2.0 Business Process

In the business side they have some checks/data quality processes and the purpose of this analysis is to reduce the number of cost objects that have not been used for 18 months that will lead to clear database, reduced risk of amounts allocated to obsolete CO objects, less chance of errors during month end close and less ad-hoc workload during peak periods.

On a quarterly basis the business do a clean up of inactive objects and this scope we are focusing in the cost center data.

Please see more detail information about the business process in here: Quarterly clean-up of inactive cost objectsCapture the business process that the application supports . This can be describe through a process diagram or a business capability model.

2.1 Challenge/Opportunities

Clearly articulate the specific problem or opportunity that the application is addressing within the business by leveraging from data. This should be a concise and well-defined statement that captures the essence of the challenge or opportunity that the application is trying to solve by providing insight from the dataThis dashboard will allow the business to have a data quality check and clean up process more efficient, quick and user friendly using our Qliksense solution instead only the BW query and do some manual effort to have the KPI's and analyze the critical cases. 

3.0 Application Feature Overview

Information about the existent features in the application.

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.

5.0 Functional Specification

5.1 Dashboard 

---

4.1 Glossary

BSA	Stands for Business Support Activity. To know more information about the BSA Business Support Activity (BSA).
Cost Center	A cost center is a specific organizational unit within a company that is responsible for incurring costs. It represents a location, department, or function where expenses are generated. Cost centers are used for tracking and controlling costs, allocating expenses, and monitoring budget compliance.
GBU	Stands for Global Business Unit. The concept for GBU doesn't exist in SAP but it’s a requirement to have in BW since in BFC we have this definition.
SRM7	The person responsible of a cost center is accountable for the elements allocated in the cost center. The companies using SRM7 for purchasing should have a SRM7 user ID (=BIP user ID) entered in this field, starting with 5, such as 50000000. To know more about this KPI we can check the the Finance Data & Reporting wiki Rules - Cost Center Responsible - FAQ.
ZCBS	ZCBS is the code of the alternative cost centers hierarchy that is mainly used for reporting purposes . It's the CBS Structure. Please check in the Finance Data & Reporting wiki more information about this hierarchy Rules - Cost Center ZCBS hierarchy

5.0 Functional Specification

5.2 Data Input

1. Data Sources

1.  
   1. BW Query BW_QRY_C_COSTCTR_0001 - Cost Center Master Data query and the query used for the frontend is QV_BW_QRY_C_COSTCTR_0001:
   2. List of active GBU's - This is filtering only for the Active GBU KPI;
   3. List of companies in scope - This is filtering only for the SRM7 KPI

2. Transformation Rules (for each of the data source in the previous point)

1. 1. QV_BW_QRY_C_COSTCTR_0001 with the General filters:
      1. Cost_Center_Authorization_Scope__Key_ = SCO and ECO;
      2. Obsolete_object=0  ( "0" means the cost center it's active, the "1" means the cost center it's blocked/inactive and "#" should not exist but we have cases maybe it was some direct creations in BW and we don't have an assignment nevertheless, the scope for the business it was decided to only filter for the active cost center.)
      3. Source_System = PF1 Client 020, WP1 Client 400 and PI1 Client 020.
      4. CO AREA from WP1 was excluded Z010
   2. After specific filter are applied to the query depending on each KPI:
      1. Active GBU’s: we consider the information from excel file with the active GBU’s and compare with the field BFC GBU (technical CPFCTR1_2) if it’s the same it’s assigned if not then we consider not assigned;
      2. ZCBS: field 2_Function_key (technical C_FUNCT_2) is not equal to # and EDISC If the C_FUNCT_2 = # or EDISC => Test failed. (Qlik code: where match(_2_Function_Key, '#','EDISC') is not assigned the rest it’s assigned.
      3. SRM7: Based on the position responsible field (technical name C_POSIT) has 8 digits and the first 3 digits (left to right) they need to start with “500”. If the output respect this condition it’s “Assigned” if not “Not Assigned”.
      4. BSA: if(Cost_Center_BSA_Group__Key_= '#','Not Assigned','Assigned') as BSA.
         
   3. For the List of the active GBU's they were based in an excel file (see below) and added directly in Qliksense.

More information:

5.3 KPI's Definitions 

5.4 Visualization

• Additional Information

5.1 Advanced Analytics

if already existed put the link to the wiki page of the user documentation 

5.2 Data Input

• Data Sources (including the nature of the data and what is it needed for)
  • Business data (actuals, facts, customer data, transactions…)
  • Reference data (hierarchies, lookup tables… )
• Transformation Rules (for each of the data source in the previous point)
  • Extraction rules and filters
  • Exception handling rules (how do we handle when data does not come in the format we need) 
  • Enrichments (normally joins) 
  • Aggregation rules

Image Removed

• If data is sourced from the data ocean, the Multidimensional modeling of the data at conceptual level including fact and dimension tables to be captured or the link to the documentation of the corresponding data mart to be provided. 

5.3 Pre-processing 

Includes the details of the operations performed on the raw data to the data set that is useable for analytics. Data Cleaning, Data Reduction, Data Transformation, and Data Integration are types of preparation tasks. 

5.4 Modelling 

Analytics type: Identify and list the key features or functionalities that the application offers. These could include recommendation systems, predictive analytics, natural language processing, image recognition, etc. descriptive, prescriptive, predictive,..

End to end pipelines:  Procedures and calculation steps to make the advanced analytics pipelines 

Algorithm: Machin learning algorithms,  data mining techniques, mathematical modeling used for each step (high level). It can include the type of model (e.g., regression, classification, neural network), the algorithm used,

Data flow: Input and output of each step 

5.5 Results 

User flow:

If applicable, use diagrams or flowchart tools to create visual representations of the user flows. Start with a high-level overview of the flow and then drill down into more detailed flows for specific tasks or features.

If applicable, indicate where users provide input to the machine learning model. This could involve uploading data, entering text, making selections, or interacting with visualizations.

If applicable, consider how user feedback or actions can impact the machine learning model or the user flow. For example, if users can provide feedback on model predictions, include this in the flow.

Output:

 Capture where the machine learning model provides outputs or results to users. This might involve displaying recommendations, predictions, insights, or visualizations generated by the model.

5.1 Artificial Intelligence

5.4.1 Reports

Data Set Table/Adhoc Report fields:

6.0 System view (Architecture)

The purpose of this part is to describe the physical components that supports the functionalities of the product. From that point of view, this part should capture and visualizes the physical components of the data products such as backend, front end, data providers, libraries for ML models, etc. 

7.0 Non-functional Descriptions 

7.1 Usability

Usability is about the ease with which a User can learn to start using the solution and the ease with which they can use the system.  In addition to ease of learning and ease of use, usability also includes areas such as ease of recall, error avoidance and handling, accessibility among others e.g., 99% of metadata entry Users who have use the Maintenance Dashboard should be able to change filters, extract etc., when required.  Maintenance data will be centrally stored in the Google Cloud platform, which will be available to other applications e.g., and Dashboards if needed.

7.2 Regulatory Compliance

Software systems must comply with legal and regulatory e.g., GDPR requirements, this can change depending on country, organisation industry and / or region.  The software systems must be secure from unauthorized access.  The Maintenance Dashboard will comply with Solvay’s regulations and compliance e.g., access only granted to authorized Users.

7.3 Security

Security refers to essential aspects that assure a solution and its components will be protected against unauthorized access or malware attacks.  Important considerations related to security aspects of a system are User authentication, User authorization or User access privileges, data theft, malware attacks, data encryption, and maintaining audit trails, e.g., only Users with administrator access shall be able to create new accounts and assign data access privileges to the new accounts e.g.,

• All data will be encrypted in the dashboard
• Only authorised Users / Administrative Users will be able to access data.
• Maintenance data will be split between either SCO or ECO, and Users will only have authority to one Entity data.

7.4 Performance

Performance defines how fast a software system or a particular section of it responds to certain User actions under a certain workload.  In most cases, this metric explains how long a User must wait before the target operation happens e.g., the page renders, a transaction is processed, etc., given the overall number of Users now.  Performance requirements may describe background processes invisible to Users, e.g., backup and speed of data transfers. 

7.5 Reliability

Reliability is the ability of a solution or its component to perform its required functions without failure under predefined conditions for a specified time / period.  Reliability can possibly be specified in terms of average time system runs before failure occurs, percentage of operations completed successfully within a time / period, maximum acceptable failure probability, or number of failures within a period.  Reliability aspects are in reference to (but not limited to) evaluation of the system to be considered as reliable, classification of reliability defining failures vs. regular failures, and the impact of failure on business operations.  The Maintenance Dashboard will display data from the previous refresh of data.   

7.6 Scalability

Scalability refers to the degree to which a solution can evolve to handle increased amounts of work.  The increased amount of work could be in terms of the user base, transactions, data, network traffic, or other factors e.g., the system should be able to handle an additional load of a maximum of 5,000 Users every month for the next 6 months without any noticeable performance impacts.  

7.7 Compatibility

Interoperability is the degree to which the solution is compatible with other components.  It is a measure of how effectively the system interoperates with other software systems and how easily it integrates with external hardware devices.

Interoperability aspects to be discussed during elicitation are in reference to (but not limited to) software systems to be interfaced with along with data / messages to be exchanged and any standard data formats, hardware components to be integrated with, and any standard communication protocols to be followed e.g., Order Management system will push the order file into a secured file transfer protocol server from where it will be loaded into the system through a daily job.  To guarantee between Google Cloud platform and SAP BW Queries e.g., BW_QRY_MVPMOR01_0002, Solvay has introduced a new tool called Xtract (Xtract).

7.8 Availability

Availability is the degree to which the solution is operable and accessible when required. It is a measure of time during which the system is fully operational e.g., available for use and sometimes included as a Service Level Agreement (SLA) considering its criticality to the business, e.g., the system shall be at least 99% available on weekdays between 09:00 to 18:30 Central European Time (CET)The availability of the dashboard should be 24 hours during 7 days per week.

7.9 Refresh of the Data

Frequency, data, and time of the data refresh in the data product. The data is refresh on a daily basis at 6:0 a.m CET (the BW query load it's 5:00 a.m CET).