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IBM Operational Decision Manager V8.7.1 Announced

A couple of weeks ago IBM announced the release of IBM Operational Decision Manager V8.7.1. The latest release delivers further enhancements for business user experience in Decision Center and Decision Server Rules, enhance platform operations for Decision Server Insights, and enhanced performance. The planned availability date for IBM ODM V8.7.1 is May 15, 2015.

Decision Center and Decision Server Rules now includes simpler deployment operations with the inclusion of Java Execution Object Model (XOM) management from Decision Center for decision services. Java XOMs can be managed in Decision Center as decision center resources. This enhancement will result in the capability to build a complete Rule Designer workspace from Decision Center. Governance is made easier with the ability to create and reuse simulations from within validation activities in Decision Governance Framework. Other highlights in Decision Center include a definition file to declare custom roles and groups, and LDAP connections for dynamic management for custom roles. This translates to no longer needing to repackage the Teamserver EAR and redeploy the application in order to map the user roles. Finally, the user experience is improved in the Business Console with a better layout for test reports and easier navigation as a result of a redesign to the breadcrumb bar.

Additionally, the IBM Operation Decision Manager Advanced Edition supports new capabilities for Decision Server Insights. This includes easier testing with the ability to define and run test scenarios without programing. The Insight Inspector interface usability has been enhanced with the capabilities to include the list of rules fired on an event. It is now also easier to deploy from Insight Designer. Decision Server Insight now has support for local languages to define business rules. In IBM ODM 8.7.1, memory usage and CPU resource consumption has been improved as a result of performance tuning on the database persistence.

See the announcement for more information.

Social Network Analysis in Action – Crime Ring Fraud Detection

This is a continuation of our previous discussion on getting started with Social Network Analysis (SNA). So now that we can do some SNA with NodeXL, how do you now go out and catch the bad guys?  Well, remember, SNA is a network of entities.  So let’s take auto insurance fraud for example and show how just you and NodeXL are not going to uncover the Mafia network up the street.  In a typical auto insurance claim you are going to have a claimant or a person making the claim, you might have another driver or passenger that could also be making a claim.  You could have a tow truck operator, body shop, healthcare provider, lawyer, claims adjuster, witnesses etc. that could all be part of a claim.  The general auto industry rule of thumb is that around 1 in 10 claims have some sort of fraud or abuse or stated differently 90% of the claims are legit while 10% are suspicious.  Now we know that SNA is a network of entities, we can network all of the parties involved in a claim.  Below is an actual example from NodeXL from a client’s claims.  The red lines is a very prolific crime ring that was detected using SPSS Modeler that I highlighted manually within NodeXL.  The remaining networks are a combination of bad guys and good guys.

SNA graph

SNA does not know which is which other than there is some sort of a connection going on.  Only through data mining and predictive modeling can you determine which networks are just part of normal business and which are possibly fraudulent.  In another post I will go through how this model worked but a high level it was tasked with finding crime rings not networks.  A ring can be thought of as an independent group of 2 or more entities like a terror cell for example.  A network is a connection of multiple entities possibly rings or other connected entities usually controlled centrally.  A mafia or organized crime network would be an example.  What this graph shows is why traditional SNA using just graphs was not the silver bullet that we had all hoped for and that we still needed predictive modeling.

In the next post I will start going over getting into more detail with SNA and SPSS. If you are looking for some data to start playing with, here a couple of sources at Stanford and Arizona State that you might find useful.

If I had a Trillion Dollars … Would I Spend it all Online?

It’s not every day you hear the word “trillion” unless you are in the B2B Online Retail business. New analysis from Frost & Sullivan, Future of B2B Online Retailing, reveals that B2B online sales will account for close to 27 per cent of total manufacturing trade, which is likely to hit 25 trillion $ by 2020.* And according to Forrester: 75% of B2B Buyers prefer convenience of purchasing from a website vs a sales representative and 93% of B2B Buyers prefer online purchasing vs sales representative. In addition, an omni-channel experience is just as important to B2B buyers as it is to B2C buyers.

As an avid online shopper this number is impressive to say the least. Tomorrow is my birthday and one of my dearest friends shares the same day and when we worked at the same company for many years, we spent 10 birthdays together at some industry event away from family and friends. He always managed to make the day special no matter where we were and it was nice to have someone to celebrate with. My friend has long since retired and lives in the UK and I am late sending a card. A few years ago, sending online flowers or gift baskets from across the pond was virtually impossible. I was pleasantly surprised to go to one of my favorite online gift vendors (1-800-flowers) to learn that not only do they ship to the UK – but my order can arrive in ONE DAY. Thanks to the B2B movement, companies are moving to ubiquitous online platforms that allow buyers and sellers from anywhere in the world to transact goods and services with ease. Thank you B2B!

But successful B2B eCommerce requires more than copying B2C companies.  The explosive growth of B2B eCommerce is forcing B2B organizations to invest in online sales channels. While B2B organizations can learn from B2C in terms of user experience, the increased complexity of B2B selling requires sophisticated workflows, pricing, product configurations, approvals and more, managed in a central platform.

With just 11 days to the IBM Amplify Conference in sunny San Diego, this new explosion in the industry makes the event even more compelling to attend with an entire track dedicated to B2B Commerce, the chance to network with over 2,800 industry leaders, execs and practitioners, and over 200 sessions that will share the latest trends and innovative solutions in customer engagement.

One of my other favorite stores that I enjoy actually stepping out in the real world and shopping in their very cool retail stores as well as online is one of our special client speakers at Amplify. Urban Outfitters will talk about how they enhanced the customer shopping experience and how they chose IBM Sterling OMS and Perficient to create a unified order process with flexibility, seamless integration across channels, and a high level of visibility and control across brands.

Other clients joining us in 11 days:

  • Varsity Spirit Fashion will talk about the new design studio, delivered by Perficient that has had 100% adoption rate by their sales teams and has been the cornerstone of their digital transformation.
  • Carhartt will share their successful B2C implementation journey on iSeries.
  • and our very own Pat Garcia was invited to run a workshop on the B2B features in IBM WebSphere Commerce FEP 8

I’m looking forward to a great event!

By the way…I am taking tomorrow off shopping online and in stores, and will finally be enjoying it with family and friends.  I wonder how many dollars I have spent online in my lifetime.  Not a trillion, but pretty close.

*Read more at:


Incident Analysis: a use case for Watson Explorer

Earlier this year, we examined how IBM Watson can help healthcare organizations analyze vast quantities of unstructured data to gain quick insights from previously untapped data sources. We found that the content analytics solution, along with IBM’s healthcare tools and accelerators, uncovers valuable intelligence from disparate healthcare data and content, including physician notes, patient surveys, call center recordings and diagnosis reports – information that is often saved in a free-form text format and is rarely used for analytics.

Incident Analysis Challenges

Most recently, Perficient looked into applying those same concepts of unstructured data and content analysis within another area challenged with increasing volumes of complex documents, asset management and heavy regulations – Incident Management, Analysis and Resolution.

Aging infrastructure, retiring knowledge workers and high turnover with existing employees is negatively impacting the knowledge base around asset management and incident resolution. Reduced infrastructure and funding for capital expenses requires companies to increase the lifespan of investments, rather than secure funds for new purchases. Creating a proactive incident mitigation plan to reduce the rate of future incidents and improve asset management would greatly reduce large infrastructure expenses and limit the risk associated with incidents.

Currently, across a variety of industries, there is very little automation within the incident reporting process space. Incident reporting usually occurs via telephone or email, and reports are manually generated. As a result, operations departments find it cumbersome to organize and process all data related to the root cause analysis of the incident event.

 How can Watson Explorer help?

Incident analysis with Watson content analytics (a component of Watson Explorer) allows investigators to identify incident relationships, visualize trends and spot correlations, with an intuitive, easy-to-use interface. An incident inspector or investigator would use the content analytics solution to identify the root cause of an incident.  Analyzed and contextualized documents are presented to the user, providing the ability to identify the most probable root cause of incidents. Documents may include inspection results, case notes, equipment/asset documentation, and maintenance logs, among others. Watson finds problems that caused several incidents and would most likely go undiscovered, not just issues with the highest frequency.


Watson allows businesses to move beyond simple incident identification and reactive resolution. With trend and pattern analysis, companies can identify which assets or pieces of equipment are most likely to cause points of failure, fixing the problematic component before it results in an incident. By reducing the rate of preventable incidents, Watson can help mitigate unnecessary risk in any industry challenged with large volumes of complex assets: energy and utilities, manufacturing, transportation, etc. By creating a proactive plan to prevent failures, the solution helps extend the life of assets and reduce avoidable capital expenditures. And by building an incident analysis platform that codifies best practices from industries and knowledge workers, companies can better adapt to a changing workforce.

World of Watson

WatsonLogoIf you’re interested in hearing more about use cases and applications of Watson – both the content analytics solution and the cognitive analytics engine, check out IBM’s upcoming World of Watson event in NYC. This event and related hackathon, run May 4-6 at the Duggal Greenhouse in Brooklyn, NY. Perficient is exhibiting at the event, along with a select group of IBM business partners that are on the cutting edge of Watson application development. We’ll provide demos of the Watson content analytics incident analysis solution covered in this post, and discuss our experiences with the Watson Explorer platform.

Big Data and the Skies

Earlier this week, I read a news article about the use of Twitter, or more accurately, the use of data collected from Twitter to prohibit a passenger from boarding a United Airlines flight.

Strangely enough, the person banned from the flight was probably among the people who should know the most about cyber-security and perceived threats.  Chris Roberts is the owner of One World Labs, which is reported to analyze cyber-security risks.

twitterMr. Roberts posted messages on Twitter suggesting he could hack into an airplane’s on-board computer systems, and as a result, United perceived Mr. Roberts to be a threat.  Upon trying to board a flight over the weekend to, ironically enough, the RSA security conference in San Francisco, Mr. Roberts was denied access to the plane.

The purpose of this blog post, as in other blog posts, is not to judge.  Whether you agree or disagree with Mr. Roberts’ actions or the actions of United Airlines’, what you can agree with is that tweets are being tracked and if there appears to be a threat, airlines among other organizations will take action.

Read the rest of this post »

Fixing COBOL layout issues using DFDL LPEX Editor


In general for message modeling COBOL  copybook layouts we use the DFDL parser within IBM Integration Bus Toolkit  to model and generate the DFDL schema . In some cases we find COBOL layout errors in generating the message model.

We might think that once a COBOL  copybook is defined in the mainframe environment and is working why would it error out ?

One of the main reasons it could happen is a COBOL  layout defined within the COBOL program itself under working storage section.

So if there is a business use case to model these kind of COBOL  layouts as DFDL schemas what we do is ask the mainframe person to do a cut and paste of the COBOL   layout in a notepad and send it across.

But in doing so there is a chance that the layout alignment might get disrupted even though the syntax of the layout in the source COBOL  program is correct.

COBOL compiler has a restriction on how the layout is defined. Few of the positional restrictions are COMMENTS in the layout should be on only on 7th column and  “01 clause” should start from “A section” which is from 8th column onwards and many more. Basically in mainframe environment COBOL  compiler will throw these errors so that the developer will fix.

So to handle the above COBOL  layout issues I noticed that we could make use of “Basic LPEX Editor” within IBM Integration Bus Toolkit which mimics the same functionality of what a COBOL  compiler does.

Read the rest of this post »

How to handle CICS requests from IBM Integration Bus(IIB)


This article will outline the necessary steps to be taken with respect to calling existing CICS programs from IBM Integration Bus ( IIB ) using COMMAREA  data structures.

The COMMAREA  specifies the name of a data area ( known as Communication Area in CICS Region) in which data is passed to a program or transaction. The maximum length of the COMMAREA  cannot be larger than 32 KB.

The CICS Request node ( which started from WMB 7 version onwards ) enables IBM Integration Bus ( IIB ) to act as a CICS Client, sending Inter Communication requests over TCPIP   to CICS  Transaction Server and receiving responses back from CICS Transaction Server.

IBM Integration Bus is an Enterprise Service Bus for transforming and routing messages between business-critical applications.

CICS Transaction Server is a widely used Application Server in distributed transaction processing environments as well as in IBM System z ( mainframe ) environments. CICS Transaction Server for z/OS provides general-purpose transaction processing meeting the needs of both large and small enterprises.

Read the rest of this post »

Clients Today Are Getting Smarter – Are You Keeping Up?

I bought a new car last month. The last time I bought a car was 10 years ago, and oh, how the times have changed. 10 years ago buying a car was not a pleasant experience; in fact it was downright stressful. My experience this year was in a word – easy. With today’s digital world, the resources readily available to help me in my buying journey were plenty. I did not have to spend nights and weekends driving from dealer to dealer reading MSRP papers taped to the front of car windows, meet with shady dealers, and get bombarded with sales pitches. Today’s digital world gave me all the information I needed to make my decision based on my wants and needs. In today’s digital world, the customer is not just always right; the customer is always on.

Our team is getting ready for IBM Amplify 2015 where we have a Gold Sponsorship. This year, instead of the Smarter Commerce Global Summit, IBM is having more of a customer event/user conference where both executives and practitioners will participate in more hands-on type workshop sessions, where customers/users can gain or enhance their Commerce skills. In fact, Perficient was invited to create and deliver a workshop during the event. In addition, our clients will be joining us in three other sessions to share their business journey with real world examples including:

• Urban Outfitters will talk about delivering a unified superior client experienceamplify blog image
• Carhartt will share their successful B2B implementation
• And Varsity Spirit Fashion will talk about how they transformed their business and achieved a 100% adoption rate with their sellers.

If you have not registered for the event, I encourage you to attend IBM Amplify to learn how to turn your relationships into results and:
• Network with over 2,800 industry leaders, execs and practitioners.
• Get hands-on with the latest technologies and solutions.
• Learn how to engage customers again in 200+ sessions spanning 4 tracks.
• And much more…

I am looking forward to this new hands-on event, and I can’t lie, who can resist sunny San Diego in May?

I wonder what my car buying experience will be 10 years from now.

Tips on Getting Started with Social Network Analysis in SPSS

shutterstock_81647656Part 1:  What is Social Network Analysis and how do I get started quickly?

I am going to be sharing with you the path that I took to get myself up to speed on Social Network Analysis (SNA) using SPSS Modeler.  If you are new to SNA, or just curious on what SNA is all about, hopefully you find this useful. I will walk you through starting from scratch to some use cases where I have successfully used SNA in various predictive models and data mining projects to uncover criminal activity.  I will also show some examples of SNA in SPSS.  In future posts I will get into some more detail on how to do SNA within SPSS Modeler and how to incorporate into your predictive models. Read the rest of this post »

Posted in Business Analytics

IBM Pure App – Create Custom VSP using for OMS 9.4

The attach document to this blog posting is on developing IBM Pure App custom virtual system patterns(VSP) using technology.

The focus of the document is to assist customers in coming up to speed on Pure App v2(vsys).

The document demonstrates how to develop a custom VSP to implement IBM Sterling OMS 9.4. The implementation of OMS 9.4 in this document was achieved by using the components and patterns that come with PureApp. So customization was keep to a minimum and delivery time was also keep to a minimum.


IBM Pure App – Create CustomVirtual System Pattern using for OMS 9.4

Posted in News