Oracle R12 Applications Using LoadRunner

The Challenge

We recently load tested our first Oracle R12 release (All modules for nationwide and international wide of Oracle ERP R12). The company was upgrading to R12 from 11.5.8 largely for performance reasons.

We knew we’d be “cutting new ground” with LoadRunner on R12. This became evident with our first testrecord-and-playback, which failed even after finding and fixing all the missing correlations. We raised a ticket with HP (SR# #4622615067), and with their initial help, step by step we overcame all the nuances of coaxing vugen to record successfully, and then creatively working around its inability to recognize the full set of identifiers for a new java ITEMTREE object.

Configuring Oracle Unified Directory (OUD) 11g as a Directory Server

I used Oracle Unified Directory (OUD) Version 11.1.1.5.0 during my test deployment locally here. I tried to collect as much information possible in this post for configuration.

Ideally, there are three possible configuration options for OUD:

• as a Directory Server
• as a Replication Server
• as a Proxy Server

Directory Server provides the main LDAP functionality in OUD. Proxy server can be used for proxying LDAP requests. And Replication Server is used for replication from one OUD to another OUD or even to another ODSEE (earlier Sun Java Directory) server. You can my previous posts on OUD here and here.

In this post, we will talk about configuring OUD after installation as a Directory Server. You can read about OUD installation in my previous post here.

HP DIAGNOSTICS

Overview
Identifying and correcting availability and performance problems can be costly, time consuming and risky. IT organizations spend more time identifying an owner than resolving the problem.
HP Diagnostics helps to improve application availability and performance in pre-production and production environments. HP’s diagnostics software is used to drill down from the end user into application components and cross platform service calls to resolve the toughest problems. This includes slow services, methods, SQL, out of memory errors, threading problems and more.

Performing Manual Correlation with Dynamic Boundaries in LR

What is Correlation: It is a Process to handle dynamic values in our Script. Here the dynamic value is replaced by a variable which we assign or capture from the server response.

Ways to do correlation: There are two ways to do this Correlation.

They are as follows:

• Auto-Correlation: The Correlation Engine in LR Package captures the value and replaces it with another value
• Manual Correlation: Understanding of the Script and its response is highly needed to do this. It is bit complex to do Manual Correlation sometimes but this is always the preferred method to handle Dynamic Values in our Script

Usually the Manual Correlation is done by capturing the dynamic value which is present in between the Static left and right Boundaries.

Objective: The intention of this article is that to give a method which will be useful when we wanted to capture and handle Dynamic Values when even the Left and right Boundaries are also dynamic.

HP Ajax TruClient – Overview with Tips and Tricks

Overview

• In LoadRunner 11.5, TruClient for Internet Explorer has been introduced. It is now possible to use TruClient on IE-only web applications.

Note: This still supports only HTML + JavaScript websites. It does not support ActiveX objects or Flash or Java Applets, etc.

• TruClient IE was developed as an add-in for IE 9, so it will not work on earlier versions of IE. This version of IE was the first version to expose enough of the DOM to be usable by a TruClient-style Vusers. Note that your web application must support IE9 in “standard mode”.
• Some features have also been added to TruClient Firefox. These include:
  • The ability to specify think time
  • The ability to set HTTP headers
  • URL filters
  • Event handlers, which can automatically handle intermittent pop-up windows, etc.

• Web page breakdown graphs have been added to TruClient (visible in LoadRunner Analysis). Previously they were only available for standard web Vusers.

XML Optimization through custom Properties

1. Problem Statement:

I am creating a XML file as an output . If my source is empty, is there a way to  avoid the creation of an empty XML file?

Sample output Data with source data :

Advanced Replication Setup for High availability and Performance

In my personal opinion, Oracle leads the market in Directory Product offerings (LDAP Directories). Starting from Oracle Internet Directory (OID), to the latest Oracle Unified Directory (OUD), Oracle definitely provides variety of LDAP Directory related products for integration.

With increasing demand for mobile computing and cloud computing offering, there is a need to standardize LDAP Deployments for Identification, Authentication and (sometimes) Authorization (IAA) services. With a highly scalable, highly performing, highly available, highly stable and highly secure LDAP Directory, these IAA services will be easier to integrate with applications in the cloud or for the mobile applications.

Transitioning to a New World – An Analytical Perspective

Recently, I had the opportunity to speak at the Silicon India Business Intelligence Conference. The topic I chose for the discussion was focused on providing the BI & Analytics perspective for companies transitioning to a new world. You can view my presentation at this link –http://bit.ly/VLDDfF

The gist of my presentation is given below:

1)      First, established the fact that the world indeed is changing by showing some statistics:

• Data Deluge: Amount of digital data created in the world right now stands at 7 Zettabytes per annum (1 Zettabyte = 1 Trillion Terabytes)
• Social Media: Facebook has touched 1 Billion users which makes it the 3^rd largest country in the world
• Cloud: Tremendous amount of cloud infrastructure is being created
• Mobility: There are 4.7 billion mobile subscribers which covers 65% of world population

2)      Enterprises face a very different marketplace due to the profound changes taking place in the way people buy, sell, interact with one another, spend their leisure time etc.

3)      To ensure that BI can help business navigate the new normal, there are 3 key focus areas.

• Remove Bottlenecks – Give business what they want
• Enhance Intelligence
• End to End Visibility by strengthening the fundamentals

For each of the 3 areas mentioned above, I gave some specific examples of the trends in the BI space.

1)      For Removing Bottlenecks, the impact of in-memory and columnar databases were elaborated.

2)      For enhancing intelligence, working with unstructured data and using big data techniques were discussed.

3)      For the 3^rd point, the focus was on strengthening the fundamentals in the BI landscape.

Please do check out my complete presentation at http://bit.ly/VLDDfF and let me know your views.

Thanks for reading.

Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

Collaborative Data Management – Need of the hour!

Well the topic may seem like a pretty old concept, yet a vital one in the age of Big Data, Mobile BI and the Hadoops! As per FIMA 2012 benchmark report Data Quality (DQ) still remains as the topmost priority in data management strategy:

‘What gets measured improves!’ But often Data Quality (DQ) initiative is a reactive strategy as opposed to being a pro-active one; consider the impact bad data could have in a financial reporting scenario – brand tarnish, loss of investor confidence.

But are the business users aware of DQ issue? A research report by ‘The Data Warehousing Institute’, suggested that more that 80% of the business managers surveyed believed that the business data was fine, but just half of their technical counterparts agreed on the same!!! Having recognized this disparity, it would be a good idea to match the dimensions of data and the business problem created due to lack of data quality.

Data Quality Dimensions – IT Perspective

 

• Data Accuracy – the degree to which data reflects the real world
• Data Completeness – inclusion of all relevant attributes of data
• Data Consistency –  uniformity of data  across the enterprise
• Data Timeliness – Is the data up-to-date?
• Data Audit ability – Is the data reliable?

 

Business Problems – Due to Lack of Data Quality

Department/End-Users	Business Challenges	Data Quality Dimension*
Human Resources	The actual employee performance as reviewed by the manager is not in sync with the HR database, Inaccurate employee classification based on government classification groups – minorities, differently abled	Data consistency, accuracy
Marketing	Print and mailing costs associated with sending duplicate copies of promotional messages to the same customer/prospect, or sending it to the wrong address/email	Data timeliness
Customer Service	Extra call support minutes due to incomplete data with regards to customer and poorly-defined metadata for knowledge base	Data completeness
Sales	Lost sales due to lack of proper customer purchase/contact information that paralysis the organization from performing behavioral analytics	Data consistency, timeliness
‘C’ Level	Reports that drive top management decision making are not in sync with the actual operational data, getting a 360o view of the enterprise	Data consistency
Cross Functional	Sales and financial reports are not in sync with each other – typically data silos	Data consistency, audit ability
Procurement	The procurement level of commodities are different from the requirement of production resulting in excess/insufficient inventory	Data consistency, accuracy
Sales Channel	There are different representations of the same product across ecommerce sites, kiosks, stores and the product names/codes in these channels are different from those in the warehouse system. This results in delays/wrong items being shipped to the customer	Data consistency, accuracy

*Just a perspective, there could be other dimensions causing these issues too

As it is evident, data is not just an IT issue but a business issue too and requires a ‘Collaborative Data Management’ approach (including business and IT) towards ensuring quality data. The solution is multifold starting from planning, execution and sustaining a data quality strategy. Aspects such as data profiling, MDM, data governance are vital guards that helps to analyze data, get first-hand information on its quality and to maintain its quality on an on-going basis.

Collaborative Data Management – Approach

Key steps in Collaborative Data Management would be to:

• Define and measure metrics for data with business team
• Assess existing data for the metrics – carry out a profiling exercise with IT team
• Implement data quality measures as a joint team
• Enforce a data quality fire wall (MDM) to ensure correct data enters the information ecosystem as a governance process
• Institute Data Governance and Stewardship programs to make data quality a routine and stable practice at a strategic level

This approach would ensure that the data ecosystem within a company is distilled as it involves business and IT users from each department at all hierarchy.

Thanks for reading, would appreciate your thoughts.

 

Transitioning to a New World – An Analytical Perspective

Recently, I had the opportunity to speak at the Silicon India Business Intelligence Conference. The topic I chose for the discussion was focused on providing the BI & Analytics perspective for companies transitioning to a new world. 

The gist of my presentation is given below:

1)      First, established the fact that the world indeed is changing by showing some statistics:

• Data Deluge: Amount of digital data created in the world right now stands at 7 Zettabytes per annum (1 Zettabyte = 1 Trillion Terabytes)
• Social Media: Facebook has touched 1 Billion users which makes it the 3^rd largest country in the world
• Cloud: Tremendous amount of cloud infrastructure is being created
• Mobility: There are 4.7 billion mobile subscribers which covers 65% of world population

2)      Enterprises face a very different marketplace due to the profound changes taking place in the way people buy, sell, interact with one another, spend their leisure time etc.

3)      To ensure that BI can help business navigate the new normal, there are 3 key focus areas.

• Remove Bottlenecks – Give business what they want
• Enhance Intelligence
• End to End Visibility by strengthening the fundamentals

For each of the 3 areas mentioned above, I gave some specific examples of the trends in the BI space.

1)      For Removing Bottlenecks, the impact of in-memory and columnar databases were elaborated.

2)      For enhancing intelligence, working with unstructured data and using big data techniques were discussed.

3)      For the 3^rd point, the focus was on strengthening the fundamentals in the BI landscape.

Please do check out my complete presentation at http://bit.ly/VLDDfF and let me know your views.

Thanks for reading.

Hexaware sees strong order pipeline; 20% growth: Nishar

Atul Nishar, chairman, Hexaware, says that we remain quite positive on growing at 20% or more. We feel that if the situation improves with US elections and no debacle in Europe then the environment could only improve.

Atul Nishar, chairman, Hexaware , says that we remain quite positive on growing at 20% or more. We feel that if the situation improves with US elections and no debacle in Europe then the environment could only improve.

He also says that currently there are five deals in the pipeline and one is in the advance stage. The deals are spread across from the United States and Europe, and across major verticals like capital markets, travel and emerging verticals. And in the last nine quarters the company has signed seven large deals.

Below is the edited transcript of his interview to CNBC-TV18.

Q: Hexaware recently had a deal and there have been reports or analyst notes which suggest that the company is in conversation with potential clients for four deals and one is in advance stages. Do you think something could fructify in the near-term?

A: Currently, there are five deals in the pipeline and one is in the advance stage. The deals are spread across from the United States and Europe, and across major verticals like capital markets, travel and emerging verticals. And in the last nine quarters we have signed seven large deals.

Q: Are billings under pressure even if the deals are coming? Are they coming from tight fisted managements?

A: In over last two years, we have marginally improved our average billing on both on onsite and offshore. We don’t see any pressure on pricing on the IT industry. Repeatedly, we have guided that our pricing should be assumed to be stable.

The important point is that the client want value, greater performance, result oriented projects and fixed priced or greater commitment by off shoring companies.  Clients do want to cut their costs and get more value, but they also know if it is all done at the cost of the service provider, it will not sustain that particular situation.

Q: How much do you think is Nasscom’s 13-14% growth target under threat? What might it fall to half or high single digits?

A: Nasscom has guided for 11-14% and it is a wide enough range. In the industry we saw that some companies like mid-sized companies and companies who are scale players have also done very well. It is a mixed reason. We have seen more client specific issues coincidence for downsizing for whatever reason that may dent revenue that doesn’t mean they will not be able to grow in future.  

Q: Do you think Nasscom will hold the lower end of their 11% range?

A: That is the current optimism. So, there is no reason to believe that there is material change from the guided number.

Q: The one concern around Hexaware has been for some time that you have seen an improvement in margins, but going forward it would come under pressure because in Q3 wage hikes are expected to shave off margins to a certain extent. How do you respond to that?

A: In Q2, ours being calendar year, Hexaware reported 22.9% EBITDA which was higher than Q1. We gave normal 10% increment to all our off shore employees. The impact was absorbed in our margin and in spite of that the margin improved.

We also absorbed the significant visa costs that traditionally come in that quarter. In the coming quarter there will be onsite increase in wages. For off shore workers the date of increment is April 1 and for onsite employees the date is July 1, which remains unchanged. And we feel with this we can guide stable margins.

We are proud that at Hexaware, we have grown at higher than the industry average at good margins. We don’t believe in taking new deals by compromising on margins in any manner.

Q: So at this juncture you don't want to change your guidance of 20% dollar revenue growth any which way, up or down?

A: We remain quite positive on growing at 20% or more. We feel that if the situation improves with US elections and no debacle in Europe then the environment could only improve. 

 

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Excerpts from Midcap Radar on CNBC-TV18 Watch the full show »