Fundamentals

Understanding No-Code Rpa And Its Small Business Value Proposition

Robotic Process Automation Meaning ● Process Automation, within the small and medium-sized business (SMB) context, signifies the strategic use of technology to streamline and optimize repetitive, rule-based operational workflows. (RPA) has moved from a futuristic concept to a practical reality for businesses of all sizes. No-code RPA, in particular, democratizes this technology, making it accessible to small and medium businesses (SMBs) without requiring specialized coding skills. This guide is designed to empower you, the SMB owner or manager, to build your first data entry workflow, unlocking significant gains in efficiency and accuracy.

Imagine the hours your team spends manually copying data from one system to another ● from online forms to spreadsheets, from emails to databases. This repetitive work is not only time-consuming but also prone to errors and demotivating for employees. No-code RPA offers a solution by automating these tasks using software robots, often called ‘bots’. These bots mimic human actions, interacting with applications and systems in the same way a person would, but faster, more accurately, and around the clock.

The ‘no-code’ aspect is key for SMBs. It means you can build and deploy automation workflows using visual interfaces, drag-and-drop tools, and pre-built connectors, rather than writing complex code. This significantly reduces the barrier to entry, allowing you to quickly realize the benefits of automation without needing to hire expensive developers or invest in lengthy training programs.

For SMBs, the value proposition of no-code RPA for data entry is compelling:

• Increased Efficiency ● Automate repetitive tasks, freeing up employees for higher-value activities like customer service, strategic planning, and business development.
• Improved Accuracy ● Bots perform tasks consistently and without errors, reducing data entry mistakes and improving data quality.
• Reduced Costs ● Automation can lower operational costs by reducing manual labor, minimizing errors, and optimizing resource allocation.
• Enhanced Scalability ● RPA workflows can be easily scaled up or down to meet changing business needs, providing flexibility and agility.
• Better Employee Morale ● Automating mundane tasks can improve employee satisfaction by allowing them to focus on more engaging and meaningful work.

This guide will focus on a practical, step-by-step approach to building your first no-code RPA workflow for data entry. We will prioritize readily available tools and straightforward techniques, ensuring you can achieve tangible results quickly. Our unique selling proposition is the hyper-practicality and speed of implementation. We won’t just explain the concepts; we’ll guide you through the exact steps to build a working automation, focusing on a common SMB scenario ● automating data entry from online forms to a spreadsheet.

No-code RPA empowers SMBs to automate data entry tasks, freeing up valuable time and resources for strategic growth.

Selecting The Right No-Code Rpa Tool For Your Business Needs

Choosing the right no-code RPA tool is a critical first step. The market offers a range of options, each with its strengths and features. For SMBs focusing on initial data entry automation, key considerations include ease of use, affordability, pre-built connectors, and scalability. We recommend starting with tools that offer free or low-cost entry points and are known for their user-friendly interfaces.

For this guide, we will primarily focus on Microsoft Power Automate Desktop (PAD). PAD is a robust yet accessible no-code RPA tool that is included with Windows 10 and 11, making it readily available to many SMBs. It offers a visual drag-and-drop interface, a wide range of pre-built actions, and strong integration with other Microsoft products, which are common in SMB environments. While other excellent no-code RPA tools exist, such as UiPath StudioX and Automation Anywhere Robotic Interface (ARI), Power Automate Desktop provides a compelling combination of accessibility, functionality, and cost-effectiveness for SMBs starting their automation journey.

When evaluating no-code RPA tools, consider the following factors:

1. Ease of Use ● Look for tools with intuitive drag-and-drop interfaces, pre-built actions, and clear documentation. Free trials and community support can be invaluable for testing and learning.
2. Pre-Built Connectors ● Ensure the tool offers connectors to the applications and systems you use most frequently, such as spreadsheets (Excel, Google Sheets), databases, email clients, and web browsers. Pre-built connectors simplify integration and reduce development time.
3. Scalability and Flexibility ● While starting small, consider whether the tool can scale as your automation needs grow. Look for features like attended and unattended automation, cloud integration, and the ability to handle more complex workflows in the future.
4. Cost and Licensing ● Understand the pricing structure, including any free tiers, trial periods, and subscription costs. For SMBs, cost-effectiveness is often a primary concern. Power Automate Desktop’s free version is a significant advantage.
5. Support and Community ● Access to good documentation, tutorials, and community forums can be crucial for troubleshooting and learning best practices.

For the purpose of this guide, and to ensure maximum accessibility for SMBs, we will proceed with Power Automate Desktop. The principles and steps outlined are broadly applicable to other no-code RPA tools as well, but the specific instructions and interface references will be tailored to PAD.

To illustrate the importance of tool selection, consider this table comparing key features of popular no-code RPA tools for SMBs:

This table provides a high-level overview. Your specific needs and technical environment will dictate the best choice. However, for getting started quickly and effectively with data entry automation, Power Automate Desktop stands out as a strong option for SMBs.

Defining Your First Data Entry Automation Workflow ● A Practical Example

Before diving into the technical steps, it’s crucial to clearly define the data entry task you want to automate. Start with a simple, well-defined process that is currently performed manually and is repetitive. This approach allows for quick wins and builds confidence as you progress.

Let’s consider a common scenario for many SMBs ● automating the entry of customer order data from online forms into an Excel spreadsheet. Many SMBs use online forms (e.g., Typeform, Google Forms, Wufoo) on their websites to collect customer orders. Currently, staff members may manually download this data as CSV files and then copy-paste it into an Excel spreadsheet for order processing, inventory management, or reporting. This process is time-consuming and error-prone.

Our goal is to automate this entire workflow using no-code RPA. The workflow will consist of the following steps:

1. Access the Online Form Data ● The bot will access the online form platform (e.g., Typeform) and retrieve new form submissions. For simplicity, we will assume the data can be exported as a CSV file.
2. Download the CSV File ● The bot will automatically download the CSV file containing the new form submissions to a designated folder on your computer.
3. Open the Excel Spreadsheet ● The bot will open the target Excel spreadsheet where the order data needs to be entered.
4. Read Data from CSV ● The bot will read the data from the downloaded CSV file, extracting relevant fields like customer name, order items, quantity, and contact information.
5. Enter Data into Excel ● The bot will then enter this extracted data into the corresponding columns in the Excel spreadsheet, row by row, for each new order.
6. Save and Close Excel ● Once all data is entered, the bot will save the Excel spreadsheet and close the application.

This workflow automates the entire process from data extraction to data entry, eliminating manual intervention and significantly reducing the time and effort required. This example is highly relevant to e-commerce SMBs, restaurants with online ordering, service businesses using online booking forms, and many other types of SMBs that collect data through web forms.

To further clarify the workflow, consider this table outlining the data flow:

By visualizing the data flow and breaking down the process into clear steps, we create a solid foundation for building our no-code RPA workflow. The next sections will guide you through the practical implementation of each step using Power Automate Desktop.

Setting Up Power Automate Desktop And Preparing Your Environment

With our workflow defined, the next step is to set up Power Automate Desktop (PAD) and prepare your environment. If you are using Windows 10 or 11, PAD is likely already installed. You can search for “Power Automate Desktop” in your Windows search bar to check.

If it’s not installed, you can download it for free from the Microsoft website. The free version is sufficient for building the data entry workflow we’ve outlined.

Once installed, launch Power Automate Desktop. You will be greeted with the PAD designer interface, which is the central hub for creating and managing your automation flows. Familiarize yourself with the interface:

• Flows Pane ● This is where you will see a list of your created flows. Initially, it will be empty.
• Designer Pane ● This is the main canvas where you will visually build your automation workflow by dragging and dropping actions.
• Actions Pane ● Located on the left side, this pane contains a library of pre-built actions categorized by functionality (e.g., Excel, File, Web Automation, etc.). You will drag actions from this pane onto the Designer pane to build your flow.
• Variables Pane ● This pane displays the variables you create and use within your flow to store and manipulate data.

Before building our workflow, let’s prepare our environment:

1. Create a Dedicated Folder ● Create a folder on your computer where the downloaded CSV files from the online form will be saved. For example, you can create a folder named “OrderData” in your Documents directory. Note the full path to this folder as you will need it later in your workflow.
2. Prepare Your Excel Spreadsheet ● Create the Excel spreadsheet where you want to enter the order data. Define the column headers that correspond to the data fields you will be extracting from the online form (e.g., Customer Name, Order ID, Item, Quantity, Email, etc.). Save this spreadsheet to a known location on your computer.
3. Access to Online Form Data ● Ensure you have access to the online form platform (e.g., Typeform, Google Forms) and know how to export the form submissions as a CSV file. For automated access, you might need API keys or specific export settings depending on the platform. For this initial workflow, we will focus on manual CSV export to simplify the process. Later, in the ‘Intermediate’ section, we will explore more automated data retrieval methods.

Preparing your environment ensures that your RPA workflow has the necessary resources and access points to function correctly. A well-organized setup makes the development and testing process smoother and reduces potential errors. With Power Automate Desktop set up and your environment prepared, you are now ready to start building your first no-code RPA workflow for data entry.

Proper environment setup is crucial for a smooth RPA workflow development and execution, preventing common errors and delays.

Intermediate

Building Your First No-Code Rpa Workflow Step-By-Step In Power Automate Desktop

Now that we have laid the foundation, let’s get hands-on and build our first no-code RPA workflow in Power Automate Desktop (PAD). We will follow the workflow steps defined earlier ● automating data entry from a CSV file (representing exported online form data) into an Excel spreadsheet.

Step 1 ● Create a New Flow

1. Open Power Automate Desktop.
2. Click on the “+ New flow” button in the Flows pane.
3. Give your flow a descriptive name, such as “Automate Order Data Entry,” and click “Create.”

This will open the Designer pane with a blank canvas where you will build your workflow.

Step 2 ● Open Excel Application

1. In the Actions pane, search for “Excel.”
2. Drag the “Launch Excel” action onto the Designer pane.
3. In the “Launch Excel” action properties, select “Open the following document.”
4. Browse to and select the Excel spreadsheet you prepared earlier.
5. Click “Save.”

This action will open your target Excel spreadsheet when the flow runs. PAD automatically creates a variable named “ExcelInstance” to represent the opened Excel application, which we will use in subsequent steps.

Step 3 ● Read Data from CSV File

1. In the Actions pane, search for “CSV.”
2. Drag the “Read from CSV file” action onto the Designer pane, placing it below the “Launch Excel” action.
3. In the “Read from CSV file” action properties:
   • In the “CSV file path” field, browse to and select a sample CSV file that represents the exported data from your online form. This is for development and testing purposes. Later, you can modify this to handle dynamically downloaded files.
   • For “Delimiter,” select the appropriate delimiter for your CSV file (usually comma “,”).
   • Ensure “First line contains column names” is enabled if your CSV file has headers.
   • In the “Variable produced” field, you will see a default variable name, such as “CSVTable.” This variable will store the data read from the CSV file as a data table.
4. Click “Save.”

This action reads the data from your CSV file and stores it in a data table variable, “CSVTable,” which we will use to iterate through the rows and enter data into Excel.

Step 4 ● Loop Through CSV Data Rows

1. In the Actions pane, search for “Loop.”
2. Drag the “For each” loop action onto the Designer pane, placing it below the “Read from CSV file” action.
3. In the “For each” action properties:
   • In the “Value to iterate” field, select the variable “CSVTable” (the data table containing CSV data).
   • In the “Store each item in” field, enter a new variable name, such as “CurrentRow.” This variable will represent each row of the CSV data table as the loop iterates.
4. Click “Save.”

This loop will iterate through each row of the “CSVTable” data table, allowing us to process each row individually and enter the data into Excel.

Step 5 ● Write Data to Excel

1. Inside the “For each” loop block (between “For each” and “End loop” actions), search for “Excel” in the Actions pane.
2. Drag the “Write to Excel worksheet” action into the loop block.
3. In the “Write to Excel worksheet” action properties:
   • For “Excel instance,” select the “ExcelInstance” variable (created in the “Launch Excel” action).
   • For “Value to write,” you need to specify the data from the “CurrentRow” variable to write to Excel. Assuming your CSV has columns like “CustomerName,” “OrderItem,” etc., you can access these values using the syntax ● %CurrentRow['ColumnName']%. For example, to write the “CustomerName” column value, you would enter %CurrentRow['CustomerName']%.
   • For “Worksheet name,” enter the name of the worksheet in your Excel file (e.g., “Sheet1”).
   • For “Column,” specify the Excel column where you want to write the data (e.g., “A,” “B,” “C,” etc.).
   • For “Row,” you need to dynamically determine the next available row in your Excel sheet to avoid overwriting existing data. We will address this in the ‘Intermediate’ section enhancements below. For now, for simplicity, you can start by entering a fixed row number like “2” (assuming row 1 has headers). However, this will overwrite row 2 in each loop iteration, so this is only for initial testing.
4. Click “Save.”

Repeat Step 5 for each data column you want to write from the CSV to Excel, adjusting the “Value to write” (column name from CSV) and “Column” (Excel column letter) accordingly. Ensure you place each “Write to Excel worksheet” action inside the “For each” loop.

Step 6 ● Close Excel Application

1. After the “End loop” action, search for “Excel” in the Actions pane.
2. Drag the “Close Excel” action onto the Designer pane.
3. In the “Close Excel” action properties:
   • For “Excel instance,” select “ExcelInstance.”
   • For “Before closing Excel,” choose “Save document.”
4. Click “Save.”

This action saves the changes to your Excel spreadsheet and closes the Excel application after the data entry is complete.

Step 7 ● Run and Test Your Workflow

1. Click the “Run” button in the PAD designer toolbar.
2. Observe the flow execution. PAD will highlight each action as it is executed.
3. Check your Excel spreadsheet to verify that the data from the CSV file has been correctly entered.

For initial testing, use a small sample CSV file and your prepared Excel spreadsheet. Monitor the execution and troubleshoot any errors. Common errors at this stage might include incorrect file paths, column names, or Excel column letters. PAD provides error messages that can help you identify and fix issues.

Building your first RPA workflow involves breaking down the task into sequential steps and using drag-and-drop actions in a no-code platform like Power Automate Desktop.

Enhancing Your Workflow ● Dynamic Row Handling And Error Management

Our basic workflow is functional, but it has limitations. One key enhancement is to handle dynamic row insertion in Excel. Currently, we are writing to a fixed row, which will overwrite data or not correctly append new data. We need to dynamically find the next empty row in our Excel sheet.

Dynamic Row Handling ●

1. Get Last Row ● Before the “For each” loop, add an “Excel” action ● “Get first free row.”
   • For “Excel instance,” select “ExcelInstance.”
   • For “Variable produced,” create a new variable, such as “FirstFreeRow.” This variable will store the number of the first empty row.
2. Increment Row Counter ● Inside the “For each” loop, before the “Write to Excel worksheet” actions, add a “Variables” action ● “Increase variable.”
   • For “Variable to increase,” select “FirstFreeRow.”
   • For “Increase by,” enter “1.”
3. Use Dynamic Row in Write Action ● In each “Write to Excel worksheet” action within the loop, replace the fixed row number (e.g., “2”) with the variable “FirstFreeRow.” Now, the “Row” field should be %FirstFreeRow%.

With these changes, the workflow will now dynamically find the next empty row in your Excel sheet and write each row of data from the CSV file into a new row, appending the data correctly.

Error Management ●

To make your workflow more robust, it’s important to handle potential errors. For example, what if the CSV file is not found, or the Excel file is corrupted? PAD provides error handling capabilities using “Try-Catch” blocks.

1. Add Try-Catch Block ● Surround the core workflow actions (from “Launch Excel” to “Close Excel”) with a “Try-Catch” block.
   • In the Actions pane, search for “Error.”
   • Drag the “Try” action to the beginning of your workflow, before “Launch Excel.”
   • Drag the “Catch” action to the end of your workflow, after “Close Excel.” PAD will automatically create an “End try” action between “Try” and “Catch.” Place your existing workflow actions between “Try” and “End try.”
2. Handle Errors in Catch Block ● Inside the “Catch” block, you can define actions to handle errors. For example:
   • Add a “Popup message” action to display an error message to the user.
   • Add a “Log to file” action to log the error details for debugging.
   • Add a “Stop flow” action to gracefully stop the workflow execution if an error occurs.

By implementing error handling, your workflow becomes more resilient and user-friendly. It can gracefully handle unexpected situations and provide informative feedback, rather than simply crashing or producing incorrect results.

To summarize the intermediate enhancements, consider this table:

These enhancements significantly improve the practicality and reliability of your no-code RPA workflow. You now have a workflow that not only automates data entry but also handles dynamic data insertion and potential errors, making it more suitable for real-world SMB operations.

Intermediate RPA workflow development focuses on making the automation more dynamic, robust, and capable of handling real-world scenarios, including dynamic data and potential errors.

Integrating With Online Forms For Automated Data Retrieval

Our current workflow relies on manually exporting CSV data from the online form platform. To achieve true end-to-end automation, we need to integrate directly with the online form platform to automatically retrieve new submissions. The method for integration depends on the specific online form platform you are using. Many platforms offer APIs (Application Programming Interfaces) or webhooks that can be used for automated data access.

For this guide, let’s consider integration with Typeform, a popular online form builder. Typeform provides an API that allows you to programmatically access form responses. Integrating with an API typically involves making HTTP requests to the API endpoints to retrieve data. While this might sound technical, Power Automate Desktop simplifies API integration with its “Web service” actions, allowing you to interact with APIs without writing code.

Steps for Typeform API Integration (Conceptual) ●

1. Get Typeform API Key ● You need to obtain an API key from your Typeform account settings. This key is used to authenticate your requests to the Typeform API.
2. Identify API Endpoint ● Determine the specific Typeform API endpoint for retrieving form responses. Typeform’s API documentation provides details on available endpoints and request parameters. For example, you might use an endpoint like https://api.typeform.com/forms/{form_id}/responses.
3. Use “Web Service” Action in PAD ● In Power Automate Desktop, replace the “Read from CSV file” action with “Web service” actions.
   • “HTTP Request” Action ● Use this action to make a GET request to the Typeform API endpoint.
     • Set “Method” to “GET.”
     • Set “URL” to the Typeform API endpoint URL (including your form ID).
     • In “Headers,” add an “Authorization” header with the value Bearer YOUR_API_KEY (replace YOUR_API_KEY with your actual Typeform API key).
     • The API response (form responses in JSON format) will be stored in a variable, e.g., “ApiResponse.”
   • “Parse JSON” Action ● Use this action to parse the JSON response from the API into a usable data structure.
     • Set “JSON content” to the “ApiResponse” variable.
     • The parsed JSON data will be stored in a variable, e.g., “ParsedJson.” This variable will typically be a complex data structure (e.g., a list of dictionaries or objects) representing the form responses.
4. Modify Loop and Data Extraction ● Adjust the “For each” loop to iterate through the “ParsedJson” data structure instead of “CSVTable.” Modify the data extraction within the loop to access data from the “ParsedJson” variable based on the structure of the Typeform API response. For example, instead of %CurrentRow['CustomerName']%, you might need to use something like %CurrentItem['fields']['customer_name']['value']%, depending on the API response format.

Integrating with APIs requires understanding API documentation and data structures (like JSON). However, Power Automate Desktop’s “Web service” actions and “Parse JSON” action greatly simplify this process. You can test API requests and inspect responses directly within PAD to understand the data structure and adjust your workflow accordingly.

Alternative Integration Methods ● Webhooks

Some online form platforms also support webhooks. Webhooks are a way for an application to send real-time notifications to another application when a specific event occurs (e.g., a new form submission). Instead of your RPA bot periodically polling the API for new data, the online form platform “pushes” data to your bot when new submissions are received.

Implementing webhook integration typically involves:

1. Setting up a Webhook Listener ● You need a service or component that can receive and process webhook notifications. Power Automate offers “Cloud flows” (part of the Power Platform ecosystem) that can act as webhook listeners and trigger desktop flows.
2. Configuring Webhook in Form Platform ● In your online form platform (e.g., Typeform), you configure a webhook URL that points to your webhook listener. You also specify the event that triggers the webhook (e.g., “form submission”).
3. Processing Webhook Data ● When a new form is submitted, Typeform sends a webhook notification to your listener (Power Automate Cloud flow). The Cloud flow can then trigger your Power Automate Desktop flow, passing the form submission data as input.

Webhook integration is generally more efficient and real-time than API polling. However, it often involves a slightly more complex setup, potentially requiring the use of cloud services like Power Automate Cloud flows in conjunction with Power Automate Desktop.

The choice between API integration and webhook integration depends on your specific needs, the capabilities of your online form platform, and your technical infrastructure. For many SMBs starting with automation, API integration using Power Automate Desktop’s “Web service” actions provides a good balance of functionality and ease of implementation.

Direct integration with online form platforms via APIs or webhooks enables fully automated data retrieval, eliminating manual CSV export and creating a seamless workflow.

Advanced

Implementing Advanced Data Validation And Transformation Techniques

As your RPA workflows become more sophisticated, you’ll need to incorporate advanced data validation Meaning ● Data Validation, within the framework of SMB growth strategies, automation initiatives, and systems implementation, represents the critical process of ensuring data accuracy, consistency, and reliability as it enters and moves through an organization’s digital infrastructure. and transformation techniques to ensure data quality Meaning ● Data Quality, within the realm of SMB operations, fundamentally addresses the fitness of data for its intended uses in business decision-making, automation initiatives, and successful project implementations. and compatibility. Raw data from online forms or other sources may not always be in the ideal format for your target systems. Advanced RPA can handle these complexities, ensuring accurate and reliable data entry.

Data Validation ●

Data validation involves checking if the extracted data meets specific criteria and business rules before it is entered into the target system. This helps prevent errors and maintain data integrity. Common validation techniques include:

• Data Type Validation ● Ensuring that data is of the correct type (e.g., numeric, text, date). For example, validating that a phone number field contains only digits or that a date field is in the correct format.
• Range Validation ● Checking if numeric values fall within an acceptable range. For example, validating that an order quantity is a positive number or that a discount percentage is within 0-100%.
• Format Validation ● Verifying that data conforms to a specific format or pattern. For example, validating email addresses, postal codes, or product codes using regular expressions.
• Lookup Validation ● Comparing data against a predefined list or database to ensure validity. For example, validating that a selected country is in a list of supported countries or that a product code exists in an inventory database.
• Custom Rule Validation ● Implementing business-specific validation rules based on your unique requirements. For example, validating that the total order amount exceeds a certain threshold for free shipping.

In Power Automate Desktop, you can implement data validation using actions like:

• “If” Conditionals ● Use “If” actions to create conditional logic based on data validation checks. For example, check if a variable is empty, if a number is within a range, or if text matches a pattern.
• “Regular Expression” Actions ● Use actions like “Text” -> “Parse text” with regular expressions to validate data formats and extract specific patterns.
• “Database” Actions ● Use database actions to query databases for lookup validation, comparing extracted data against database records.

For example, to validate an email address format, you could use a “Regular expression” action with a regular expression pattern for email validation. Then, use an “If” condition to check if the email address matches the pattern. If it doesn’t, you can trigger error handling actions, such as logging the invalid data or sending a notification.

Data Transformation ●

Data transformation involves converting data from one format to another to make it compatible with the target system. This is often necessary because data sources and target systems may use different data formats, units, or structures. Common transformation techniques include:

• Data Type Conversion ● Converting data from one type to another (e.g., text to number, date to text). For example, converting a date string from a CSV file into a date format suitable for Excel.
• Data Formatting ● Changing the format of data (e.g., date formats, number formats, currency formats). For example, formatting a date value to display in a specific format in Excel or converting currency values to a consistent format.
• Data Cleansing ● Removing or correcting inconsistencies, errors, or unwanted characters from data. For example, removing leading/trailing spaces from text values, correcting misspelled words, or handling missing values.
• Data Mapping ● Transforming data from the source data structure to the target data structure. This often involves renaming fields, restructuring data, or combining data from multiple fields. For example, mapping columns from a CSV file to corresponding columns in an Excel spreadsheet, or restructuring JSON data from an API response to fit the Excel structure.
• Data Aggregation and Calculation ● Performing calculations or aggregations on data. For example, calculating order totals, summing quantities, or averaging values.

In Power Automate Desktop, you can implement data transformation using actions like:

• “Convert Data Type” Action ● Use this action to convert variables from one data type to another (e.g., text to number, number to text, text to datetime).
• “Text” Actions ● Use various text actions for data cleansing, formatting, and manipulation, such as “Trim text,” “Replace text,” “Substring text,” “Format number,” “Format datetime.”
• “Math” Actions ● Use math actions for calculations and aggregations.
• “Variables” Actions ● Use variable actions to manipulate and restructure data, such as creating new variables, assigning values, and combining variables.
• “JSON” Actions ● Use JSON actions (like “Parse JSON” and “Compose JSON”) for transforming JSON data structures.

For example, if you receive a date in “MM/DD/YYYY” format from an API and need to enter it into Excel in “YYYY-MM-DD” format, you can use the “Convert data type” action to convert the text to a datetime variable and then use the “Format datetime” action to format it into the desired “YYYY-MM-DD” string format before writing it to Excel.

By incorporating advanced data validation and transformation techniques, you can build RPA workflows that are not only automated but also ensure high data quality and seamless integration with your business systems. This is crucial for achieving reliable and impactful automation results.

Advanced RPA workflows incorporate robust data validation and transformation techniques to ensure data accuracy, consistency, and compatibility with target systems.

Implementing Conditional Logic And Branching For Complex Workflows

For more complex data entry scenarios, you’ll need to implement conditional logic and branching in your RPA workflows. Simple sequential workflows may not be sufficient to handle variations in data, different business rules, or exception handling. Conditional logic allows your bot to make decisions and follow different paths based on data conditions or workflow outcomes.

Conditional Logic with “If” Actions ●

The “If” action in Power Automate Desktop is the foundation of conditional logic. It allows you to define conditions based on variables, values, or expressions. Based on whether the condition is true or false, the workflow can branch to different sets of actions.

Common use cases for “If” conditions in data entry workflows include:

• Data Validation Checks ● As discussed earlier, use “If” conditions to check if data is valid before proceeding with data entry. If validation fails, branch to error handling actions.
• Handling Different Data Types ● If your data source can provide data in different formats or types, use “If” conditions to detect the data type and apply appropriate processing steps.
• Business Rule Implementation ● Implement business rules using “If” conditions. For example, if an order amount exceeds a certain threshold, apply a discount; otherwise, proceed with the standard price.
• Exception Handling ● Use “If” conditions to check for errors or exceptions during workflow execution. For example, check if a file exists before attempting to read it, or check if an API request was successful. If an exception occurs, branch to error recovery or notification actions.

You can create nested “If” conditions to handle more complex decision trees. For example, you might have an “If” condition to check the data type, and within each branch of that “If” condition, you might have further nested “If” conditions to handle specific validation rules for that data type.

Branching with “Go to Label” and Labels ●

For more complex branching scenarios, especially when you need to jump to different parts of the workflow based on multiple conditions, you can use “Labels” and “Go to label” actions.

1. Add Labels ● Place “Label” actions at different points in your workflow to mark specific sections or branches. Give each label a unique name (e.g., “ValidationSuccess,” “ValidationError,” “ProcessOrder,” “HandleException”).
2. Use “Go to Label” Action ● In your “If” conditions or other decision points, use the “Go to label” action to jump to a specific label based on the condition outcome. For example, if data validation is successful, use “Go to label” to jump to the “ProcessOrder” label; if validation fails, jump to the “ValidationError” label.

“Labels” and “Go to label” provide a way to create more structured and flexible branching logic in your workflows, especially when dealing with multiple possible paths or complex decision-making processes.

Example ● Conditional Data Entry Based on Order Type

Imagine you are automating order data entry, and your online form has a field for “Order Type” (e.g., “Standard,” “Express,” “Bulk”). You want to enter different data into Excel based on the order type. You could implement this using conditional logic:

1. Extract Order Type ● Extract the “Order Type” value from the data source (e.g., from CSV or API response). Store it in a variable, e.g., “OrderType.”
2. “If” Condition for Order Type ● Add an “If” action to check the value of “OrderType.”
   • Condition ● %OrderType% == 'Express'
3. “Then” Branch (Express Order) ● In the “Then” branch of the “If” condition, add actions to:
   • Write specific data related to express orders to Excel (e.g., expedited shipping details, priority processing flags).
   • Potentially jump to a specific label for express order processing steps.
4. “Else” Branch (Standard/Bulk Order) ● In the “Else” branch, add actions to:
   • Write standard order data to Excel.
   • Potentially jump to a different label for standard/bulk order processing steps.

By using conditional logic and branching, you can create RPA workflows that are adaptable to different data scenarios, business rules, and exception conditions, making your automation more intelligent and versatile.

Conditional logic and branching, implemented with “If” actions and “Go to label,” enable complex RPA workflows to handle variations in data, business rules, and exceptions effectively.

Leveraging Ai And Intelligent Automation For Enhanced Data Entry

The cutting edge of RPA is moving towards intelligent automation, incorporating Artificial Intelligence (AI) to handle more complex and unstructured data entry tasks. Traditional RPA excels at rule-based, structured data processing. AI-powered RPA extends these capabilities to handle unstructured data, make intelligent decisions, and learn from data, significantly enhancing data entry automation.

AI-Powered Data Extraction from Unstructured Sources ●

Many SMBs deal with unstructured data sources like emails, documents (PDFs, images), and handwritten forms. Traditional RPA struggles with these formats because the data is not in a structured, easily parsable format. AI technologies like Optical Character Recognition (OCR), Natural Language Processing Meaning ● Natural Language Processing (NLP), in the sphere of SMB growth, focuses on automating and streamlining communications to boost efficiency. (NLP), and Machine Learning (ML) can be integrated with RPA to extract data from unstructured sources.

• Optical Character Recognition (OCR) ● OCR technology converts images of text (e.g., scanned documents, images of forms) into machine-readable text. AI-powered OCR engines can handle variations in fonts, layouts, and image quality with high accuracy.
• Natural Language Processing (NLP) ● NLP enables bots to understand and process human language. This is crucial for extracting data from emails, chat logs, and text documents. NLP can identify key entities, relationships, and intents within text.
• Machine Learning (ML) ● ML algorithms can be trained to recognize patterns, classify data, and make predictions. In data entry automation, ML can be used for tasks like document classification, data categorization, and intelligent data validation.

Integrating AI with Power Automate Desktop ●

Power Automate Desktop offers integration with AI Builder, Microsoft’s AI platform, which provides pre-built AI models for various tasks, including:

• Form Processing ● AI Builder’s form processing models can automatically extract data from structured and semi-structured forms (e.g., invoices, receipts, purchase orders). You can train these models on your specific form layouts to achieve high extraction accuracy.
• Document Processing ● AI Builder’s document processing models can extract text, tables, and key-value pairs from unstructured documents like PDFs and scanned documents.
• Object Detection ● AI Builder’s object detection models can identify and locate objects in images. This can be used for tasks like image-based data entry or visual validation.
• Text Analytics ● AI Builder’s text analytics models provide NLP capabilities for sentiment analysis, key phrase extraction, language detection, and entity recognition from text data.

To leverage AI in your Power Automate Desktop workflows:

1. Access AI Builder Actions ● In the Actions pane of PAD, you will find “AI Builder” actions. These actions allow you to integrate with AI Builder models.
2. Choose Appropriate AI Model ● Select the AI Builder model that is suitable for your data source and task (e.g., “Process form” for form data extraction, “Extract text from document” for document processing).
3. Train or Configure AI Model ● For some models, like form processing, you may need to train the AI model by providing sample documents and labeling the data fields you want to extract. For pre-trained models (e.g., text analytics), configuration might involve selecting the desired features and parameters.
4. Integrate AI Actions in Workflow ● Drag the AI Builder action into your PAD workflow and configure it to process your data source (e.g., provide a path to a document or image, or pass text data).
5. Process AI Output ● The AI Builder action will output the extracted data or analysis results as variables. Use these variables in subsequent actions in your workflow to enter data into target systems, perform further processing, or make decisions based on AI insights.

Example ● Automating Invoice Data Entry from PDF Invoices

Many SMBs receive invoices in PDF format via email. Automating invoice data entry is a valuable application of AI-powered RPA. You can use AI Builder’s form processing model to extract data from PDF invoices and then enter it into your accounting system.

1. Receive Invoice Email ● Use email automation actions in PAD to monitor an email inbox for new invoices (e.g., emails with invoice attachments).
2. Save PDF Invoice ● Automatically save the PDF invoice attachment to a local folder.
3. Use AI Builder “Process Form” Action ● Add the “AI Builder” -> “Process form” action to your workflow.
   • Configure the action to use a pre-trained or custom-trained form processing model for invoices.
   • Provide the path to the saved PDF invoice file as input.
4. Extract Invoice Data ● The “Process form” action will extract data fields from the invoice (e.g., invoice number, date, vendor, line items, total amount) and output them as variables.
5. Enter Data into Accounting System ● Use application automation actions (e.g., UI automation, API integration with your accounting software) to enter the extracted invoice data into your accounting system.

By leveraging AI and intelligent automation, SMBs can automate data entry from a wider range of data sources, including unstructured formats, and handle more complex data processing tasks, significantly expanding the scope and impact of RPA.

AI-powered RPA extends automation capabilities to unstructured data sources and complex tasks, enabling intelligent data extraction, validation, and decision-making in data entry workflows.

References

• Chui, M., Manyika, J., & Miremadi, M. (2015). Four fundamentals of workplace automation. McKinsey Quarterly.
• van der Aalst, W. M. P. (2018). Process mining ● Data science in action. Springer.
• Aguirre, S., & Rodriguez, A. (2017). Automation of a business process using (RPA) ● A case study. International Journal of Computer Trends and Technology, 48(2), 96-101.
• Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation at Tesco. Outsourcing & offshoring of business services, 143-160.