Assessment 2: Data Cleansing & Conversion

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Assessment 2: Data Cleansing & Conversion (20%)

Overview of Tasks 

Task: Data Cleansing and Conversion

Variant	Target Template
BCO5651-Variant 1	202

Details of the task 

During software implementations data transfer and the set-up of periodic/permanent interfaces is one of the most costly and time-intensive work packages. Several market studies have shown that, on average, data transfer accounts for 20% of implementation costs. In individual cases it can exceed that amount. In order to perform the initial data transfer from non-SAP systems, you need to efficiently carry out the following steps:

1. Identify data to be transferred in the legacy system and, if possible, eliminate (Clean up/filter) obsolete data and inconsistent data records at the same time.
2. Extract relevant data and write it to an ASCII file.
3. Convert the data from ASCII format to the object-specific data format required by SAP.
4. Process the converted data file based on BAPI, batch input or direct input technologies using SAP Load Programs (load).
5. Run your own check programs (if available) to further examine the results of the data transfer. You can compare information such as the number of open items or the number of materials in both systems. Basic consistency checks are run during loading, as R/3 does not allow the creation of inconsistent data sets.

You will be given two files (both in the single spreadsheet file), the first a file containing 199 records (File 1) of customer data. This is the file that will be converted. The second file contains about 10 records (File2) and becomes the template file. Using a package cleanse the 199 customer records. Use another package to convert the customer data into the same format as your template file. 

What to hand-in

•   File of cleansing
•   File of conversion
•   Word file with assumptions and screen captures describing the process of cleansing and conversion in full.
•   Final output file (in xls or txt or csv format) after cleansing and conversion

Introduction

KKAE is an industrial company that is undergoing an SAP implementation The rollout has reached the phase of data conversion and migration. This deliverable aims to show the procedures of how KKAE Pte. Ltd handled the data cleansing and data mapping based on a list of employees who were using SAP system in KKAE. The conversion is based upon a set of business assumptions.

 Details

Two files are used in this activity. The first file is a list of employees who are using SAP system. The list contains their contact and SAP access information. Some of the information is duplicated, and some information contains human-unreadable characters or invalid data. The other file is a template that shows the required layout format of the final output list.

 The main objectives of this activity were as follows:

• Mark all the duplicated data with proper error flags.
• Mark all the missing data with proper error flags.
•  Verify the human-unreadable characters, then take proper action on it; either remove the human-unreadable character or correct the unreadable character.
• Verify and mark all the invalid data with proper error flags, then correct it.
• Once the data is cleansed, map the data based on the template’s pre-defined layout and create the final list.

Preparation 

Download Mapforce or become familiar with the software package you are using to complete this task

Assessment Criteria and Submission

Please read the rubric set up for this assessment task carefully. These are the criteria that you will be assessed against for this task. It is important that you read and understand the rubric before you attempt to write this essay. Understanding the criteria written in this rubric will help you clarify what we are looking for in this assessment. 

The rubric for this task can be viewed in unit guide.

Business Assumptions

The following is the list of business assumptions made in this activity.

• Data backup has been performed on the employees’ relevant data before the data cleansing and mapping activity. The data backup is to prepare for any incidence in wrong data manipulation.

• The head office of KKAE is located in Australia/Sing/India. Documents and reports must be displayed/printed in English. Grammar and spelling in all the documents and output reports follow the English version. For example, some of the foreign salutations will be kept.

• The final output list will serve as a global internal contact list that includes all the key employees who are involved in the SAP Business-By-Design Project. As it is a contact list, some data such as the contact numbers, must be stated in complete format with country code, area code, followed by the phone number.

• KKAE has started to use SAP system since 1996.

• The data source was extracted on 31 March 2012.

• Before the data extraction activity, a junior staff had executed a wrong program. That wrong program duplicated some data in the database. It has also shifted some data to the wrong fields. In addition, some data has been overwritten by wrong data or un-readable/ASCII codes.

• Each row of record represents a single employee and the record stores the employee’s contact and SAP related information. All the rows are unique and there should be no duplication.

Data Mapping Example

Header of Source (S) File:

Header of Template (T) File:

College of Business-BCO5651 Del 2 – Data Cleansing & Conversion Mark Sheet

Semester2020
Team no/Template
Name/number/Signed
Breakdown

Make Sure your Template is the same as your group number

Handed up Assignment cover sheet with % breakdown	YES – NO
Displayed Field Mapping in Word	√ YES –NO
Completed and handed up cleansed file	√ YES –NO
Completed and handed up converted file	√ YES –NO
Word file describing how you completed cleansing & conversion	√ YES –NO

Exceeds Deliverable Requirements HD

Meets All Requirements D

Meets Most Requirements C

Meets Pass Requirements P

Does not Meet Requirements N

Presentation		HD	D	C	P	N
Displayed Field Mappings	√√	Ex Field Map	Very Good Field Map	Good Field Map	Limited Field Map	No Field Map
Explained Business Assumptions	√√	Ex Assumptions	Very Good Assump	Good Assump	Limited Assump	No Assump
Separated incomplete records	√	All separated and stored	Most separated and stored	Some separated and stored	Separated but not stored	None separated
Cleansed Data	√√	Perfect clean	One or two examples	Mostly clean	Limited clean	Not cleaned
Removed Duplicates	√	All dupes sep and stored	Most dupes sep and stored	Limited dupes sep and stored	Limited dup sep but not stored	All dup remain
Final data in Correct Order	√√	Correct order	Mostly in order	Some out of order	Incorrect template	Many incorrect
Correct Data Structure-no of fields	√ √	Excellent	Very Good	Good	Acceptable	Poor
Data Converted	√√	Excellent	Very Good	Good	Acceptable	Poor
Documentation Showing Explanations, Procedures & Assumptions	√√	Excell doc professional	Very good doc	Good doc	Acc doc	No doc
√ = 1 mark		x1	x.8	x.6	x.5	x0
Total (20)

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