Integrating Data into Customer Analytics Tools with Datameer and Snowflake
- Jeffrey Agadumo
- August 7, 2023
Customer analytics tools are a requirement in the tool stack of customer-facing businesses. This necessity is due to the importance of understanding customers more and connecting with them on a deeper level, which inevitably translates to business success. However, businesses that solely depend on these analytics tools to manage and analyze their customer data often encounter the challenge of dealing with data silos.
This article emphasizes the advantages of adopting a centralized cloud-based analytics solution and outlines how it can enhance customer data quality for analysis.
Improved customer analytics is only a short read away!
Integrate Your Favorite Customer Analytics Tools
Snowflake is considered a go-to for cloud data storage. This notoriety, in part, is due to its versatility with data (from structured to unstructured) and seamless integration with a diverse array of data tools, enabling effortless data transfers in both directions.
Whether you collect customer data on Google Analytics, Salesforce, Shopify, or even on a database from an in-house Customer Relationship Management software, you can integrate it into Snowflake.
How? you might ask.
Well, there are a number of ways to integrate data from multiple sources, so let us take a look at a few
1. Manual Data Loading
This first data integration method involves exporting customer data from your analytics tools into a staging environment in Snowflake using the PUT command and manually uploading them into your corresponding Snowflake tables using the COPY command. However, the data can only be periodically loaded in batches and is not ideal for more frequent data collection.
2. Third-Party Integration Tools
Employing third-party integration tools is another great way to connect your favorite analytics tools to Snowflake and load your data into it. These ETL tools often have pre-built connectors for popular data sources and help simplify and automate integration. Some such tools include Fivetran, Hevo, Stitch, Celigo, and Qlik.
3. Real-Time Streaming
If you collect your data in large volumes or more frequently, you may consider real-time streaming options to send data to Snowflake in near real-time usingg CDC technologies like Apache Kafka, Amazon Kinesis, or Snowpipe.
4. Using Tool Native Connectors
Some customer analytics tools have direct connectors or plugins to connect to Snowflake. Check if your analytics tools have native connectors available to establish a direct connection with Snowflake and transfer data seamlessly.
5. Custom Data Integrations
If you have unique requirements or need to integrate with lesser-known analytics tools, you can build custom data connectors using Snowflake’s support for external functions or Snowflake’s JavaScript User-Defined Functions (UDFs) to fetch data from APIs and load it into Snowflake.
Data Preparation With Datameer
Once data has been uploaded to Snowflake, in comes Datameer to tidy up your customer data and make it analysis ready
Here’s what it has to offer
1. Search and Data Catalog
Datameer’s built-in search functionality and data catalog empower your team to discover the relevant datasets needed for your projects swiftly. The data catalog comes equipped with documentation, lineage, and custom metadata, making locating the right data for your analysis is a breeze.
2. Data Profiling
With Datameer, you can thoroughly explore and assess the shape and quality of any dataset within Snowflake. The data profiling capability enables you to identify and address potential data quality issues, such as missing values, outliers, and inconsistencies, ensuring that your analyses are based on accurate and reliable information.
3. Built-in and Custom Transformations
Datameer provides pre-built drag-and-drop transformations for easy data cleaning and standard operations or customize with SQL for specific data requirements. But no complex coding is needed for basic transformations.
4. AI-powered User Experience
Datameer uses cutting-edge AI technologies to enhance your data analysis. Benefit from AI-generated recommendations and transformations for faster data preparation. Convert natural language into SQL queries, making data exploration more accessible and efficient.
5. Open AI Integration for Documentation
Datameer’s Open AI integration automatically documents your projects, capturing all the steps taken during data cleaning and transformation. This documentation ensures your work is well-documented and reproducible, supporting effective data governance and collaboration.
Better Customer Analytics With Datameer and Snowflake
With Datameer and Snowflake, you truly unlock the power of your data, seamlessly integrate and analyze your customer data with ease. Empower your business with valuable insights to make data-driven decisions. Don’t miss out on the opportunity to supercharge your customer analytics tools. Get started today and take your business to new heights!
