A statistical study on the analysis of consumer buying behavior, impacted due to the presence of Malls and Marketplaces.

To understand the impact of malls on consumer buying behaviour:

 

The consumer buying behaviour is a complex structure, where the perception of the consumer towards the factors of decision making process plays a vital role.

An effort, to understand the perception of malls on consumers mind was brought about through few specific questions in questionnaire, which are as listed below:

 

Computation and analysis:

 

To have a general understanding of the trend exhibited by the collected data, the bar graph has been plotted using the raw data for the responses of the above specified questions.

 

The forward regression analysis method was used to establish the relationship between the mall preference and other factors which may affect the consumer buying behaviour, to name few: Information, Money, Perception etc…

OpenStat software was used to analyse the data.

The statistical inferences achieved are as below:

1. The adjusted R2 was found to be 1.

2. The standard error was 0.90

3. The prob>t was found to be 0 for all the independent variables.

 

The snapshot of the output explains the overall fit:

 

Thus, the final, multiple regression linear equation to be:

Pref_Mall = -0.362 + 0.82 * Visit_Yes + 1 * Pref_Mkt

 

Where,

Pref_Mall = preference of the customer towards a mall.

Visit_Yes = will the customer visit the mall, before making any buying decision.

Pref_Mkt = affinity of the customer towards the marketplace.

 

Thus, the complex buying behaviour of the customer depends on the various attributes present in the environment and most of all, the perception of a consumer towards the factors plays a vital role in buying decision.

From the model explained above, we can also infer that:

1. Consumer has negative perception about his preference towards a mall.

2. The consumer does visit the mall to collect information about the product he is willing to buy.

——————————————

 

New Service Introduction:

 

The new product is termed as “Shopping-Mall Cart”; the cart can be used just like any cart which are seen in retail stores like “Big-Bazaar, Tesco, and Star-Bazaar etc…” the carts in retail stores are being used to collect the items by the customers and move to billing counter to get the items billed. Similarly, the “Shopping-Mall Cart” will be in dispense to the customers at the time they enter into the mall and the same cart can be used to carry their items of purchases as well as the items they are going to purchase in any of the retail stores inside the mall. The “shopping-Mall Cart” has the following advantages:

1. The customer need not deposit their belongings in every retail stores while entering the stores.

2. The customer need not carry their belongings in their hands while they are walk in the mall.

The conjoint technique was used to understand the feasibility for introducing a new product to provide service to customers who visit the mall.

Conjoint Approach:

 

The customers of the mall were interviewed to rank their preferences to a mall based on the attributes listed below:

1. Parking Space and

2. Shopping Mall Cart.

The below shown question was used to understand the preference of the customer for the above listed attributes.

The above question is designed based on ‘Pairwise Approach’ / ‘Two – Factor Approach’. The attributes in evaluation has (3 level) X (2 level) = 6 profiles. The 3 levels are constituted from attribute “Parking Availability”, the 2 level is constituted from “Shopping- Mall Cart” attribute.

The statistical inferences after doing conjoint analysis are as below:

 

Inferences:

1. The coefficient of determination, or r2, is 0.930032285 which would indicate a strong relationship between the independent and dependent variables.

2. The critical level of F is 0.09803453. Since F = 1.37314E+31 is much higher, it is extremely unlikely that an F value this high occurred by chance. The hypothesis that there is no relationship between independent and dependent variables is to be rejected.

3. The regression equation will be:

Thus, the introduction of new service “Shopping-Mall Cart” in malls will increase the consumer preference towards such malls.

——————————————-

 

Comparison between Mall and Marketplace:

To understand the consumer orientation towards the mall or the marketplace, the following attributes were considered:

1. Distance of mall and marketplace from consumer’s residence.

2. Number of monthly visits made by consumers to mall and marketplace.

3. Approximate money spent in each of the places.

4. Time spent by consumers in each of the places.

5. Few of the personal information.

Analysis:

 

The analysis of the surveyed data was done purely by using descriptive analysis – Frequency distribution analysis.

The surveyed data was scaled to interpret the data, the scales are as described below:

1. For the data related to Distance between mall / marketplace, Staple scale was used. The scale being, [ 2 , 1, -1, -2] for distances [ less than 2km, 2km to 5km, 5km to 10km, more than 10km].

 

The reason for selecting such weights being, nearer the mall / marketplace, its more convenient for the consumers to visit such places.

 

2. For the data related to Number of Visits, Staple scale was used. The scale being [ -2, -1, 1, 2] for the Number of Visits [ 0, 1 to 5, 6 to 10, more than 10].

 

The reason for selecting such weights being, more the visits more likely the customer is going to spend / gets acquainted with such places.

 

3. For the data related to Money Spent in previous visits, Semantic Differential scale was used. The scale being, [ -2, -1, 0, 1, 2] for spending [0rs, less than 500rs, 500rs to 1000rs, 1000rs to 5000rs, more than 5000rs].

 

The reason for selecting such weights being, if the customer spends no money, the malls/marketplace will not be able to earn any revenue, in such a case, it suffers loss. If the customers spend less than 500rs, the mall may not make any profits when compared to investments it has made. If the customer spends between 500rs to 1000rs, the spending may meet the necessary expenses of the mall but, need not make any profits. Contrarily, if the customer spends anything more than 1000, the malls may earn some profits.

1. For the data related to time spent, Ranking / Ordinal scale is being used. The scale being, [ 4, 3, 2, 1] for the time spending [ less than an hour, 1hr to 3hrs, 3hrs to 5hrs, more than 5hrs].

 

The reason being, if the customers take less time to make any buying decisions and leave the mall/marketplace, allows the mall/marketplace to establish more Footfalls, thereby, creating new opportunities.

The following questions from questionnaire were used to do a comparative study between the mall and marketplace:

 

The following were the inferences made after analyzing the data:

 

Thus, if a retail stores owner has to make a decision either to setup his establishment either in a mall or in a marketplace, he can opt to choose a marketplace rather than in mall, by considering the statistics listed in the table above. An opportunity lies more in marketplace, rather than in a mall.

 

 

 

 

 

 

YES NO

 

4> What do you prefer?

 

 

 

 

 

 

MALL MARKET-PLACE

 

 

Computation and analysis:

 

To have a general understanding of the trend exhibited by the collected data, the bar graph has been plotted using the raw data for the responses of the above specified questions.

The forward regression analysis method was used to establish the relationship between the mall preference and other factors which may affect the consumer buying behaviour, to name few: Information, Money, Perception etc…

OpenStat software was used to analyse the data.

The statistical inferences achieved are as below:

1. The adjusted R2 was found to be 1.

2. The standard error was 0.90

3. The prob>t was found to be 0 for all the independent variables.

 

The snapshot of the output explains the overall fit:

 

Thus, the final, multiple regression linear equation to be:

Pref_Mall = -0.362 + 0.82 * Visit_Yes + 1 * Pref_Mkt

 

Where,

Pref_Mall = preference of the customer towards a mall.

Visit_Yes = will the customer visit the mall, before making any buying decision.

Pref_Mkt = affinity of the customer towards the marketplace.

Thus, the complex buying behaviour of the customer depends on the various attributes present in the environment and most of all, the perception of a consumer towards the factors plays a vital role in buying decision.

From the model explained above, we can also infer that:

1. Consumer has negative perception about his preference towards a mall.

2. The consumer does visit the mall to collect information about the product he is willing to buy.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note: the detailed statistical computation on consumer behaviour is attached in appendix 4

New Service Introduction:

 

The new product is termed as “Shopping-Mall Cart”; the cart can be used just like any cart which are seen in retail stores like “Big-Bazaar, Tesco, and Star-Bazaar etc…” the carts in retail stores are being used to collect the items by the customers and move to billing counter to get the items billed. Similarly, the “Shopping-Mall Cart” will be in dispense to the customers at the time they enter into the mall and the same cart can be used to carry their items of purchases as well as the items they are going to purchase in any of the retail stores inside the mall. The “shopping-Mall Cart” has the following advantages:

1. The customer need not deposit their belongings in every retail stores while entering the stores.

2. The customer need not carry their belongings in their hands while they are walk in the mall.

The conjoint technique was used to understand the feasibility for introducing a new product to provide service to customers who visit the mall.

Conjoint Approach:

 

The customers of the mall were interviewed to rank their preferences to a mall based on the attributes listed below:

1. Parking Space and

2. Shopping Mall Cart.

The below shown question was used to understand the preference of the customer for the above listed attributes.

 

In the grid given below, rank your preferences with the assistance of the interviewer.

 

Note: Please rank on a scale of 1 to 10, 1 being the lowest and 10 the highest.

Please DO NOT repeat the ranks once used to help us in interpreting the data.

 

SHOPPING-MALL CART AVAILABILITY

NO

YES

PARKING AVAILABILITY

Not at all

Limited

Ample

 

 

 

The above question is designed based on ‘Pairwise Approach’ / ‘Two – Factor Approach’. The attributes in evaluation has (3 level) X (2 level) = 6 profiles. The 3 levels are constituted from attribute “Parking Availability”, the 2 level is constituted from “Shopping- Mall Cart” attribute.

The statistical inferences after doing conjoint analysis are as below:

Measure

Description

Value

se1, se2…. se4

The coefficients of attribute and constant

Se1 = Constant = 2.09

Se2 = coefficient of ShoppingMallCarts = 2.15

Se3 = coefficient of AmpleParkingSpace = 4.72

Se4 = coefficient of LimitedParkingSpace = 1.99

r2

The Coefficient of determinate

0.930032285

F

The F-observed value.

1.37314E+31

df

The degrees of freedom

2

Critical Value of F

Critical Value of F

0.09803453

 

Inferences:

1. The coefficient of determination, or r2, is 0.930032285 which would indicate a strong relationship between the independent and dependent variables.

2. The critical level of F is 0.09803453. Since F = 1.37314E+31 is much higher, it is extremely unlikely that an F value this high occurred by chance. The hypothesis that there is no relationship between independent and dependent variables is to be rejected.

3. The regression equation will be:

Shopping Mall preference = 2.09 +2.15 * ShoppingMallCarts + 4.72 * AmpleParkingSpace + 1.99 * LimitedParkingSpace

Thus, the introduction of new service “Shopping-Mall Cart” in malls will increase the consumer preference towards such malls.

 

 

 

 

 

 

 

 

 

 

 

Note: the detailed statistical computation is attached in appendix 2

Comparison between Mall and Marketplace:

 

To understand the consumer orientation towards the mall or the marketplace, the following attributes were considered:

1. Distance of mall and marketplace from consumer’s residence.

2. Number of monthly visits made by consumers to mall and marketplace.

3. Approximate money spent in each of the places.

4. Time spent by consumers in each of the places.

5. Few of the personal information.

Analysis:

 

The analysis of the surveyed data was done purely by using descriptive analysis – Frequency distribution analysis.

The surveyed data was scaled to interpret the data, the scales are as described below:

1. For the data related to Distance between mall / marketplace, Staple scale was used. The scale being, [ 2 , 1, -1, -2] for distances [ less than 2km, 2km to 5km, 5km to 10km, more than 10km].

 

The reason for selecting such weights being, nearer the mall / marketplace, its more convenient for the consumers to visit such places.

 

2. For the data related to Number of Visits, Staple scale was used. The scale being [ -2, -1, 1, 2] for the Number of Visits [ 0, 1 to 5, 6 to 10, more than 10].

 

The reason for selecting such weights being, more the visits more likely the customer is going to spend / gets acquainted with such places.

 

3. For the data related to Money Spent in previous visits, Semantic Differential scale was used. The scale being, [ -2, -1, 0, 1, 2] for spending [0rs, less than 500rs, 500rs to 1000rs, 1000rs to 5000rs, more than 5000rs].

 

The reason for selecting such weights being, if the customer spends no money, the malls/marketplace will not be able to earn any revenue, in such a case, it suffers loss. If the customers spend less than 500rs, the mall may not make any profits when compared to investments it has made. If the customer spends between 500rs to 1000rs, the spending may meet the necessary expenses of the mall but, need not make any profits. Contrarily, if the customer spends anything more than 1000, the malls may earn some profits.

 

4. For the data related to time spent, Ranking / Ordinal scale is being used. The scale being, [ 4, 3, 2, 1] for the time spending [ less than an hour, 1hr to 3hrs, 3hrs to 5hrs, more than 5hrs].

 

The reason being, if the customers take less time to make any buying decisions and leave the mall/marketplace, allows the mall/marketplace to establish more Footfalls, thereby, creating new opportunities.

The following questions from questionnaire were used to do a comparative study between the mall and marketplace:

5> How far is the MALL that you visit frequently, from your residence?

 

 

 

Less than 2 kms

 

 

 

2 to 5 kms

 

 

 

 

 

 

5 to 10 kms

More than 10 kms

 

6> How often do you visit a MALL in a month?

 

 

 

 

 

 

Not even once 1 to 5 times

 

 

 

 

 

 

6 to 10 times More than 10 times

 

7> How much did you approximately spend in the last 3 visits at a MALL?

 

 

 

Did not spend

 

 

 

Less than Rs. 500

 

 

 

Rs.500 to Rs.1000

 

 

 

Rs.1000 to Rs.5000

 

 

 

Rs. 5000 and above

 

8> How much time do you approximately spend at a MALL?

 

 

 

Less than 1 hour

 

 

 

1 to 3 hours

 

 

 

3 to 5 hours

 

 

 

More than 5 hours

 

9> How far is the MARKET-PLACE you visit frequently from your residence?

 

 

 

Less than 2 Kilometers

 

 

 

2 to 5 Kilometers

 

 

 

 

 

 

5 to 10 Kilometers

More than 10 Kilometers

 

10> How often do you visit a MARKET-PLACE in a month?

 

 

 

 

 

 

Not even once 1 to 5 times

 

 

 

 

 

 

6 to 10 times More than 10 times

 

11> How much time do you approximately spend in a MARKET-PLACE?

 

 

 

Less than 1 hour

 

 

 

 

 

 

1 to 3 hours

 

 

 

3 to 5 hours

More than 5 hours

 

Age: ___

 

Monthly Income:

 

 

 

Less than Rs. 10,000

 

 

 

Rs. 10,000 to Rs. 25,000

 

 

 

Rs. 25,000 to Rs. 50,000

 

 

 

Greater than Rs. 50,000

 

 

 

Not Applicable

 

 

The following were the inferences made after analyzing the data:

Parameter /

Attribute

Mall

 

 

 

(freq) (%ge)

Marketplace

 

 

 

(freq) (%ge)

Inference

Distance

More than 10km = 23 0.14

5km to 10km = 42 0.26

2km to 5km = 54 0.33

Less than 2km = 45 0.27

More than 10km = 10 0.06

5km to 10km = 30 0.18

2km to 5km = 40 0.24

Less than 2km = 83 0.51

Marketplace is much closer to residence for most of the samples.

Monthly

Visits

No visit = 15 0.09

1 to 5 = 131 0.80

6 to 10 = 11 0.07

more than 10 = 6 0.04

No visit = 13 0.08

1 to 5 = 89 0.54

6 to 10 = 41 0.25

more than 10 = 18 0.11

Marketplaces have more opportunity to influence on the consumer behaviour as consumers visit them the most.

Money

Spending

No Spending = 5 0.03

less than 500rs = 13 0.08

500rs to 1000rs = 61 0.37

1000rs to 5000rs = 64 0.39

more than 5000rs = 21 0.13

No Spending = 3 0.02

less than 500rs = 10 0.06

500rs to 1000rs = 63 0.38

1000rs to 5000rs = 79 0.48

more than 5000rs = 9 0.05

Consumers spend more money in marketplaces rather than in malls.

Time

Spending

less than an hour = 4 0.02

1hr to 3hrs = 109 0.66

3hrs to 5hrs = 46 0.28

more than 5hrs = 3 0.02

less than an hour = 35 0.21

1hr to 3hrs = 93 0.57

3hrs to 5hrs = 30 0.18

more than 5hrs = 6 0.04

More footfalls can be achieved in marketplace rather than in mall.

 

Thus, if a retail stores owner has to make a decision either to setup his establishment either in a mall or in a marketplace, he can opt to choose a marketplace rather than in mall, by considering the statistics listed in the table above. An opportunity lies more in marketplace, rather than in a mall.

 

 

 

 

Note: the detailed analysis data can be found in appendix 3. This also has data related to monthly salary and age.

To understand the impact of malls on consumer buying behaviour:

 

The consumer buying behaviour is a complex structure, where the perception of the consumer towards the factors of decision making process plays a vital role.

An effort, to understand the perception of malls on consumers mind was brought about through few specific questions in questionnaire, which are as listed below:

Difference between ‘==’ and ‘equals’ operator for comparison:

The comparison operator for objects may confuse some of the java novice programmers. In this article I am going to shed some thoughts on, when and where to use ‘equals’ and ‘==’ operators.

The ‘==’ operator is used to check if the two objects which are being compared are of the same object reference or not. This means to say that comparison is done on the memory location in which the objects are created / located. We use the ‘==’ operator to check whether the objects which we are being compared are the aliases or not. Object alias means, an object which is allocated on the memory and have different names to access the object location. For instance refer the sample code below:

String a = new String(“Bharath”);
 String b = new String(“Bharath”);
System.out.println(String ‘==’ operator output >—-> “ + (a == b)); // always results false, as the reference of object ‘a’ is different  from reference of object ‘b’
  

The ‘equals’ operator is used to compare the value / property contained in the comparing objects. For instance refer the sample code below:

  
String a = new String(“Bharath”);
 String b = new String(“Bharath”);
System.out.println(“String ‘equals’ operator output >—-> “ + a.equals(b));// results true because, the contents/property in both the objects ‘a’ and ‘b’ are the same.
 
example of object reference:


 

String a = new String(“Bharath”);
String b = new String(“Bharath”);
String c= b;
System.out.println(“comparison of ‘c’ and ‘b’ output >—-> “+(c==b)); //results true as object ‘c’ is reference of object ‘b’; in other words ‘c’ is an alias of ‘b’
System.out.println(“comparison of ‘c’ and ‘a’ output >—-> “+(c==a));//results false as object ‘c’ is not a reference of object ‘b’

 

 

 

The header and the footer section incorporation using Struts tiles!

Last week in my project I had an interesting situation; I had to integrate couple of navigation buttons across JSP’s. The buttons had to go into footer section of the pages. But, the existing JSP’s had no footer section. The struts-tiles is in place and had only header and body attributes defined in it. Initially, I was asked to integrate the new requirement at framework level. This decision had made my life easy all I had to do was to follow couple of steps below:

  1. Create a new footer JSP.

  2. Edit the struts-tiles configuration file and add footer attribute into definition node.

  3. If any page demanded no footer; in the value attribute for put node I had to leave it blank.

  4. Last but the least, I had to add the Javascript function for button onclick event into JSP’s.

The life was smart, but, later a decision was made not to make the changes at framework level and had to be integrated only at JSP level. Now, I had to follow few simpler steps as below:

  1. Create a new footer JSP.

  2. Edit each JSP and include the footer JSP after the body tag in JSP.

  3. Last but the least, I had to add the Javascript function for button onclick event into JSP’s.

Though the alter looks simple it’s not a wise thing to do; I thought. The processing time of JSP’s would have reduced if it was made at the framework level, as struts-tiles would have rendered the footer at server start up itself. But, this would have not been a noticeable change. To add bit more to it, the primary concept of adding of navigation into footer section itself seems irrational, as the concept of footer is lost, footer is something which is constant and needs no change when adapted across pages. So, there’s a pain of editing each JSP’s.

Thus, to conclude, I guess the alter decision was wise.

“Use header and footer when the header and footer section are constants, for example, across the pages if the menu is constant, add it into header section. Across the pages if the company details or contact info is to be displaced gracefully embrace them into footer section and it would be the wise decision to adapt them into struts-tiles by doing which we may need not edit individual pages to incorporate header or footer sections.”

SOA Implementation Using Java Skeleton And .Net Stub.

Conversion of POJO’s into Web-Services

SOA: SOA is the abbreviation for Service Oriented Architecture. It is one of the programming styles by which one can create/use and expose the services over a network.

Why do we need SOA?

The world is expanding with knowledge, in the form of data; there is an immense need for few of the legacy applications to be in phase with the new worlds data. Programmers face difficulty in maintaining and updating these legacy applications due to various reasons like internal dependencies, language obscurity, etc… In such a case, SOA seems to be an auxiliary tool which can void the programmer’s night-mare.

Using SOA programmer’s can program in such a way that the legacy system can be in phase with the new technology. SOA does this miracle by sandwiching the new technology and the legacy application with a thin layer which can interact between the two entities.

Not only in case of the legacy applications, SOA show’s it’s commanding position even in case of new technologies/ applications. Whenever two or more application needs to exchange the data, SOA provides a bridge between the applications, which overlays one of the programming challenge language dependency.

What do we achieve by implementing SOA?

By using SOA programmers can easily configure two or more application to interact and to exchange the data.

Which are the different approaches to implement SOA?

There are two approaches to implement SOA:

  1. Top Down approach: in this approach the programmers integrate the SOA structure for the existing application by creating a wrapper layer which interacts between the applications. This approach is usually used only when SOA needs’ to be integrated to the existing systems.

  1. Bottom up approach: in this case the programmers code the application, by keeping a view in the mind that the services being coded will be exposed as a service over the network. This approach is usually followed while developing a new application.

Concerns to be accounted while implement SOA:

  1. While implementing SOA, there is a requirement for the service provider and the service consumer to agree upon the data format being exchanged across the applications.

  2. Security is the main concern which has to be taken care off. Whenever a service is exposed, the system should be intelligent enough to know the consumer’s consuming the service and to log the transactions. As well as, system should expose the service only for the legitimate consumers, System should be protected from the hackers.

Software requirements:

  1. Apache Tomcat Server / JBoss.

  2. Eclipse IDE.

Configuring Eclipse for Axis2 plug-ins:

1) Download Axis2 plug-ins:

axis2-eclipse-codegen-wizard.zip

axis2-eclipse-service-archiver-wizard.zip

2) Unzip the files into eclipse plug-in folder.

1

Configuring Apache Tomcat server with Axis2 plug-in:

1) Download the plug-in:

axis2-1.2.zip

2) Unzip the file into \webapps folder which is present in Apache Tomcat installed directory.

2

3) To confirm whether the Apache Tomcat Server is configured with Axis2, hit the URL: http://localhost:8080/axis2/ .

The below shown page should appear.

3

  1. Conversion of POJO’s into Web-Services:

Steps:

  1. Once Eclipse and Server are configured, the next step will be to identify the services which you will be exposing as Web-Services.

  2. Decide whether a wrapper class is required to expose the service, if required code the wrapper class then, proceed to step 3 to create Axis Archived file (.aar file).

  3. Compile the class to generate the .class files.

  4. Right click on the “Package Explorer” window on Eclipse. Then follow the path
    New >
    Other > Axis2 Wizard.

    45Then click on next.

  5. Select the directory where the .class files for the services are present.

    6

  6. Check the “Skip WSDL” check-box.

    7

  7. Add the entire jar’s which will be required to generate the .aar files.

    8

  8. If the archive file requires any service XML files it can include. Else select the “Generate the service xml automatically” checkbox.

    9

  9. Load the class file and select the methods which you will be exposing as a web-service.

    10

  10. Select the output location where the .aar file, is to be generated.

    11

  11. When you click finish, wait for the confirmation message.

    12

    The .aar file will be successfully generated.

II. Deploying .aar file into Tomcat server using Axis2 service provider:

  1. Login into the axis2 server by accessing the URL: http://localhost:8080/axis2/axis2-admin/

    13

  2. Click on “upload service”

    14

  3. Select the .aar file and click on “upload” button.

    15

  4. Wait for the conformation message!

    16

  5. To confirm the service is uploaded hit the url: http://localhost:8080/axis2/services/listServices

    17

This confirms that the service is deployed successfully.

Finally, make sure that the library files which will be required for the service to work are deployed in lib directory.

C:\Program Files\Apache Software Foundation\Tomcat 5.5\webapps\axis2\WEB-INF\lib

18

Testing of the service using .net Client:

Steps:

  1. Add the web reference by following the path:
    Project
    Add web References.
    19

  2. Add the web reference by selecting the URL on which the web service is hosted:

    20

  3. Write the client code.

    21

  4. Run the code to see the output:

    22@ .net Client – Courtesy – Deepesh P C – NTL Bangalore.

****************** ******************


Business Intelligence 2.0

For me the march from Gen-X to Gen-2.0 has been with lots of disarray. There was a mad rush in the industry where even a single click on the network, would bring the buzz word 2.0. Pondering whether 2.0 is only about Rich Internet Applications(RIA’s) with loads of jazzy stuffs on User Interface, I have come to an assumption that 2.0 is not for the system its for the users of the system.

A decade back(1990’s) the world saw the revolution in governance, where the files, papers, man-power where replaced by intelligent systems called the computers. Those where the days, where the users of the system where satisfied by the analytic capability of the system; Those where the days, where the data played a major role and the whole focus was on the data interpretations. Now, there has been a paradigm shift in the focus of the user’s, the need of the user then now become his greed, the user of a system is demanding more out of the computer. The user sees a system as a genie. With the hardware – technology breakthroughs, there is an immense need in software – technology breakthrough, the style in which programs where written has to be changed. Now a programmer when he codes should not keep all his focus on machine capability but, also on the users of the system. The programmer has to become an user when he codes. For all these challenges, the word 2.0 seams to put some guidelines.

2.0 is not to do with any technology, computer configuration, but, its all about the approach – the guiding principles to shift the view from the data centric to the view of user centric.

Yikes, see what I mean, I am using Microsoft word pad to write this blog, when I completed the above paragraph and pressed enter key. The Microsoft word interpreted it as a points and added a bullets, but, I did not expect it. If the system was intelligent enough it should have primarily looked for 1.0 before considering 2.0 for bullets. See 1.0 is always a foundation for 2.0, 2.0 cant stand alone, it has to march hand in hand with 1.0.

As of today, there is a need for complete Business Intelligence for various sectors. Business Intelligence is not a straight forward solution for any challenges. Its a complex task and lot of analytic involved. The beautiful and toughest part of this vertical is in harnessing information from data. There are many BI tools in the market, but, the sad part of the story is most of them are on shelf. The reason behind this being, first and foremost, the BI tools are very expensive, a license for each of these tool costs an arm and a leg. The second factor, being the updates to these solutions are very cumbersome and painful. The third factor being, most of the tools in market are specialised in its own way, say a tool x is very good in analytic but when the same tool is considered for reporting, it provides very basic UI and interpretations of the data become an anguish. Now consider a tool y, it will be very good in UI, but, when it comes to synthesising the data, it becomes a programmers nightmare. It boils down to say, as of today, its an challenge to find a tool which can offer a complete satisfaction by providing a complete solution for a problem. Leaving the rest to be thought of, coming to BI today, when compared with BI2.0 dreams, its left in the middle of the desert.

BI 1.0 mainly concentrates on data, i.e., its data centric. The whole design for a solution mainly concentrates on modelling of data, very less flexibility is given for extended interpretations / interaction by the user, users have to take assistance of some other tool for complex analysis, but the satisfaction still remains unfulfilled. Overall, we can say, that the BI 1.0 as of today has just been able to replace lots of paper work and time, saving resources from nightmare of data thefts and management of file system.

BI 2.0, will be the gateway which will break the shell and force the designers and the developers to think out of the box of BI 1.0, but, reminding them not to forget the classic foundations of BI 1.0.

BI 2.0 will encourage the developers to make use of SOA, AOP. BI 2.0 will force the developers to free themselves from the cage of “data first”. Natural language processing and semantics will be basic mantra of BI 2.0, to cater to the needs of collective intelligence or social networking. I think, the march towards BI 2.0 has started off, many architectural changes in data management like In Memory Data Base are emerging, RIA seams to offer some solution to UI. BI is very much specific to a domain, there is an immense need for a domain specific tool in BI, for an instance, Ruby On Rails which is a domain specific language. BI 2.0 will be a feature rich approach which will aim to enhance the user experience with the system.

One feature, which comes up in my mind is, BI 2.0 should provide a work place for the users, in report the user should be able to drag and drop even a tiny subset of information on to his workspace, and he should be able to drag and drop the analytic feature in to his workarea. Then he submits his blue print and after processing the request the user should be able to see what he expected. This may overcome his workaround time. He need not run places to accomplish his task.

Summing up, BI 2.0 will aim at:

  • Fulfilling the user need by proving extended functionality to help the user to accomplish his job more efficiently.
  • Brings the user closer to the system and information.
  • Makes a shift from data first to user first approach.
  • It will force the developers to give more importance to user than the features.

BI 2.0 will no longer need long hands to reach the information, it will blend the user and information.