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Using Data Automation for Salesforce

Create data pipelines with GRAX to streamline digital transformation and improve analytics and intelligence capabilities.

Source: Image created by OpenAI’s DALL-E, January 26, 2024

What is data automation?

Salesforce generates and houses vast amounts of diverse data, which can be a goldmine for insights. However, managing this data is often a Herculean task. If done manually, the amount of time and resources required to collect and prepare data for analyses can be enormous. The process itself is also prone to errors. 

Data automation addresses the challenges of volume, variety, and velocity of data while ensuring accuracy, efficiency, and scalability. It typically uses algorithms, scripts, and tools to automatically collect, process, transform, and analyze data without requiring manual human intervention. Sometimes this is called ETL (Extract, Transform, Load). 

In Salesforce this might include extracting data from sales records, customer records, and marketing campaigns. Transformation means cleaning up inconsistencies, eliminating duplicate records, and standardizing formats to get it ready for analysis. “Load” is putting prepared data into a place where it can be used effectively, like a database or a business intelligence platform. 

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5 benefits of data automation

  1. Timely insights

In today’s dynamic business world, companies need real-time or near-real-time data analysis to stay ahead of the game. Data automation enables you to rapidly process Salesforce data.

  1. Fast response to market changes

In addition to real-time data, businesses also need access to their historical data. That’s what enables you to uncover patterns and identify anomalies. Organizations that automate the processing of real-time and historical data can effectively adapt to market trends and customer behavior changes. 

  1. Reliable results

By eliminating manual processes, you help ensure the data used for analytics is accurate, and that consistent processes are used to handle data.

  1. Save time and resources

Free up employee time to focus on more strategic tasks.

  1. Easily scale

As a business grows, so does its data. Automation helps you more easily manage increasing amounts and types of data.

Best practices for data processing automation

To get the best results from your data automation initiatives, follow these guidelines:

  • Set clear objectives: Define what you want to achieve. This could include saving X amount of time or generating daily reports on deals closed. Start by understanding which data processes would benefit most from automation, how much data you need to process, where that data comes from, and how often it’s updated.  Encourage collaboration between data engineers, analysts, and business users to ensure you meet everyone’s needs.
  • Ensure quality data sources: Make sure your data is from reliable and clean sources. Remember, garbage in means garbage out.
  • Design for scalability and flexibility: Design your pipeline to handle an increase in data volume without degradation in performance. Ensure your pipeline can adapt to new data sources, schemas, and technologies.
  • Track and optimize performance: Monitor key indicators such as processing speed, hours saved, and errors. Regularly review and refine your workflows to optimize performance, identify areas for improvement, and adapt to evolving business needs. Be mindful of the trade-off between speed and resource consumption. Optimize based on your organization’s priorities.
  • Security and compliance: Regularly review your data automation processes and pipelines against industry standards and regulations. Instill security measures to minimize data vulnerability.
  • Documentation and training: Assign stakeholders to document processes and pipelines and keep the information up to date. Train users on how to interact with automated systems and interpret the data.

What is a data pipeline?

A data pipeline is a set of actions that automate the flow of data from its source to a destination, such as a database or a data lake. It might entail extracting financial data from Salesforce, transforming it into a suitable format, and loading it into an analytical tool. 

There are three types of pipeline automation: scheduled, triggered, and streaming.  

Scheduled automation

Scheduled automation involves running the data pipeline at predetermined times. This could be hourly, daily, weekly, or any other interval that suits your business needs.  

Triggered automation

Triggered automation starts the data pipeline in response to specific events or conditions. A trigger could be the arrival of new data or the completion of another process.  

Streaming automation

Streaming automation is for real-time data processing. Data is continuously ingested and processed as soon as it arrives, without waiting for batch collection.  

Streamlining data automation with GRAX

GRAX simplifies data automation. It lets you activate Salesforce data for AL/ML and analytics without extensive technical expertise.

Whether you want to load data into a central data warehouse such as Snowflake or feed an analytics platform such as Tableau, Microsoft Azure Synapse Analytics, AWS QuickSight, or PowerBI, GRAX does it with minimal configuration and setup.

What makes it so effective?

  • Historical Data Replication

GRAX continuously captures and replicates every version of Salesforce data changes into your own cloud instance. This means the data you use for analysis is always current and complete – and the decisions you make are always well-informed.  

  • Object Stores 

GRAX uses the highly scalable object stores built into major public cloud platforms – Amazon AWS, Microsoft Azure, and Google Cloud Platform – for data delivery. By using an object store like Amazon S3 rather than a queueing platform like Kafka or Kinesis, it keeps things simple for consumers.  

  • Parquet Format

GRAX automatically writes data into Parquet for its strong compatibility and compression. Parquet is well-supported by all major ETL, data storage, and data processing platforms. This makes it easy to use your favorite tools to extend the value of your Salesforce data.   

  • Schema Changes:  GRAX encodes schema with the object data it replicates.  Its automated handling and support of schema changes gives Salesforce users a crucial advantage. By enabling new/custom objects (and their entire history) to be readily accessible, GRAX effortlessly evolves with your business needs.  

In the fast-paced world of Salesforce, data automation is not just a convenience but a necessity. Follow best practices and use data automation tools like GRAX to get much more value from your data with much less effort.

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