Karthi Project Report Doc

8/2/2019 Karthi Project Report Doc

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AUTOMATION OF JE ANALYTICS PROCESS IN ERNST AND YOUNG

ABSTRACT:

Journal entry data analytics are one of the main functions of IMAS unit in Ernst and

Young. It is mandatory to check the JE data in the countries like United States. General Ledger,

part of ACL Analytic Essentials, is a collection of pre-packaged analytic scripts that enhance an

auditors effectiveness to identify errors and fraud in financial statements. Thus the auditors in

Ernst and young US need to be done the JE testing. Ernst and Young formed the skilled

employees in data analytics team to perform this process. Thus the cost involved in this process

is huge.

Thus automation of certain functions of this process helps the processor for doing such

projects and the management in terms of cost effectiveness. This project starts with first

identifying the need for automation and then identifies the areas to be automated. After

collecting the information by direct observation method and structured interview method, the

new system was designed and built using the Ernst and Young technical resources. Then the

effectiveness of the new system was measured using GAP analysis, Cost-Benefit analysis,

System Integration testing and Functional testing has been done. It has been found to be more

efficient than the existing system in terms of economically, functionally and technically.

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Table of Contents:

1. Introduction----------------------------------------------------------------------------------------- 7

1.1. Introduction ----------------------------------------------------------------------------- 8

1.2. Company Profile----------------------------------------------------------------------- 9

1.3. Objective--------------------------------------------------------------------------------- 11

1.4. Scope of the study--------------------------------------------------------------------- 11

2. Review of Literature------------------------------------------------------------------------------ 12

3. Methodology------------------------------------------------------------------------------------- 14

3.1. Research Methodology-------------------------------------------------------------- 15

3.2. Research Design---------------------------------------------------------------------- 16

3.3. Limitation------------------------------------------------------------------------------- 29

4. Data Analysis------------------------------------------------------------------------------------ 30

4.1. GAP Analysis--------------------------------------------------------------------------- 31

4.2. Cost-Benefit Analysis----------------------------------------------------------------- 32

4.3. Functional Testing--------------------------------------------------------------------- 33

4.4. System Integration Testing---------------------------------------------------------- 34

5. Conclusion---------------------------------------------------------------------------------------- 35

5.1. Conclusion------------------------------------------------------------------------------ 36

5.2. Suggestion------------------------------------------------------------------------------- 36

6. Appendix------------------------------------------------------------------------------------------ 37

6.1. Bibliography---------------------------------------------------------------------------- 38

6.2. Questionnaire--------------------------------------------------------------------------- 38

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1. INTRODUCTION

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1.1. Introduction:

Data analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data

with the goal of highlighting useful information, suggesting conclusions, and supporting decision

making. Data analysis has multiple facets and approaches, encompassing diverse techniques

under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and

knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence

covers data analysis that relies heavily on aggregation, focusing on business information. In

statistical applications, some people divide data analysis into descriptive statistics, exploratory

data analysis, and confirmatory data analysis. EDA focuses on discovering new features in the

data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on

application of statistical or structural models for predictive forecasting or classification, while

text analytics applies statistical, linguistic, and structural techniques to extract and classify

information from textual sources, a species of unstructured data. All are varieties of data

analysis.

Data integration is a precursor to data analysis, and data analysis is closely linked to datavisualization and data dissemination. The term data analysis is sometimes used as a synonym for

data modeling.

Data Analytics

Data analytics (DA) is the science of examining raw data with the purpose of drawing

conclusions about that information. Data analytics is used in many industries to allow companies

and organization to make better business decisions and in the sciences to verify or disprove

existing models or theories. Data analytics is distinguished from data mining by the scope,

purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated

software to identify undiscovered patterns and establish hidden relationships. Data analytics

focuses on inference, the process of deriving a conclusion based solely on what is already known

by the researcher.

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Ernst & Young is a global organization of member firms in more than 140 countries,

headquartered in London, UK

Early history

Ernst & Young is the result of a series of mergers of ancestor organizations. The oldest

originating partnership was founded in 1849 in England as Harding & Pullein. In that year the

firm was joined by Frederick Whinney. He was made a partner in 1859 and with his sons in the

business it was renamed Whinney Smith & Whinney in 1894.

In 1903, the firm ofErnst & Ernst was established in Cleveland by Alwin C. Ernst and

his brother Theodore and in 1906 Arthur Young & Co. was set up by the Scotsman Arthur

Young in Chicago.

As early as 1924 these American firms allied with prominent British firms, Young with

Broads Paterson & Co. and Ernst with Whinney Smith & Whinney. In 1979 this led to the

formation of Anglo-American Ernst & Whinney, creating the fourth largest accountancy firm in

the world. Also in 1979, the European offices of Arthur Young merged with several large local

European firms, which became member firms of Arthur Young International.

Mergers

In 1989, the number four firm Ernst & Whinney merged with the then number five,

Arthur Young, on a global basis to create Ernst & Young.

In October 1997, EY announced plans to merge its global practices with KPMG to create

the largest professional services organization in the world, coming on the heels of another

merger plan announced in September 1997 by Price Waterhouse and Coopers & Lybrand. The

merger plans were abandoned in February 1998 due to client opposition, antitrust issues, cost

problems and difficulty of merging the two diverse companies and cultures.

EY had built up its consultancy arm heavily during the 1980s and '90s. The U.S.

Securities and Exchange Commission and members of the investment community began to raise

concerns about potential conflicts of interest between the consulting and auditing work amongst

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the Big Five and in May 2000, EY was the first of the firms to formally and fully separate its

consulting practices via a sale to the French IT services company Cap Gemini for $11 billion,

largely in stock, creating the new company of Cap Gemini Ernst & Young, which was later

renamed Capgemini.

Global structure

EY is the most globally managed of the Big Four firms. EY Global sets global standards

and oversees global policy and consistency of service, with client work being performed by its

member firms. Each EY member country is organised as part of one of four areas:

EMEIA: Europe, Middle East,India and Africa

Americas

Asia-Pacific

Japan

Each area has an identical business structure and one management team that is led by an

Area Managing Partner is part of the Global Executive board.

Services

EY has four main service lines and share of revenues in 2010:

Assurance (47%): comprises Financial Audit (core assurance), and Fraud Investigation

& Dispute Services.

Advisory Services (17%): consisting of four subservice lines: Actuarial, IT Risk and

Assurance, Risk, and Performance Improvement.

Tax Services (27%): includes Business Tax Compliance, Human Capital, Indirect Tax,

International Tax Services, Tax Accounting & Risk Advisory Services, Transaction Tax.

Transaction Advisory Services (TAS) (9%): includes commercial, financial, real estate

and tax due diligence, mergers & acquisitions, valuation & business modeling, corporate

restructuring and integration services.

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http://en.wikipedia.org/wiki/Capgeminihttp://en.wikipedia.org/wiki/Europehttp://en.wikipedia.org/wiki/Middle_Easthttp://en.wikipedia.org/wiki/Middle_Easthttp://en.wikipedia.org/wiki/Indiahttp://en.wikipedia.org/wiki/Africahttp://en.wikipedia.org/wiki/Americashttp://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Europehttp://en.wikipedia.org/wiki/Middle_Easthttp://en.wikipedia.org/wiki/Indiahttp://en.wikipedia.org/wiki/Africahttp://en.wikipedia.org/wiki/Americashttp://en.wikipedia.org/wiki/Japanhttp://en.wikipedia.org/wiki/Capgemini


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1.3.Objective:

Primary Objective:

To Automate the JE Analytics process in E&Y.

Secondary Objective:

To reduce the performance and cost issues in JE Analytics process.

To improve the efficiency of JE analytics process

To provide more useful informations to the auditor for their better decision

making by using customized reporting tool.

1.4. Scope of the study:

This project mainly focuses on the Journal Entry analytics process in Ernst and Young

and it does not interfere the decision authorities of the auditors, it only provide the valuable data

to the auditors for better decision taking.

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2. Review of Literature

Review of Literature:

a. Fraud Prevention And Detection For Credit And Debit Card Transactions (IBM

white paper):

The key value of adopting BRMS to provide fraud detection capabilities lies in the

flexibility that this methodology offers from an installation and a business-use perspective.

BRMS offers the ability to use a common platform to address fraud issues throughout an

organization, removing the need to identify different solutions and platforms to tackle credit

card, debit card, check and money-laundering fraud. BRMS provides a user-friendly point and

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click environment that helps business users to create and modify fraud detection rules "offline."

Rules can be created, modified and tested quickly and then deployed to a production system

when ready. This enables institutions to react quickly in their effort to keep pace with fraudsters.

New detection policies can be activated in hours, instead of months, helping to reduce lost

revenue and increase customer satisfaction.

b. Risk Analysis on the Fly: Fast Markets, Complex Portfolios (Tabb Group):

In response to unprecedented market stresses in the past year, highly complex financial

organizations - such as large multi-strategy hedge funds and bulge bracket banks - need to

enhance their data management capabilities, principally for purposes of on-demand portfolio risk

analysis. This facility with data management can, in large part, be established by extending a

unified and high-performance technical architecture to the entire enterprise.

c. Analysis of Data from Recurrent Events, Cary, North Carolina, USA

Recurrence data consists of the times to any number of repeated events for each sample

unit, for example, times of recurrent episodes of a disease in patients or times of repair of a

manufactured product. The sample units are considered to be statistically independent, but the

times between events within a sample unit are not necessarily independent nor identically

distributed. The data are usually censored in the sense that sample units have different ends of

histories.

Time-to-event data have long been important in many applied fields. Many models and

analysis methods have been developed for this type of data. It describes methods for the analysis

of recurrent events data. Nonparametric methods involving extensive use of graphics for the

analysis of such data.

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3. Methodology

3.1. Research Methodology:

Applied research in administration is often exploratory because there is need for flexibility in

approaching the problem. In addition there are often data limitations and a need to make a

decision within a short time period. Qualitative research methods such as case study or field

research are often used in exploratory research.

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There are three types of objectives in a marketing research project.

Exploratory research or formulative research

Descriptive research

Causal research

Exploratory research or formulative research: The objective of exploratory research is to gather

preliminary information that will help define problems and suggest hypotheses.

Exploratory research is a type of research conducted for a problem that has not been clearly

defined. Exploratory research helps determine the best research design, data collection method

and selection of subjects. It should draw definitive conclusions only with extreme caution. Given

its fundamental nature, exploratory research often concludes that a perceived problem does not

actually exist.

Since JE Analytics is new emerging area, not much study is carried out and also the current

problems like Performance, quality and cost are not defined clearly

3.2. Research Design Steps:

1. Study the current JE analytics process in E&Y:

2. Identify the areas to be automated (Where the manual efforts can be reduced).

3. List all the possible information needed by the auditor from JE Analytics

4. Build the framework for the new system.

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5. Perform the feasibility study (Economical and Technical study).

6. Built the new system model (common to all the subareas).

7. Compare the new system model with old system. Identify the effectiveness of the

new system.

8. Selection of Platforms and softwares.

1. Study of Existing system:

Existing system was studied by using the structured direct interview method and

direct observation of the system.

Direct Observation method:

It consolidates the JE amount for each account number and compares it with the trail

balance details.ACL scripts were pre-written for perform the above operations and

prepare the report contain the difference account details.

These scripts are more standardize, so processor just edit the standard scripts with the

values for performing roll-forward and preparing reports. Also it prepares the various

reports related to audit. This reports prepared using JE details.

Below are the current systems process flows of JE Analytics,

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Reports:

Below are the reports are generated while the final deliverables are sent to the auditors for their

reviews.

1. List Of Journal Entry Line Items With Zero Amounts; Excluded From Further Processing

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Validation Process and

report creation from JE

data

Validation

Results and

Reports

Formatte

d JE fileFormatte

d TB file

Cleanse and format the

JE, TB and COA files to

the standard

JE FilesTB Files


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2. Trial Balance Adjusted By EY;Closed Income/Expense To Retained Earnings

3. Trial balance roll forward summed by account number

4. Trial balance roll forward summed by account number; differences only

5. Detail entries for accounts with differences.

6. Statistics on amount all records, system only, manual only.

7. Classification on company, account type, GL account, effective date, entry date, source,

currency, preparer, period, system/source, account class, preparer/account class,

preparer/system/source, approver.

8. List of trial balance items with a blank GL account number, GL account description,

account type, account class.

9. List of journal entry line items with a blank GL account number, GL account description,

account type, account class, effective date, entry date, preparer, approver, journal number,

period, journal description, source, company, system/manual flag, and currency.

10. List of journal entries that do not foot to zero.

11. Classification of journal entry records without account descriptions

12. Preparer summary profile; summary totals by period; current fiscal year journal entries

13. GL account summary profile; summary totals by period; current fiscal year journal

entries

14. Account type/account class summary profile; summary totals by period; current fiscal

year journal entries

15. Preparer/source summary profile; summary totals by period; current fiscal year journal

entries

16. Company summary profile; summary totals by period; current fiscal year journal entries

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17. List/detail of journal entries where the absolute value of the total debits or credits is

greater than the specified scope; summarized

18. List/detail of line items where the absolute value of the amount is greater than the

specified scope.

19. List of professional fee line items where the absolute value of the amount is greater than

the specified scope.

20. List/ detail of related party /inter company line items where the absolute value of the

amount is greater than the specified scope.

21. List/ detail of line items with key phrases identified; where the absolute value of the

amount is greater than the specified scope.

22. List/ detail of line items where the entry date is > = # days before period end; and is

greater than the absolute value of the specified scope.

23. List of line items made in subsequent period; where the absolute value of the amount is

greater than the value of the specified scope.

24. List of journal entry line items with specified unusual pairs of account types; where the

absolute value of the amount is greater than the specified scope.

25. Number patterns; journal entry line items summarized on last 3 digits of amount; sorted

in descending order by count

26. List/ detail of line items identified number patterns

27. List/ detail of random sample items; where amount0

KNOWN ISSUES IN THE CURRENT JE ANALYTICS PROCESS:

Effective date error:

Prob: Below are occurred when run the Validation script.

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Turn Around: It was due to assign the CLIENT_JE_EFF_DATE as Blanks. It was changed to

CTOD ('01/01/1900','MM/DD/YYYY'). Since the default value for JE_Eff is the above value.

Numerical Expression Error:

Prob: The Beg Bal and End Bal are in ASCII format, Conversion from ASCII to DEC gets failed

Turn Around: DEC for decimal conversion for Beg BAL and End Bal are changed to VALUE

Customized Report:

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For generating the customized reports than standard. Processor needs the coding knowledge in

ACL for altering or creating the own script for preparing the reports

Time for performing the process issue:

Normally it takes 15 to 17 hours for performing the validation and Reports generation.

Issue in Big Project:

Syntax errors in the high volume projects are shown after a long time run of the project, it kills

most of the processors time.

Structured Interview Method:

Below are the consolidated results from structured interview method.

1. Current systems performance:

20 Respondent said current system needs to be automated for better performance.

5 Current system is fine for use.

2. Improve the current sys:

10- Improved the system to Business Intelligence level.

10 -Reduction in known errors is sufficient.

5 No need to improve, cost incurred for new system is higher.

3. Advantage/Disadvantage in the current sys:

Advantage:

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Pre-written script

Less coding knowledge

Disadvantage:

Difficult to identify the reason for error

For customization, high level of coding knowledge needed.

Difficult to execute the high volume project.

ACL is not suit for high volume handling

Difficult to import the print image files.

4. Time take to correct the errors:

o 0-10 mins 5

o 10-20 mins 7

o 10-30 mins 10

o 30 and above 3

5. Other issues

Not responding 14

Data Spill over 10

Difficult to execute 2

Coding issues -5

6. Suggestion

Like BI, it must be more customized 12

Less work to processor 10

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2. Identify the areas to be automated

Based on the above Direct interview method and the Structured interview method. The areas

needs to be automated are founded. They are,

Uploading of the files

Handling of frequent errors

Mapping of the fields

Reports

Report customization.

Language other than ACL

3. List all the possible information needed by the auditor from JE Analytics

List Of Journal Entry Line Items With Zero Amounts; Excluded From Further

Processing

Trial Balance Adjusted By EY;Closed Income/Expense To Retained Earnings

Trial balance roll forward summed by account number

Trial balance roll forward summed by account number; differences only

Detail entries for accounts with differences.

Statistics on amount all records, system only, manual only.

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Classification on company, account type, GL account, effective date, entry date,

source, currency, preparer, period, system/source, account class, preparer/account class,

preparer/system/source, approver.

List of trial balance items with a blank GL account number, GL account

description, account type, account class.

List of journal entry line items with a blank GL account number, GL account

description, account type, account class, effective date, entry date, preparer, approver,

journal number, period, journal description, source, company, system/manual flag, and

currency.

List of journal entries that do not foot to zero.

Classification of journal entry records without account descriptions

Preparer summary profile; summary totals by period; current fiscal year journal

entries

GL account summary profile; summary totals by period; current fiscal year

journal entries

Account type/account class summary profile; summary totals by period; current

fiscal year journal entries

Preparer/source summary profile; summary totals by period; current fiscal year

journal entries

Company summary profile; summary totals by period; current fiscal year journal

entries

List/detail of journal entries where the absolute value of the total debits or credits

is greater than the specified scope; summarized

List/detail of line items where the absolute value of the amount is greater than the

specified scope.

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List of professional fee line items where the absolute value of the amount is

greater than the specified scope.

List/ detail of related party /inter company line items where the absolute value of

the amount is greater than the specified scope.

List/ detail of line items with key phrases identified; where the absolute value of

the amount is greater than the specified scope.

List/ detail of line items where the entry date is > = # days before period end; and

is greater than the absolute value of the specified scope.

List of line items made in subsequent period; where the absolute value of the

amount is greater than the value of the specified scope.

List of journal entry line items with specified unusual pairs of account types;

where the absolute value of the amount is greater than the specified scope.

Number patterns; journal entry line items summarized on last 3 digits of amount;

sorted in descending order by count

List/ detail of line items identified number patterns

List/ detail of random sample items; where amount0

3.3.4. Build the framework for the new system:

3.4. High Level Diagram for the new system is,

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DB

Serve

r

Appln

Serve

r

User Interface

Front endsystem


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5. Feasibility study (Economical and Technical study):

Economic Feasibility Test:

An economic feasibility test focuses on returns and costs of a proposed plan in both the

short and long-term. An economic feasibility study (EFS) should consider investment and

operating costs, the time value of money, risk and uncertainty, quality of available data,

and the sensitivity of assumptions. An economic feasibility study should demonstrate the

net benefit of the proposed course of action in the context of direct and indirect benefits

and costs to the organization and to the general public as a whole. An EFS makes a

business case, prepares analytical worksheets and other necessary supporting

documentation. EFS should be required for both pilot and long-term activities, plans and

projects.

Cost-based study: It is important to identify cost and benefit factors, which can be

categorized as follows: 1. Development costs; and 2. Operating costs. This is an analysis

of the costs to be incurred in the system and the benefits derivable out of the system.

Cost-Benefit analysis was carried out for doing this study, the result is positive (Please

refer the Data Analysis part for the Cost-Benefit analysis result).

Technology and system feasibility

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The assessment is based on an outline design of system requirements in terms of Input,

Processes, Output, Fields, Programs, and Procedures. This can be quantified in terms of

volumes of data, trends, frequency of updating, etc. in order to estimate whether the new

system will perform adequately or not. Technological feasibility is carried out to

determine whether the company has the capability, in terms of software, hardware,

personnel and expertise, to handle the completion of the project when writing a feasibility

report, the following should be taken to consideration.

Ernst and Young provided the following resources to carry out this automation.

1. Software Application server (shared with IMS appln)

2. Database Database server (Shared with IMS appln)

3. Human resource VB Developer and SQL coder.

4. Network Chennai local LAN connectivity.

6. Built the new system model:

A use case insoftware engineeringand systems engineeringis a description of steps oractions between a user (or "actor") and a software system which lead the user towards

something useful. The user or actor might be a person or something more abstract, such

as an external software system or manual process.

Use cases are a software modeling technique that helps developers determine which

features to implement and how to gracefully resolve errors.

Within systems engineering, use cases are used at a higher level than within software

engineering, often representing missions orstakeholdergoals. The detailed requirements

may then be captured in system requirement diagrams or similar mechanisms.

Use case Steps for this automation project:

1. Login using the processor user id and password.

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2. Verify the user in the user list data. Three time chance for incorrect password before

it gets locked.

3. After the successful login, processor gets the home processing page.

4. It contains the new project link, and the history of project links.

5. Once he clicks the new project link.

6. Provide the Client information and dates for processing in User Interface front screen.

7. Import the files in User Interface site.

8. Click auto-format of files.

9. If step2 success, Click import for processing button.

10. If Step2 fails, Format and import for processing button.

11. Provide the reports needed in UI by selecting the dropdown box.

12. Provide the scopes if needed and click submit button.

13. Data will be stored in database.

14. Click validation only button only, then it performs validation process

15. Click Validation and reports for Validation and reports.

16. Stored procedure invokes as per the inputs provided in UI.

17. If Step 9 process, then export the reports for verification of validation results in UI.

18. If Step 10 process, then exports all the needed reports in Excels or Pdf for final

deliver in UI.

19. Auto updates the SharePoint forms for project updating.

20. Any person can get the project from their application using the project code.

7. Identify the effectiveness of the new system.

Effectiveness of the new system are measured using Functional testing, System

Integration testing.

Functional testing:

Functional testing is a type of black box testing that bases its test cases on the

specifications of the software component under test. Functions are tested by feeding them

input and examining the output, and internal program structure is rarely considered (Not

like in white-box testing).

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Functional testing differs from system testing in that functional testing verifies a

program by checking it against ... design document or specification, while system testing

validate a program by checking it against the published user or system requirements.

Functional testing typically involves five steps:

The identification of functions that the software is expected to perform

The creation of input data based on the function's specifications

The determination of output based on the function's specifications

The execution of the test case

The comparison of actual and expected outputs

Functional testing was carried out for this automation project (Please refer the data

analysis part for functional testing results).

System Integration testing:

System integration testing is the process of verifying the synchronization between two or

more software systems and which can be performed after software system collaboration

is completed.

This is a part of the software testing life cycle for software collaboration involving

projects. This kind of software consumers run system integration test (SIT) round before

the user acceptance test (UAT) round. And software providers usually run a pre-SIT

round before Software consumers run their SIT test cases.

System integration testing was carried out for this automation project (Please refer the

data analysis part for System integration testing results).

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3.5. Limitation:

This system does not meet all the criteria as mentioned in the requirement due to the

technical feasibility of the software used. The database should be junk if it is not purged

weekely.

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4. DATA ANALYSIS

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4.1. GAP Analysis:

Sl. NoCase (As per

Requirement)

Case (In new system)Met the

Requirement(Y/N)

Turn

around

1Uploading of All

types of filesAble to upload all types

of filesY

2Handling of

frequent errors

Numeric/Characterexpression errors are

auto-corrected and themessage will be display

Y

3Mapping of the

fields

Auto mapping andManual Mapping offields are available

Y

4Report

customizationAll the reports are not

customized.N

Raw dawill beextractefrom thsystem

andprepar

the repousingexcel

5 Data Spill overThis issues has beenremoved in the new

system

Y

6 Time of Execution 1-10 mins Y

7 Effective Date IssueError message poped

up, Manual edit ofdates

N

Errormessagpoped u

Manuaedit odates

8Reports(All the

standard Reports)All the standard reports

are able to extractY

Interpretation:

All the requirements are tested with the new system, for some of the requirements are not met

have the turn around to work out.

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4.3. Functional Testing (Test Case):

Sl.No

Test case Pass/Fail Remarks

1Welcome screen revokes the Project screen(If user id is correct)

Pass

2Welcome screen revokes the Project screen(If user id is wrong)

Pass

3After clicking the new project link, itrevokes the new project screen

Pass

4After submitting all the details in the newproject screen, it revokes the status barscreen

Pass

5

If the process cancelled in-between, it

return to the New project screen with allthe details Pass

6

After the completion of validation, itrevokes the complete project processscreen, where it have the report selectionscreen

Pass

7After submitting the Complete projectProcess menu it redirects to the processscreen

Pass

8After the completion of Processing, itrevokes the complete project screen, where

the reports are available for download

Pass

9If the history project clicked, it redirectedto the correct project status screen

Pass

10If the back button is clicked it redirected tothe correct last menu screen

Pass

11Revoke of the project done by others in thesearch project option

Pass

12

Authorized project cannot beupdated by the others are restricted

if the user ticks "Restrict others touse"

Pass It is disabled for other users

13

Authorized project cannot beupdated by the others are restrictedif the user unticks "Restrict others to

use"

Pass It is editable for other users

Interpretation:

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5. CONCLUSION

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5.1. Conclusion:

From the above analysis of the automated system for JE analytics process shows that the

new system has been designed and implemented with operationally, functionally and

economically feasible for use.

Below are some of the key results of the new system,

The new system has been built as per the requirements collected from the users

The new system is cost benefit than the existing system

The new system modules are integrate with all the modules built

The new system has been built with all the functional cases.

As conclude, this new system has more effective than the existing system (using ACL). It

has been built with the more user-friendly usage of the system.

5.2. Suggestion:

Below are the recommendations or suggestion given to the Ernst and Young for doing

better system for processing the JE Analytics data.

Business Intelligence (BI) is recommended for more customized and effective

system.

An ambiguity in the data leads to difficult in processing, thus standardization in

the input data will more helpful for the processor to access.

Data warehousing and Business intelligence improves the effectiveness of the

system

It also helps to improve the customization of creating the reports.

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6. APPENDIX

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6.1. Bibliography:

Uma Sekaran, Research Methods for Business, Wiley India Pvt. Ltd, Fourth

edition

Hell locks, Software Engineer practices, Oxford publication.

www.ey.com

http://emisfard.com/site-promotion/site-promotionjournal-entry-1/

https://www.ohloh.net/p/database-analytics/

http://accountinginfo.com/study/je/je-101.htm

6.2. Questionnaire (Direct Interview Method):

1. How feel about the current systems performance?

2. What is your suggestion to improve the current sys?

3. What is advantage/Disadvantage in the current sys?

4. How much time it take for you to correct the errors like

numeric/Character expression error?

5. What are all the other issues faced during execution of project?

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6. Please provide the suggestion to improve the system?

Documents

Karthi Project Report Doc