2.0 Business Process

Solvay GBUs need to receive an excel file with an automated extraction of shipment details in order to calculate their CO2 emissions.

The extraction is scheduled to happen on the 3rd of the month, with data of the previous month M-1

This file will be the input for an external tool, TK Blue, that will make the calculation in TK BlueCapture the business process that the application supports . This can be describe through a process diagram or a business capability model.

2.1 Challenge/Opportunities

Using Talend also allows us to sort the fields in an order that makes it easier to figure out the shipment flows (due to the arborescence in BW, the attributes appear after the main dimension.

Talend also cleanses special characters to ease the legibility by TK Blue by removing (hash signs #), solo dots, solo commas => dots and commas in the middle of a value are ok

Talend is used to separate the unit of the measures from the value to help the legibility of the unitsClearly 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 data. 

3.0 Application Feature Overview

Information about the existent features in the application.

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Source query: [DI_BW_QRY_MVSDTR01_0010] BW - Shipment costs detailed analysis TIERS (Core Query)

Attention point: there is a QV version but that is not fully aligned with the core version. The DI version has been created for this purpose based on the core query

Scope: Solvay GBUs

Monthly extraction based on: actual completion month

All the dimensions and measures listed below, with the same order and labels

Fields that have the properties Key and Text have the second column empty => we need them to be renamed with the suffix (Key) and (Text) for the usage of the external tool. See the list of dimensions here

Measure are: Gross Dels, Net Weight,  and No of Deliveries

Extraction of previous month M-1 scheduled for the 3rd day of the Month M

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 

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

5.2 Data Input

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

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

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• 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:

see 3.0

 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.

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. 

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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.NA, the extraction is an excel file

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.NA

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

Only members of this Google Group are allowed to access the extraction in GCP ECo-$Carbon emissions extraction access group

It is composed of at least 1 key user by GBU

The rationale is that for the time being, the extraction is automated, but the monthly upload is manual. The business key leader is ready to do this manual processing every month but we anticipate the fact that she may be out of the office and need a back up

Long term, the goal is to automatize this part as well and plug directly the extraction and TK Blue

7.4 Performance

NA

7.5 Reliability

NA 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).

7.9 Refresh of the Data

The extraction is scheduled to happen on the 3rd of the month, with data of the previous month M-1

A new file is created each month and added to the folderFrequency, data, and time of the data refresh in the data product.