hadoop architecture and HDFS. The first proposed project (and demo) has two components; one to simply use BI4 to visualize data within HDFS- no requirement to move the data. The 2nd use case would be to select some segment of the data set and bring it into HANA for deeper/faster analysis that can then be visualized. An effort would be made leverage large repostiories of both coprorate and external data to then demonstrate an ability to uncover trends, present meaningful statistics and to obatin information that can be acted upon by management. One example would be to pull vast amounts of customer sentiment data from a public dB (like Amazon) and examine it alongside of product details found in ECC that map to uncovered product sentiments.

    It is desirable to understand how we can begin to identify the most interesting use cases to explore that can lead to increasing customer confidence and value in big data technologies. There is no question that we are exploring how Hana and hadoop can and should work together as it is becoming more and more commonplace to hear big data discussions typically include how to use hadoop despite the fact that their are many other non-hadoop approaches to high perfomance cluster computing. We itend to explore both realms.  It is all still relatively new. Roughly 1 percent of all enterprises have even attempted to deploy hadoop into production environments but the trend to play around and explore what is possible is accelerating. There are still many production hurdles to clear ranging from security concerns to change management, performance and TCO issues. The challenge is to identify the leading types of problems bsuinesses are trying to solve for and what use cases can drive useful POC projects. One thing is for certain, that even with an increased use of big data technologies, companies are finding an increased need to attract and to develop better business intelligence expertise to help in developing and using much needed analytic modeling.

    If you are a big data subject matter expert or just an afficionado, what sort of big data projects would you envision SAP doing with partners? What are your ideas for the sorts of use cases that could prove interesting?  Who should we work with and why?  How would you choose whcih partners and which scenarios to examine first? The project work is just spinning up now in earnest but the efforts will span all of 2012 and well into 2013. If you have ideas or an interest in all of this, please comment.

    Introduction

    Delivering Enterprise Services (ES) forms a crucial element of SAP's SOA strategy. Enterprise services combine the technical communication standard of SOAP/WSDL with the semantic naming standards of UNCEFACT/CCTS integrated into SAP’s SOA methodology that additionally incorporates SAP’s strong experience in business processes in more than 25 industries.

    By re-using the large repository of SOA artifacts such as Integration Scenarios, Process Components, Business Objects, Service Interfaces etc. within SAP’s Enterprise Services Workplace, Customers and Partners can benefit from SAP’s almost 30 years of experience in the development of business software. By applying SAP’s SOA methodology, Customers and Partners can enhance existing SOA artifacts and where necessary develop new ones fitted to their specific needs.

    To support partners in their efforts to enable their software and solutions with own SAP SOA methodology-compliant web service interfaces, the Global SAP Co-innovation Lab (COIL) Network team started the Partner-delivered Enterprise Services (PdES) (PdES) initiative in 2008 and is continuing to work closely with selected partner projects. We in COIL see SOA clearly beyond the peak-and-trough side of the hype cycle but still in the “Slope of Enlightenment” phase with the need to support partners as well as customers in their efforts of applying not only the technical integration aspects of SOA (see e.g. COIL-IBM whitepaper on Interoperability of the IBM WebSphere Message Broker and the SAP NetWeaver Process Integration) but as well the aspects of business semantics carried on top of the technical integration layers.

    Today, I would like to share with you the results of the joint PdES project activities of COIL and Accenture over the last 1 ½ years. I am particularly using the blog format to be able to discuss with you both the path to as well as the benefits of SAP’s SOA methodology for SAP partners as well as customers.

    The initial steps

    In 2009, Accenture Innovation Center (AIC) for SAP located in Bangalore joined SAP's global initiative Partner-delivered Enterprise Services with the goal to make SAP's SOA methodology a key element of their SOA practice.

    The starting point was an initial one-day knowledge transfer training session on SAP’s SOA methodology which was immediately followed by a two-day scenario workshop which aimed at applying the freshly learned content to selected business scenarios that Accenture saw as most beneficial to their customer base.

    Accenture’s scenario brought to the workshop table was Emergency Asset Management enhancing the then existing SAP Enterprise Services offering around the business object Maintenance Order. It was used to gain first hands-on experience on applying the methodology as well as using the Enterprise Services Builder toolset.

    The path to the first implemented scenario

    Starting with the initial face-to-face workshop the whole process for the first scenario was continued and finished using remote communication via web and telephone conferencing, email etc.

    In an iterative process guided by COIL experts, Accenture identified all necessary process component model elements required in the business scenario. A detailed analysis of SAP’s existing enterprise service operations revealed the remaining gap as seen by Accenture’s scenario experts. For this gap, Accenture started developing the ESR (Enterprise Services Repository) content down to the message data type field level with us from the COIL team reviewing the artifacts, giving general feedback and SOA methodology guidance along the way.

    The scenario was successfully finalized within two months and one can investigate the modeling elements on the respective SAP Community Network page: Emergency Asset Management by Accenture. If interested in more detailed information, please contact the Accenture colleagues named on the page directly.

    Re-applying the methodology and stabilizing the gained knowledge

    To be able to make PdES a standard offering within their services portfolio, Accenture decided to in the end implement three more industry scenarios (JUST-IN-TIME by Accenture, Traders and Schedulers Workbench, Retail Event and Space Management ). In doing so, they were able to anchor the freshly gained knowledge in their team. While Accenture’s learning curve was rising, COIL was able to reduce its support efforts and with the latest scenario implementation of the Retail Event and Space Management scenario delivered we see Accenture’s Innovation Center (AIC) for SAP in a very good position to in future work on their own in the domain.

    Please have a closer look at all four Partner-delivered Enterprise Services Scenarios by Accenture.

    Lessons learned & findings straight from Accenture

    Instead of providing my own view of Accenture’s lessons learned & findings with SAP’s SOA methodology, I will now hand over to Siva Devireddy, Lead – Innovation Center for SAP, Bangalore, who has been heading Accenture’s collaboration with COIL around PdES:

    "Service Oriented Architecture (SOA) is a key enabler for business process transformation and enterprise integration. SAP Service Oriented Architecture is the basis for the Business Process Platform and all the new SAP Solutions and Enterprise Services are key building blocks.

    As System Integrators, it is essential for us to understand the concepts and the process involved in designing and building these Enterprise Services.

    Accenture Innovation Center for SAP (AIC) has partnered with SAP Co-Innovation lab (COIL) to drive innovation and though leadership around SAP’s Service Oriented Architecture (SOA). The key initiative was Partner delivered Enterprise Services (PdES) which involved identifying, designing and building Enterprise Services (ES) across different industries.

    We designed and developed 25 Partner-developed Enterprise Services for 4 industry scenarios in collaboration with COIL experts from Walldorf, Tokyo and Bangalore. This was a very enriching experinece for both Accenture Technology and Industry teams. Following are some of our key findings.

    • There was a steep learning curve for all the industry/functional and technical experts to understand the SAP SOA and Enterprise Service concepts. The workshop conducted by SAP COIL team in India gave us a great head start in understanding the concepts in detail and the benefits.
    • One of the biggest challenge was to get all the industry and technology stakeholders on a common ground of understanding for identifying and finalizing the industry scenarios. SAP COIL experts had facilitated these discussions along with Accenture experts in identifying suitable scenarios.
    • The process and methodology followed in designing and developing these services is quite unique and requires a good understanding of various SAP SOA artifacts including Integration Scenarios, Process Components, Business Objects and Global Data Types. The review check points with SAP COIL experts at each stage of the development lifecycle helped Accenture team in understanding and correcting any gaps.
    • As we have transitioned in this process over the multiple iterations, we have a greater understanding of the process and the underlying SOA artifacts and this has also helped us in evolving or base lining the SOA standards/guidelines for building the enterprise services. For example naming conventions, etc.

    Overall, I would consider PdES as a very successful joint initiative between Accenture Innovation Center and SAP COIL We have not only enabled our Industry and Technology groups on the SAP SOA concepts but have built deep skills in designing and developing Enterprise Services."

    image

    The previous blogs of this series focused on modeling and defining services using the Enterprise Services Builder which is part of the Enterprise Services Repository toolset. Starting with this blog we will show you in the following three blogs how the design time WSDL created during this process may be implemented using different technologies: JEE, .NET and ABAP.

    This blog will show you the detailed steps of how to implement the Read Sales Order service using JEE technology on SAP NetWeaver Composition Environment 7.1 EhP 1. The development toolset is provided within SAP NetWeaver Developer Studio.

    So continue on your PdES journey and have a look at the screencam:

     

    image

     

    The detailed storyboard is outlined in the following:

     

    Steps in SAP NetWeaver Developer Studio:

     

    • Configure the connection to the Enterprise Services Repository
    • Configure the connection to the SAP NetWeaver Composition Environment 7.1 EhP 1 server
    • Configure web services runtime
    • Create a JEE project
    • Create an EJB project
    • Import WSDL from Enterprise Services Repository into EJB project
    • Generate Java Skeleton for imported WSDL
    • Implement Java Skeleton
    • Build and Deploy JEE application

    Steps in SAP NetWeaver Administrator:

     

    • Configure web service endpoint
    • Test web service using Web Services Navigator

     

    For more details on providing stateless web services see SAP Help Portal. Navigate to Providing, Discovering and Consuming Services -> Providing Web Services -> Providing Web Services in Java Applications -> Providing Stateless Web Services 

     

    Below you find the sample code used in the service implementation implementing the skeleton method readSalesOrder(...):

     

    //Creating a sample Sales Order structure:
    SalesOrderByIDResponseMessageSync out_MDT = new SalesOrderByIDResponseMessageSync();

    SlsOrdByIDRespMsgSyncSlsOrd out_MDT_SlsOrd = new SlsOrdByIDRespMsgSyncSlsOrd();
    out_MDT_SlsOrd.setID(salesOrderByIDQuery_sync.getSalesOrderSelectionByID().getID());

    XMLGregorianCalendar calendar = new XMLGregorianCalendarImpl(new GregorianCalendar());
    out_MDT_SlsOrd.setDate(calendar);

    List out_MDT_item= new ArrayList();
    SlsOrdByIDRespMsgSyncSlsOrdItm item = new SlsOrdByIDRespMsgSyncSlsOrdItm();
    item.setID("10");
    out_MDT_item.add(item);
    out_MDT_SlsOrd.item = out_MDT_item;

    out_MDT.setSalesOrder(out_MDT_SlsOrd);

    //Create a log element reflecting successful processing:
    //[For documentation see SAP GDT catalog in PDF version on sdn.sap.com]
    NOSCLog log = new NOSCLog();

    log.setBusinessDocumentProcessingResultCode("3");
    log.setMaximumLogItemSeverityCode("1");

    List logItemList = new ArrayList();

    NOSCLogItem logItem = new NOSCLogItem();
    logItem.setSeverityCode("1");
    logItem.setNote("Read operation for sales order was successful.");
    logItemList.add(logItem);
    log.item = logItemList;

    out_MDT.setLog(log);

    return out_MDT;

     

    The correct usage of the Log data type for non-technical error reporting is described in the corresponding section in the SAP Global Data Type catalog in PDF format.

     

    Ride in the tube of SOA and learn how to deliver your services based on SAP's modeling and definition methodology. Stay tuned for more information and move on in your PdES journey with the next blog on "Implementing Services in .NET".

    In the following the links to all other blogs from the PdES Architecture Series (Not all blogs are published yet):

    1. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Service - Introduction (Part 1)
    2. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Service - Outlining the ‘way to deliver' services (Part 2)
    3. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Service - Modeling Services (Part 3)
    4. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Defining services (Part 4)
    5. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Leveraging a wizard to automate Service Creation (Part 5)
    6. Implementing Services in Java (This Blog)
    7. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Implementing Services in .NET (Part 7 - provided by Nakisa)
    8. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Implementing services in ABAP (Part 8)
    9. SAP Co-Innovation Lab Architecture Series: Custom Development delivered Custom Enterprise Services (Part 9)
    10. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Summary (Final Part)

    image

    The previous blog of this series - SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Service - Modeling Services (Part 3)- showed the path of creating the model entities (process component model, business object, etc.) for the Read Sales Order scenario. The next step is to derive the actual service definition from the model ultimately receiving a design time WSDL.

    The Enterprise Services Builder (ES Builder) tool as part of the Enterprise Services Repository (ESR) remains the working environment for the steps of

    • Creating service interface and operation definitions
    • Assigning the previously created model entities to the respective definitions
    • Creating Message Types and Fault Message Types
    • Creating Message Data Types
    • Detailing the message structure down to the element level
    • Data typing

    The attached screencam is targeted to guide you through exactly these steps building upon the ESR content that has been created in the screencam in the previous blog on service modeling.

    We recommend reading Dirk Richtsteiger's and Michael Seubert's SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Summary (Final Part). It gives a great introduction to the concept of Global Data Types (GDTs). Here you will find as well the link to the GDT catalog in PDF format. This PDF document not only provides the actual catalog of Core Data Types (CDT) and GDTs with detailed documentation. In the introduction the overall topic of data typing is very well described. So continue on your PdES journey and have a look at the screencam (please note the corrections below):

    Screencam Defining Services

    http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/library/uuid/302cbcdc-342c-2c10-d19d-a4de4273041c

    Correction note: The intermediate data types (IDT) in the screencam have been erroneously derived from the message data type (MDT), e.g. SlsOrdByIDQryMsg_syncSlsOrdSelByID. This is not correct: The IDT names must be derived from the message type (MT) name. In the example above the IDT name should be SlsOrdByIDQry_syncSlsOrdSelByID. The naming in the storyboard below are correct.

    The detailed storyboard is outlined in the following:

     

    • Create a Repository Namespace

    Name: http://mycompany.com/

     

    • Create a Service Interface

    Name:  SalesOrderProcessingManageSalesOrderIn

    Namespace: http://mycompany.com/

    Category: Inbound

    Interface Pattern: Stateless

    Description: "Groups operations that read, create, change, delete, or update an House shipment, or parts of it."

     

    • Delete automatically created default operation

    Name:  SalesOrderProcessingManageSalesOrderIn

     

    • Create an Operation

    Name "ReadSalesOrder"

    Mode: Synchronous

    Description: "Reads a unique sales order instance"

     

    • Create model assignments of service interface and operation to corresponding definition entities

     

    • Define Operation Structure and create Message Types

    Request Message Type: SalesOrderByIDQuery_sync

    Response Message Type: SalesOrderByIDResponse_sync

    Fault Message Type: StandardMessageFault

     

    • Create Message Data Types

    Request Message Data Type: SalesOrderByIDQueryMessage_sync

    Response Message Data Type: SalesOrderByIDResponseMessage_sync

    Fault Message Type - Standard Data: ExchangeFaultData

     

    • Define Message Data Type Structure

    Request Message Data Type structure: 

    Content node: SalesOrderSelectionByID

    Typed by: Intermediate Data Type (IDT) SlsOrdByIDQry_syncSlsOrdSelByID
    [Type name derived using abbreviation concept based on GDT AbbreviationCode]

    Only element: ElementID

    Typed by: GDT NOSC_SalesOrderID

     

    Response Message Data Type structure:

    Message Data Type Structure

     

    Ride in the tube of SOA and learn how to deliver your services based on SAP's modeling and definition methodology. Stay tuned for more information and move on in your PdES journey with the next blog on "Leveraging a wizard to automate Service Creation".

    In the following the links to all other blogs from the PdES Architecture Series (Not all blogs are published yet):

    3. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Service - Modeling Services (Part 3)
    4. Defining Services (This Blog)
    5. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Leveraging a wizard to automate Service Creation (Part 5)
    6. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Implementing Services in Java (Part 6)
    7. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Implementing Services in .NET (Part 7 - provided by Nakisa)
    8. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Implementing services in ABAP (Part 8)
    9. SAP Co-Innovation Lab Architecture Series: Custom Development delivered Custom Enterprise Services (Part 9)
    10. SAP Co-Innovation Lab Architecture Series: Partner-delivered Enterprise Services - Summary (Final Part)

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