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Begin by logging into your Insightly account. Navigate to the data or reports section where your desired data resides. Use Insightly’s built-in export feature to download the data. Typically, this will be available in CSV format. Follow the prompts to complete the export process and save the file to your computer.
Open the downloaded CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure it has exported correctly and make any necessary adjustments, such as removing unwanted columns, renaming headers, or correcting any data inconsistencies.
Open Google Sheets by navigating to sheets.google.com in your web browser. Sign in with your Google account if prompted. If you don't already have a specific sheet prepared for this data, create a new blank spreadsheet by clicking on "Blank" to start a new sheet.
In the new spreadsheet, click on "File" in the top menu, then select "Import." Choose "Upload" and drag your CSV file into the window or click "Select a file from your device" to locate and select your CSV file. Choose the import location (e.g., replace current sheet, insert new sheet) and import the data by clicking "Import data."
Once the data is imported, ensure that the information is organized correctly. Adjust column widths, apply filters for easy sorting, and use formatting tools to highlight or emphasize important data. This step will make the data easier to read and analyze.
Double-check that all data has been imported accurately from Insightly. Compare some sample records from the CSV to those in Google Sheets to confirm that the data matches without any errors or discrepancies. Address any issues by correcting them directly within Google Sheets.
While direct automation from Insightly without connectors isn’t possible, you can use Google Sheets features like Google Apps Script to create scripts for periodic data updates if necessary. This requires basic scripting knowledge but can be a powerful tool for automating repetitive tasks, such as data cleansing or formatting.
By following these steps, you can efficiently move your data from Insightly to Google Sheets manually without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Insightly is a cloud-based customer relationship management (CRM) software that helps businesses manage their sales, marketing, and customer service activities. It provides a centralized platform for managing customer interactions, tracking leads and opportunities, and automating workflows. Insightly also offers project management tools, allowing teams to collaborate on tasks and projects, and track progress in real-time. The software integrates with popular business applications such as Google Apps, Office 365, and Mailchimp, making it easy to streamline workflows and improve productivity. With Insightly, businesses can gain valuable insights into their customers and improve their overall customer experience.
Insightly's API provides access to a wide range of data related to customer relationship management (CRM) and project management. The following are the categories of data that can be accessed through Insightly's API:
1. Contacts: This includes information about individuals or organizations that are associated with a company, such as their name, email address, phone number, and job title.
2. Organizations: This includes information about companies or other types of organizations, such as their name, address, and industry.
3. Opportunities: This includes information about potential sales opportunities, such as the name of the opportunity, the expected revenue, and the stage of the sales process.
4. Projects: This includes information about ongoing projects, such as the project name, description, and status.
5. Tasks: This includes information about tasks that need to be completed as part of a project, such as the task name, due date, and status.
6. Events: This includes information about events that are scheduled, such as the event name, date, and location.
7. Notes: This includes information about notes that have been added to a contact, organization, opportunity, project, or task.
8. Emails: This includes information about emails that have been sent or received by a contact or organization.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
