ANLY482 AY2017-18T2 Group13 Analysis & Findings

Home

Project Overview

Analysis & Findings

Project Management

Documentation

Main Page

DATA CLEANING & MAPPING

Manipulation and Cleaning of Training Records

In order to rectify these missing data points, we replaced the values whenever possible as the team hopes to analyze as many data points as possible. When replacement of values are not possible, records are excluded from analysis.

Missing data (Field)	Action
Start and End date	1. Attempted to find corresponding dates from exact course titles, unsuccessful 2. Attempt to take 'Create date' however inaccurate as record could have been created after actual start and end date 3. Excluded (Final action)
Category	Replaced missing data with corresponding records with exact course title

Groupings within Training Records

Using text explorer and vetting through our Sponsor, course titles were grouped in order to narrow down our analysis to specific areas. Grouping was done with a calculated field to facilitated breakdown analysis in later stage.

[Text explorer screenshot]

Groupings :

- Bulk

- HSEQ

- Equipment and Maintenance

- Miscellaneous

- Packaging

- Packaging and Raw Material

- Warehouse

- Others

Manipulation and Cleaning of Staff List

1. As the staff list provided were in separate sheets, data fields were standardized and merged into 1 data set.

Previous field name	New field name	Sheet affected
Job Code	Job Title	Dec'13 & Dec '14
Job Group	Staff Group	Dec'14
Designation	Job Title	Dec'15, Dec'16, Dec'17
Staff Grade	Staff Group	Dec'15, Dec'16, Dec'17
Section	Department	Dec'15, Dec'16, Dec'17
Department	Location	Dec '14, Dec'15, Dec'16, Dec'17

2. Standardization of variables within columns were also made to ensure consistency throughout the years.

Issue	Action taken	Sheet affected
Location column missing from sheets and location information was combined in the naming assigned to the departments (e.g. EMOS7000)	Location Group column created to extract the front letters from the department in Excel with a calculated field	Dec'13 & Dec '14
Department variables inconsistent with other years as it was combined with location. Other sheets used a single descriptive field to specify departments (e.g. EMOS Shared Services) [SNAPSHOT]	Used corresponding descriptive department name based on matching of employee number to other sheets	Dec’13 & Dec'14
Variables as Job Title was inconsistent with other years and were in the form of acronyms	Cross referenced employee number to other years and Training Records for corresponding job title	Dec'13 & Dec ‘14
Variables in Cost Centre , Citizenship and Serial Number fields were inconsistent over the years (e.g. Missing entire field) and had missing records that could not be matched with other years	As these fields were not within the scope and were advised by the Sponsor to exclude, these fields were excluded from the data analysis entirely	Dec'13, Dec'14, Dec'17

Groupings within Staff Records

As provided by the Sponsor, staff departments were grouped into the following : - Bagging - CSR - Operations - Technical Towhead - Transport - Warehouse - Others: Includes all other departments not belonging to either of the above

Mapping Staff List and Training Record data set

Employee number is unique to each employee hence it was used an identifier. While an left join was considered, a full outer join was used instead to include all the records from both data sets independent from whether there were matching employee numbers.

As distinct count of employees in the Training Record is greater than that in the Staff List, the team attributed this to the fact that there are employees who received training but left in the short term to be captured in the Stall List records.

A full outer join would : - Include employees who undergone training but left the company in a short term hence will not be traceable in the annual Staff List - Include employees who did not undergo training However, with a full outer join, crucial fields such as employee number, location etc will be duplicated and may present differing variables. Meanwhile, employee records who appeared in staff list but not training records would have null values in training employee fields.

[Screenshot of tableau]

As such additional columns were created generalize columns that combined data from both sets

1.Employee Number

2.Location Group Filter

3.Staff Department Group Filter Created to first obtain the department group information from the Training Records, if it is unavailable, information from the Staff List would be used.

4.Job Title Group Filter Upon mapping, discrepancies in the staff department groups were found. Since employees underwent training based on the job title that they are categorized for training purposes,job title group in Training Records took precedence over job title group in Staff Records.

INSIGHTS

To be updated

ANALYSIS

To be updated