1.0 Overview


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Business Context: 

The Data Quality Dashboard  is implemented on Qlik Sense, designed to serve multiple organizational domains, including Human Resources (HR), Marketing and Sales, Structured and Shared Services, Finance, Supply Chain, and Procurement.

Key Processes:

The dashboard supports critical data quality management processes across the involved domains. It includes:

  • Data Quality Monitoring: Enables users to continuously track data quality metrics, ensuring the integrity, accuracy, consistency, timeliness, conformity, uniqueness and completeness of data.

  • Failed Data View: Provides users with a centralized view of failed data records, allowing them to identify and review data quality issues that needs cleansing. 


Users Profiles with access to the Data Quality Dashboard to monitor data quality metrics and view failed data records can:

Data Stewards leads the DQ process by participating in the DQ rule creation as well as the DQ issue identification and resolution

  • Defines the DQ rules from gathered requirements and  the profiled data in a functional way and complete the rules attributes. If the rule is simple he can also implement it.
  • Continuously track data quality metrics across various DQ dimensions such as integrity, accuracy, consistency, timeliness, conformity, uniqueness, and completeness.
  • Access a centralized view of failed data records to identify data quality issues.
  • Review the failed data to proceed with the cleansing process.
  • Refine or log new issues eventually and prioritize them based on the impact/severity.

  • Lead the DQ issue resolution and align with stakeholders on solving strategy. Prepare the fixing plan and follow the cleansing /remediation process.

  • Monitor the DQ metrics and assess impact.

Rule Owner is responsible for DQ under his scope by ensures the conformity of the DQ rules and the root cause of the DQ issues. 

  • Review The DQ Rule: Approve or reject the proposed DQ rule already reviewed by business.

  • Validate the issue Root Cause found by the steward as responsible of ensuring the data quality meets the identified targets under his scope

Target Users:

Domains data stewards, data governance teams, other stakeholders.

 

Data Product Type 
  • Dashboard
  • Report
  • Advanced analytics
  • AI 
  • Others <specify which one>
Technologies
  • BW
  • Tableau
  • Qliksense
  • Talend
  • Dataiku
  • Others <specify which one>

Data Sources 

Note: list of all applications and various environment

  • SAP PF1 (Production environment)
  • SAP WP1
  • SAP PI1
  • BW (versions)
  • iCare CRM 
  • CORE CRM
  • SAP SuccessFactors



2.0 Data Quality Process


The Data Quality process and it's key activities involved can be found here.




2.1 Data Quality Dashboard Objective/Opportunities 

The primary objective of the Data Quality Dashboard is to empower data stewards and other stakeholders within each domain to maintain high standards of data quality. By implementing automated data quality rules and offering a centralized dashboard for monitoring and reviewing failed data, the dashboard provides data stewards with an opportunity to ensure that data across all domains is accurate, up-to-date, and consistent. This, in turn, supports informed decision-making and operational efficiency across the organization.


3.0 Application Feature Overview

Information about the existent features in the application.


FeatureDescriptionLatest uppdate in production (DD/MM/YYYY)










4.0 Business Objects

This section should contain a table with the business objects used in the reports with links to the business object definition in LeanIX.  The purpose is to ensure that all DA&AI Products adhere to a centrally maintained list of business objects and definitions to allow us to achieve our digital ambitions.  For any questions about business objects and LeanIX, contact Data Governance or the Enterprise Information Architect.

Data DomainBusiness Object (in LeanIX)Business Object Definition (only use when the object is not yet in LeanIX)
ex: Marketing & Salesex:  Customer 







5.0 Functional Specification


5.1 Dashboard 

The Scope, reload frequency, screens, filters and KPIs are documented in the Wiki Page for DQ QlikSense Documentation 

5.2 Rules Definitions & Data Input

Overview:
 

The Key Performance Indicators (KPIs) within the Data Quality Dashboard are defined based on data quality rules specified by data stewards from each domain. The rules define the criteria for evaluating the quality of data and are used to calculate the KPIs displayed in the dashboard. These rules are categorized under various data quality dimensions to systematically monitor and enhance data quality, and help in identifying data quality issues, thereby providing actionable insights to maintain high data quality standards.

The following rules are currently present in the dashboard. 

  1. Marketing & sales
Rule IDDQ dimensionBusiness NameFunctional descriptionSource Systems Tables
MRK-3UniquenessDuplicate customer Customers with the same name, address, VAT and Account Group. SAP PF1 
SAP WP1 		 KNA1


      2. Structures & Shared 

Rule IDDQ dimensionBusiness NameFunctional descriptionSource Systems Tables
SSR-1ConsistencyNo active plants linked to obsolete companies# of active plants linked to obsolete companies / total number of active plantsSAP PF1 
SAP WP1 

T001W

T001K

SSR-2ConsistencyNo active material codes connected to obsolete plant# of active materials in obsolete plants / total number of active materialsSAP PF1 
SAP WP1 

MARC

T001W

T001K

SSR-9ConsistencyNo active materials linked to obsolete sales org# of obsolete sales organizations linked to active material(s)/ Total number of  sales organizations linked to active materials in material sales views SAP PF1 
SAP WP1 

T001W

T001K

MVKE

TVKOT

SSR-11ConsistencyT134G - active plants linked to inactive business area# of entries with inactive business area / total number of entriesSAP PF1

T134G

TGSB

T001W


     3. Finance

Rule IDDQ dimensionBusiness NameFunctional descriptionSource Systems Tables
FIN-1ConsistencyActive CCs to L4 in the ZCBS hierarchyThe rule checks if all "Active" Cost Centers are in "Level 4" in ZCBS hierarchy EXCEPT the Cost Centers that are in EDISCXX, they are in Level 4 but they should be blocked and be Inactive. SAP BW

BW_QRY_C_COSTCTR_0001

FIN-3Consistency Accuracy of assigning "Inactive" Cost centers to the EDISCXX node  The rule checks if all Cost centers in EDISCXX node are inactiveSAP BW

 BW_QRY_C_COSTCTR_0001

FIN-4Consistency All cost centers are assigned to an active GBU Cluster The rule checks if all Cost centers are in active GBU ClusterSAP BW

 BW_QRY_C_COSTCTR_0001

FIN-5ConformitySRM7 responsible codification The rule checks if the position responsible field of the cost center has 8 digits and the first 3 digits (left to right) need to start with “500”

SAP BW

 BW_QRY_C_COSTCTR_0001

FIN-6Consistency All cost centers are assigned to a BSA The rule checks if the BSA group is assigned to the cost centerSAP BW

  BW_QRY_C_COSTCTR_0001

FIN-7Consistency Cost Centers with Profit Centers The rule checks if all the cost centers have a profit center associatedSAP BW

  BW_QRY_C_COSTCTR_0001


Detailed information on the rules with their functional, technical specifications and the data inputs are documented in  a centralized Google Sheet.


Data Quality Dimensions:


The following are the data quality dimensions with their definitions under which the KPIs are grouped to assess the quality of data within Solvay.

DimensionDefinition
AccuracyDegree to which data correctly reflects the real world
CompletenessAchieved when all the data required for a particular use is present and available to be used
ConformityAchieved when the data is conforming to a pre-defined business rule/syntax (e.g. format, type or range)
ConsistencyAchieved when data values do not conflict with other values within a record or across different data sets and sources
IntegrityEnsures that all the data in a database can be traced and connected to other data/Degree to which a defined relational constraint is implemented between two data set
TimelinessIndicates whether the data is available when expected and needed and represent reality from the required point of time (Degree to which specified data values are up to date between data change and processing)
UniquenessMeasures the number of unique values and highlights if the are any data duplicates


5.4 Visualization

Graph name

Description 

Calculations//Measures/Rules (if applicable)Scope / FiltersGraph picture






  • Additional Information

6.0 System view (Architecture)

The system view (Architecture) can be found in the technical documentations .


7.0 Non-functional Descriptions 

Please populate the relevant section and delete those that are not applicable.

7.1 Security

 The dashboard is secure from unauthorized access, access only granted to authorized Users. 

7.2 Refresh of the Data

The data is refreshed weekly, every Monday.