What are the best ways to balance data complexity and automation simplicity?

->Understanding the process steps & scope/boundaries will play a vital role during the planning of automation. ->Clearly define what we are going to achieve by automating the task. ->Understand the process nature - Attended Automation or Unattended Automation ->Clear vision on formation of dataset ->Understand the purpose & benfits of automation - time savings, cost reduction, improved accuracy and scalability ->Understanding the success & failure rate which enforce us to get feedback on product ->Break down the process into smaller modules/steps and identify which parts are most suitable for automation ->Eradicate the unnecessary steps in the automation process which improve preformance & error of the automation process

Standardization of data formats which always make the process Light weighted & run easier. Understand about the schema of each data format (Excel, Csv, Json, Xml, or Pdf) which can be structured, Semi-structured or Un-structured.Detecting anomalies, identifying patterns, and assessing the overall quality of the data.Transforming data from one format to another (Common format). This can be useful when we need to aggregate or consolidate information from various sources Example: To extract data from Excel binary workbook It's hard to extract data from xlsb format but if we convert the data from xlsb to xlsx it would become much easier to fetch data which will be supported by all the automation platform

Before starting the automation one should identify the automation type whether Simple or Complex based upon that we can make our design/approach, Even basic data extraction approach will vary from simple to complex ->Simple Process - Extract data using Native and Normal Get Text mechanism ->Complex Process - Extract data using OCR Technique and Integration of AI & ML approach where it doesn't need any programming or coding skills Validation processes can be incorporated to ensure data accuracy and integrity once we have developed a solution. Also, automation can facilitate the integration of various applications and services and collaborate with different tools and systems

Data storage source/Data set decide the complexity of the automation where we have to understand the scheme, datatype & size. Databases - Basically helps to store confidential information/data (i.e - PII) Cloud Storage - They are more scalability, accessibility and cost-effectiveness. Also, don't have any limitation on storing data. Local Storage - Storing data locally will be suitable for smaller datasets where direct control over hardware is necessary. Where automation platform like UiPath, Power-Automate, AA360 have separate activities for all above storage operation. These all storage setups adds an extra layer of security, especially when dealing with sensitive information

Test & Monitoring always ensure its effectiveness, reliability, and stability over time. Several kind of testing will be perfomed against automation process, Unit Testing - Test individual components and functionailty by the developer who designed the process Cross Functionality Testing - Testing components and functionailty by the Teammate Integration Testing - Testing the entire automation process which include the Desktop & Web application and connections Regression Testing - Testing the entire automation process using different dataset to ensure the nature of automation Error Handling - Error handling mechanisms helps to test how the automation responds to unexpected scenarios

- Modularize your automation routines. Build parameterized reusable components to avoid monolithic automation processes that won’t easily generalize to other use cases. This will also simplify troubleshooting and maintenance. - Sometimes less is more; don’t over abstract your architecture. Challenge yourself to minimize the lines of written code required to organize your automation process into the fewest number of components, without violating the single-responsibility principle.