Data automation is the use of software, infrastructure and artificial intelligence to automate time-consuming processes. It can reduce costs and improve productivity while delivering better business results.

Any large dataset that requires frequent updates, transformations or manipulation before uploading is a good candidate for data automation. It can also be used to streamline data collection and analytics for increased efficiency and accuracy.

Identifying the Problem

Data Automation is an essential strategy for ensuring that your business processes run smoothly. It eliminates manual handling of large volumes of information and reduces the amount of human error that can occur. However, implementing this solution is not without its challenges. Some key obstacles include identifying the right data sets for automation, setting clear criteria for automating that data, and testing your system to ensure it works correctly.

To start the process, identify where your business could benefit from a more efficient data management approach. Ask yourself questions like: How much time do your employees spend on manually processing data? Which processes involve a significant number of repetitive tasks? Which are prone to errors? The answers to these questions will help you identify the ideal areas for data automation.

Once you have identified the potential problem areas, create a plan for automation. Decide what data sets you want to automate and map out the steps involved in each of these. This will make it easier to test and deploy your new automated sequences. Ideally, you should test and optimize your workflows regularly to avoid a disruption in your business processes.

The next step is to define the transformations required for converting your source data into the desired volume and format. This can be as simple as transforming hard acronyms into full-text names or as complex as converting relational DB data into a CSV file. Specifying the necessary transformations is crucial to attaining the intended results during the data automation process; otherwise, your entire set of data may be polluted.

Finally, determine how your agency/department will access the automated data. This can be done by specifying whether you need to query your central IT department or if you want each individual department/agency to access the data directly from the source systems.

Classifying the Data

Data automation is the process of streamlining and automating data-related processes to eliminate manual interference, saving time and resources, enhancing productivity, and improving accuracy, consistency, and quality of data within businesses. It involves identifying and implementing the best tools, technologies, and methodologies to accomplish these goals. Some of the most important steps in this process include classifying the data, transforming it, and loading it.

To identify the right software to use for data automation, it’s important to first understand your business needs and goals. Ultimately, you want to select a program that will help your team complete critical tasks consistently. These programs can also reduce the amount of time a team spends on tedious and repetitive tasks, freeing them up to work on more complex projects.

When selecting a tool, it’s important to consider its features and scalability. It should be able to connect with various data sources and formats, and support a variety of data workflows. It should also allow users to customize and automate processes, and provide easy-to-use interfaces to simplify the design process.

Once the software has been installed, it must be configured and customized to meet specific data needs. It should be capable of transforming raw data into a usable format, including adding or removing fields and tagging values. It should also be able to detect and correct errors in data. In addition, it should be able to recognize patterns and exceptions in data, and automatically adjust accordingly. It should also be able to notify the data team if the automated processes encounter any issues. This allows them to troubleshoot and fix the problem, ensuring that the data is accurate and up-to-date.

Extracting the Data

Data extraction is a key step in the ETL (extract, transform, load) or ELT (extract, load, transform) data integration process. It involves transferring data from disparate sources into centralized storage systems, which can be on-site or cloud-based. It may also include converting, cleaning or enriching the data to make it ready for processing and analytics. This can include removing whitespace or symbols, merging duplicate results and filling in missing values.

For example, a retail company might need to collect data from multiple sources, including customer transactions, website visits, social media mentions and in-store purchases. The information is then loaded into a data warehouse, where it can be analyzed to understand the company's reputation in the marketplace and help drive decisions.

The ETL process can be complex, but automation tools can reduce complexity and speed up the time required to run each step. A centralized repository for all the data makes it easier to find and analyze, which can save valuable resources and improve productivity across the business.

Businesses need to identify their data needs and implement a strategy for implementing data automation. This includes identifying the types of data that need to be automated, determining how often the data should be updated and who will be responsible for each step.

Then, they can select the right data processing tool to automate those tasks. This can reduce the need for manual processes, free up employee time to focus on more strategic work and allow them to generate insights faster. The resulting data can be used for reporting and analyzing trends and patterns, enabling companies to make better decisions that lead to higher profits and growth.

Transforming the Data

Once data has been gathered, it needs to be transformed into a format that can easily be stored and used for analysis. This step is referred to as the ETL process (extract, transform and load). The first thing that is done is extracting the data from all of your sources – which can include databases, IT systems, flat files, third-party data, web services, APIs, and more. Then the data is transformed, either in a simple way such as aggregation or by using other tools to change its format. Finally, it is loaded into the destination system which could be a database, a data warehouse, or even just a spreadsheet.

This can take a lot of time and effort if done manually. Automating this work can free up domain experts to focus on what they do best, increasing their productivity and boosting revenue-per-employee. It also reduces operational costs by reducing the amount of manual tasks that need to be performed.

Choosing the right automation tool is key for this process. Look for one that can handle the amount of data you are automating, has a robust workflow management system to manage the automation, and provides easy integration with BI and reporting tools. The tool should also support a variety of data formats, connections, and transformations.

Once your automation tool is ready to go, it’s important to test and review your results. This is a good opportunity to make sure that the automation tool is operating correctly, and that it is not collecting any information you don’t want it to. It’s also a good idea to reassess the tool on a regular basis to ensure that it continues to operate properly.

Loading the Data

Depending on the industry, businesses generate massive amounts of data that could range from system logs and financial reports to customer profiles and market trends. Manually handling such data can be time-consuming and prone to error. However, using a well-defined automation process can eliminate these errors and reduce the time needed for a company to make data-driven decisions.

With all the test data automation, a team can spend more time on projects that will drive business value. Sales teams can close more deals, marketing can focus on attracting new customers and the support team will have more time to improve the customer experience. This is why it’s important for companies to automate their ETL process to free up team members.

Once the data is processed and ready for analysis, it needs to be loaded into your data warehouse. This can be done via a scheduled REST API call or through a real-time Webhook or WebSocket. The latter is more complex but can be faster and more cost-effective because it avoids loading the entire data set every time a source updates.

A Reverse ETL tool like Fivetran can take care of all this work for you. This allows you to centralize your data and make it available for analytics, dashboards and reporting. It can also send your clean data to the tools your teams use so they can be used in their everyday workflow.

Building your own automation pipelines can provide more control, but it can be a full-time job in itself. An off-the-shelf solution like this offers a lower total cost of ownership and requires much less maintenance when an API changes or something breaks.