Marlabs Test Digest March 2014
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Marlabs Test Digest March 2014

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Of late, we have been seeing greater interest and enquiries on Test digest. This is from both, within and outside the company. Thank you for your continued support. Coming to Market trends, a recent ...

Of late, we have been seeing greater interest and enquiries on Test digest. This is from both, within and outside the company. Thank you for your continued support. Coming to Market trends, a recent IDC study projects a 2013-2017 forecast for the worldwide cloud testing and automated software quality (ASQ) software-as-a-service (SaaS) market, which experienced 37.7% growth, from 2011. IDC expects a huge annual growth rate of 31.1% for 2013-2017, resulting in projected 2017 revenue of $1 billion from this service alone. Another trend which has been steadily increasing is the phenomenon of “Crowd-sourced” test-ing. This consists of delegating software testing to a community or hiring testers through an online forum. Among the advantages it provides is that testing is carried out by a number of different testers from different places rather than by hired consultants or professionals. Through this forum, we will continue to share news and information on some of these trends apart from the core test-ing areas.

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Marlabs Test Digest March 2014 Marlabs Test Digest March 2014 Document Transcript

  • March 2014 Marlab’s I N S I D E T H I S I S S U E : Business Intelli- gence : Quality Perspective 2 Quality News & Views 5 Cartoon Space 6 Volume VII From the Editor … Of late, we have been seeing greater interest and enquiries on Test digest. This is from both, within and outside the company. Thank you for your continued support. Coming to Market trends, a recent IDC study projects a 2013-2017 forecast for the worldwide cloud testing and automated software quality (ASQ) software-as-a-service (SaaS) market, which experienced 37.7% growth, from 2011. IDC expects a huge annual growth rate of 31.1% for 2013-2017, resulting in projected 2017 revenue of $1 billion from this service alone. Another trend which has been steadily increasing is the phenomenon of “Crowd-sourced” test- ing. This consists of delegating software testing to a community or hiring testers through an online forum. Among the advantages it provides is that testing is carried out by a number of different testers from different places rather than by hired consultants or professionals. Through this forum, we will continue to share news and information on some of these trends apart from the core test- ing areas. Happy Reading !!
  • T E S T D I G E S T © 2 0 1 4 M A R L A B S S O F T W A R E P V T L T D P A G E 2 What is BI: For any analysis and or decision making there will be fair amount of study to be done on incidents, and this can only happen if the incidents directly or indirectly available recorded, without which making any decision would be quite risky and neither carry any surety on the success that would neither yield any autopsy nor lesson learnt, hence in order to make decisions we should have the incidents recorded and for business the incident registry is the " Business Data" and the way the data is been interpreted is called "Intelligence" and the whole story leads to business intelligence. Hence given the above need of niche technology made every growing organization to look at their data intelligently and hence the scope for the ‘Business intelligence’ as the technology. In order to build any ‘Business Intelligence’ solution the business data should be collected to the common pool technically known as ‘Data Warehouse’ and the sources of the same could be from intra or inter or- ganization(s). How it is built: The technically architecture of the Business intelligence would typically be as below. (Images : Copied from the internet, as available already then why should create one) Data Sources: since the data could be sourced intra or inter organization as mentioned above, since the data may or may not be in same format unless the data collected is of same format there cannot be a col- Mahesh Rayappa G BUSINESS DATA INTELLIGENT DATA Current year the sales increased by 12% Current year sales is higher by 12% with 100 employees, but last year it was 100% with 10 employees This year hired 20 candidates Current year 50 people resigned VS 2 last year Bangalore division sales are up by 50 % Overall sales is down by 6% in India BI is about providing the right data at the right time to the right people so that they can take the right decisions” – Nic Smith
  • lection, hence one of the main design step of BI solution is makes the data of similar format to make the decisions and would be done by process called ETL (extract, transform and load). ETL : This is 3 step process which extracts data from the various sources, formats to be in similar for- mat along with the business operations done uniformly and then loads data to the common pool i.e. Data warehouse. Data Ware House: is a common repository holding the data prepared by the ETL (gate keeper) with all the necessary curing of data done. Data Marts: The ETL process stores data according to the subject matter necessary for Ex: HR data is stored only with employee, Sales Data mart only stored with sales and invoices etc. BI Analytics: Reporting layer which actually reads data from the marts and portrays according the business needs for ex. current year VS previous year. Snippet of sample of BI Report: Why we need QA in DWBI projects: In any other technology its ‘garbage in and garbage out’.. but in BI it is going to be ‘garbage in and exponential garbage out’ Given the typical design architecture as illustrated above there are many integration points with immense possibility of defects referred as ‘software bugs’ and those could be due to  Business requirement complexity  Data changing over period  Data Quality  Improper handling the process  No traces of data being lost  Unaware of the actual of business need  Technically infeasibility  Inexperience  Inter technology integrations  Tool incapability (hence there will be lot of workarounds) P A G E 3T E S T D I G E S T © 2 0 1 4 M A R L A B S S O F T W A R E P V T L T D continuation of ‘Business Intelligence ... “If you don’t have a competi- tive advantage, don’t compete!” – Jack Welsh View slide
  • P A G E 4T E S T D I G E S T © 2 0 1 4 M A R L A B S S O F T W A R E P V T L T D Hence software testing is very essential in BI projects to make sure all integration points are functioning as designed. Example of different referencing subject areas but read from single point. How to do BI Testing: No different to any other testing life cycle as mentioned below  Business and requirement understanding &Validation  Test Estimation  Test planning  Designing test scenarios and test cases from all the available inputs  Test data preparation  Test execution  Regression Testing (upon issues fixed / change request implemented)  Test Report  Release post mortem  Production support What to test in BI: Some of the noticeable metrics which needs to be checked for are listed below  Make sure complete source data is loaded to the reporting tables  No data loss  Making sure that the data is not redundantly stored  Data accuracy according to the business needs  Data integrity (Data if spilt and stored in multiple tables should be referenced as received)  Reporting valid Data  Providing intuitive data display  Usability and intuitive representation of the data  Latest analysis capability  Supporting all the historical data with the current latest data  User security w.r.t Data, Display, Privilege  Performance checks on the presentation layer  To be made to be compatible to migrate to the next version of the tool or inter technology Challenges in BI QA: Typically the BI testing not less than the developer level, more over it is time con- suming process as here we should justify the issue along with the finding out the route cause  Data quality : inconsistent data (structure of future or historic data cannot be imagined )  Lack of SQL knowledge  Real time data unavailability  QA having less privileges in the Data bases  Huge Volume and complexity of data  Fault in business process and procedures  Lack of documentation continuation of ‘Business Intelligence ... View slide
  • P A G E 5 Information 2020: Big Data and Beyond This talks about the ‘Big Data’ technology and more http://my.gartner.com/portal/server.pt?open=512&objID=202&mode=2&PageID=5553&resId=2512815&ref=Webinar-Calendar Is ETL Still Relevant in the Era of Hadoop? This presentation outlines architecture and methods developed to migrate high cost ETL processing to a low cost processing platform Hadoop. https://www.brighttalk.com/webcast/9059/91121 Webinars >> Top 10 Business Intelligence Trends for 2014 This throws some light on how data and analytics continues to accelerate, transform the staid old business intelligence http://tdwi.org/whitepapers/2014/02/top-10-business-intelligence-trends-for-2014.aspx?tc=page0 A Strategic Approach to Complex ETL Testing This paper describes effective data warehouse testing strategy http://www.querysurge.com/resource-center/white-papers/strategic-approach-to-complex-etl-testing BI - Today and Tomorrow This paper gives a perspective on the advancements in the BI technology platform expected in future. http://www.tcs.com/SiteCollectionDocuments/White%20Papers/BIPM_White_Paper_Business-Intelligence-Today-Tomorrow_120111.pdf How Five Hot Trends Are Shaping the Future of Business Analytics How cloud-based services, predictive analytics, social media analytics, big data, and mobile computing are combining to take business intelligence beyond traditional boundaries http://tdwi.org/whitepapers/2013/03/anatomy-of-the-new-decision-how-five-hot-trends-are-shaping-the-future-of-business- analytics.aspx?tc=page0 Strategies for Testing Data Warehouse Applications This article talks about strategic decision-making through Data Warehousing www.information-management.com/issues/20070601/1086005-1.html eBooks , Whitepapers & Columns >> T E S T D I G E S T © 2 0 1 4 M A R L A B S S O F T W A R E P V T L T D Big Data Essentials: Removing the Skills Barrier In this you will learn about A flexible and comprehensive Big Data integration platform architecture www.brighttalk.com/webcast/10477/97301 Business Intelligence Series (Session 2): Data Integration Platform vs. ETL This presentation will discuss how this need has led to the scope of ETL to evolve into that of an Enterprise Data Integration Platform http://www.techgig.com/expert-speak/Business-Intelligence-Series-Session-2-Data-Integration-Platform-vs-ETL-39 7 habits of highly *ineffective* Big Data security This talks about few security habits that expose Big Data to a breach or cause major delays/rework www.brighttalk.com/webcast/10573/104617
  • P A G E 6T E S T D I G E S T © 2 0 1 4 M A R L A B S S O F T W A R E P V T L T D RajeshSundararajan. MuraliDubutavalu. VaraprasadaraoYarra.