What is Data Automation

For ETL and ELT, numerous tools can help you automate the data integration process. One of the best on the market is Fivetran. Fivetran comes with prebuilt connectors that can extract data from a data source and load it to your desired destination.

You can also use tools to automate data transformation. One of the best would be dbt. dbt lets you build template SQL queries for data modeling and schedule them to run regularly within the data warehouse.

How to Automate Data Activation

Reverse ETL is the opposite process of ETL/ELT. It takes data from a data warehouse and moves it to a destination, like a marketing tool or another database.

The whole reason for getting data into a central point is to drive business value. Usually, data just sits on a dashboard and should be reviewed continually. But the reality is that dashboards get infrequently looked at.

Reverse ETL lets you send your data to tools to take advantage of it.

• Your Sales team could receive data such as customer product usage or how customers interact with your website to help them close deals.
• You could send customer data to ad platforms to create lookalike audiences.
• Or you could send data such as lifetime value, annual recurring revenue, or churn rate directly to the support team to help them prioritize tickets.

How to Automate Data Analytics

Automating data analysis uses processes and computer systems to automate analytical tasks. Data analytics automation helps create business intelligence dashboards or assists data scientists.

The data scientist garners insights into data gathered by a company using statistics, artificial intelligence, and machine learning. Specific tasks in data science are complex and happen regularly, which can be automated to save time and reduce human input errors.

You can partially automate data cleaning, which helps get it in the right format and remove any errors. To display the insights from the data, automation can help create components for any visualizations, such as graphs or charts. AutoML can help train and deploy machine learning models, helping save resources and speed up the research on machine learning. And automation can be used to continuously monitor and maintain AI models to ensure the models are still accurate over time.

Advantages of Data Automation

Automating your data in your business has many great benefits

Saves Time

Having to extract data, transform it, and then move it into a data warehouse takes time. And if the frequency is daily and requires manual work, the hours add up. Automating data removes the manual work freeing up the data team to work on more important tasks.

Cost Efficiency

There are two ways that data automation can save money.

1. You get back the data team resources reducing the need to hire extra people for those unstarted projects.

2. Build vs. buy. There’s a lot of debate about if you should build your own data automation or buy off-the-shelf solutions. Sure, building your own can give you more control as you can tailor your solution to exactly what you want, and it can prevent any vendor lock-in. But to build your own comes at the cost of the number of people required to carry out the work, the ongoing maintenance when there are API changes or things just break, and the time you have to wait for the automation to be delivered.

In fact, building this yourself can result in a full-time job! An easy option that produces far less effort is to buy. You could use Fivetran for your data pipelines, dbt to model your data, Snowflake to store your data, and Hightouch to activate your data.

These tools take the maintenance burden off the data team’s shoulders and save money in the long run.

Data in the Right Hands

With the chosen data automated, you can make it available to the right people for them to use. Typically this data could sit in a dashboard for the sales or marketing team. But a Reverse ETL tool like Hightouch can send the data from your data warehouse to the tools your teams use so they can have the data they want at their fingertips.

Better Data Quality

Once you’re automating data, fewer things can go wrong. The raw data you have cleaned up goes into your data warehouse when needed. If you do this process manually, you are more likely to add human mistakes that can taint your data quality.

Data Automation Use Cases

Sales

Getting data to your sales team can help them in many ways to close deals. You could provide them with product usage data so they can see which leads are getting value and are likely to convert to paying customers.

Lead score can be of value for your sales team to ensure they are making contact with customers most likely to convert. Establishing what events count towards lead score can let you take raw data, transform it into a lead score, and then move it to tools like Salesforce for the sales team to prioritize who they contact.

Marketing

The marketing team can benefit from data automation in their marketing campaigns. With a list of active users, the marketing team can use that data to create lookalike audiences to attract customers likely to be interested in your product or service.

Getting data from customers on the fence about your product, such as customers adding things to their cart but not buying, can help you create retargeting campaigns to get conversions.

Support teams

Access to the right data can help the support team provide a better customer experience. It can help ensure the support team prioritizes the tickets from high-value customers to keep them happy. With the churn score available, the support team can be proactive in their activities to reduce customer churn.

Personalization

With customer data more accessible, you can offer a better experience to your customers. It could be in emails tailored to show more attractive offers or presenting information in-app for a better customer experience.

How To Get Started With Data Automation

There are a few steps to get started with data automation.

1. Identify data

   The first step is to understand your data and where it’s stored. Then note the most valuable datasets to extract.

2. Determine access

   Find out who in your company can access the identified datasets. This could be getting database access or a simple CSV.

3. Define transformations and build models

   Once you know what data you want and how to access it, you need to decide on the data structure. Examples of this could be determining the format of how to store someone’s name or merging two datasets.

   As we mentioned before, dbt can help you build models that will make transforming data easy.

4. Develop and test ETL process

   Once you have your data transformed or modeled with dbt it’s always wise to test to ensure it’s working as intended. Testing involves confirming the data arrives in a relational database, data warehouse, or data lake, in the correct format. If the data isn’t arriving in the correct format, you could be making incorrect decisions or sending out the wrong message to customers.

5. Reverse ETL

   Once you know the data in the data warehouse is available and in the right format, it’s time to take action on it. Rather than data just sitting on a dashboard, you can use a Reverse ETL like Hightouch to sync data into whatever business tools you have so the right data is with the right people at the right time.

6. Schedule

   Once you know that data is coming in and out of your data warehouse correctly, you can set a schedule. Depending on the dataset and its use case, it could happen daily, or if it’s critical data, scheduled hourly.

Conclusion

It’s clear that deploying a data automation strategy can revolutionize your business processes, saving employee time, helping your sales team close more deals, your marketing team attracts new customers, and your support team provides a better customer experience.

Creating your own data pipelines can offer you more control, but you have to consider at what cost. With the introduction of the modern data stack, automating data is easier than ever, and the result is fewer things to worry about and more time spent on driving initiatives that drive business value.