Harnessing useful insights from data that flows in with high volume, variety, and velocity is a tedious task. Moreover, manual data processing steps add to the pain of organizations in handling such data. In this article, we will discuss what data automation is and how you should automate the data management process in your organization to reduce the problems associated with the management of data.

What is Data Automation?

Data automation is the process of collecting, preprocessing, and storing data using computer applications and automated tools instead of manual activities.

Data management steps can be broadly divided into three steps i.e. Extraction, Transformation, and Loading.

• In Extraction, we collect data from different sources and store them in a single place.
• In Transformation, we pre-process raw and messy data from all the sources and convert it into a standard format according to a specified schema.
• In Loading, we load the clean, standard, and formatted data to a storage device that data analysts and data scientists have access to. This data is then analyzed to gain insights and solve business problems.

In data automation, we need to automate all three tasks mentioned above. 

How to Develop a Data Automation Strategy?

For data management automation, we first need to develop a data automation strategy. Let us discuss how to develop a data automation strategy. 

1. Identify the problems: Before thinking about data automation, we first need to analyze the areas where automation is required. For example, there might be operations that fail the most in the entire data pipeline or there might be processes that take most of the developer’s time. In these cases, you can use data automation to increase accuracy and efficiency.
2. Classify the data: With every action of a user, data is generated. However, not all data is important for analysis. Also, You might not have access to all the systems where the collected data is stored. Therefore, we first need to classify the data to point out the data that is required and accessible to us. This helps us choose the best data automation tools that support all the available data formats.
3. Determine the purpose of automating the data: Determine the reason for automating the data and how it will benefit your business. This will help you prioritize which data to automate first and how to structure the automation process.
4. Prioritize operations: Not every process in the entire data pipeline has the same priority when we discuss automation. For instance, automating a task that takes 10 hours of manual work will always be better than automating a task that requires only 10 minutes. The more intensive processes are automated, the better will be the results. Hence, the time and quality impact of automation should be considered to prioritize operations while data automation.
5. Outline required transformations: The raw data isn’t ready for use. In this step, we need to outline all the transformations that are required to convert the data into a usable format. For instance, you might want the raw XML or JSON data to be converted into a CSV file or to be stored in a relational database according to the available infrastructure. You need to specify each and every transformation step that is required to convert the raw data into the desired format. 
6. Determine execution of operations: After outlining the transformations, we need to implement the data pipeline, procedures for data analysis, and dashboards for better reporting.
7. Schedule data for updates: To make better and more informed decisions, we need to use the data flowing into our systems. For this, you need to create a schedule to update the data at a regular frequency. To implement this process without manual intervention, you can opt for data automation solutions with process automation features such as task scheduling and workflow automation.
8. Monitor and maintain: We need to regularly monitor the automation process to ensure that it is functioning properly and to identify any issues that need to be addressed. We also need to perform regular maintenance of the entire pipeline to ensure that it continues to operate efficiently.
9. Continuous improvement: As the business grows and changes, the data automation process may need to be modified to meet new requirements. We need to continuously review and improve the process to ensure that it remains relevant and effective.

Steps to Automate Data in Your Organization

After creating the automation strategy and understanding the data pipeline in your company, you can perform data automation using the following steps.

1. Identify the data: First, you need to identify the high-value datasets that create the most impact. For instance, if you run an e-commerce business, the customer behavior including product views and purchases by the customers might have the largest impact. Using this data, you can create automated processes to identify user behavior for retargeting the users and recommending new products.
2. Determine data access: Having identified the data, you need to determine how and in what format the data will be made available to the team. This step is critical to ensure data security and make sure that no data contracts are breached.
3. Evaluate the tools and technologies available: After obtaining the data, you need to evaluate the tools and technologies available for data automation that fits the infrastructure, budget, and expertise of your organization. You can use open-source tools like Python and PySpark for implementing the entire data pipeline. Alternatively, you can also use data automation solutions like Oracle Data Warehouse, Amazon Redshift, Google Analytics, and other cloud-based tools.
4. Define transformations and operations: After selecting tools, you need to define all the data transformations and operations outlined in the data automation strategy. 
5. Develop and test the automated procedures: At this step, you need to implement procedures, test the input and output of all the procedures and integrate them to create the entire data pipeline. Before integration, you should thoroughly check the execution of each procedure for every possible use case. This will help you avoid any possible failures and economic losses related to them.
6. Execute automation: After testing, you can deploy the entire data pipeline to automate the data management process.
7. Evaluate and improve: Once the data automation tool is up and running, you can evaluate its performance and verify if the system is fulfilling the objectives or not. You need to iteratively evaluate the KPIs and improve the data pipeline as required.

Suggested Reading: Free Data Visualization Tools for 2023

Why Should You Automate Data Fulfillment?

Data automation has many benefits. By handling the data correctly, a business can improve its internal processes as well as cater to its customers efficiently. Following are some of the benefits of automating data fulfillment.

• Improved efficiency: Automated data pipelines don’t require any kind of manual intervention. If implemented correctly, data automation leads to lower turnaround time, resource utilization, and errors. 
• Improved data quality: As there is no manual intervention, data automation helps you avoid any kind of human error. This results in increased data quality and reliability.
• Enhanced data analytics: The improved data quality will help data analysts and data scientists to obtain better insights from the available data. Enhanced data analytics can empower the sales and marketing teams to make better decisions.
• Customer retention: With sales and marketing teams making informed decisions using insights from customer behavior data, you can implement policies to improve customer experience and retain customers for a more extended period. 
• Enhanced data security: If there is no human access to the data, the data security increases automatically. This helps you comply with data regulations such as GDPR and CCPA.
• Enhanced data governance: Automating data fulfillment can help ensure that data is collected, processed, and disseminated in accordance with relevant laws, regulations, and policies, improving data governance and compliance.  

Examples of Data Automation in Industries

Each and every industry where data is generated in digital format can automate the data processes for better results. Following are some examples of how data automation is used in industries.

1. Healthcare: In the healthcare industry, data automation can be used to automate the process of collecting and managing patient data, such as medical histories, diagnoses, and treatment plans. This can help improve the efficiency and accuracy of patient care and reduce the risk of errors.
2. Manufacturing: In the manufacturing industry, data automation can be used to automate the collection and analysis of data from manufacturing processes, such as production rates, machine utilization, and quality control. This can help improve efficiency, reduce costs, and identify opportunities for process improvement.
3. Finance: In the finance industry, data automation can be used to automate the collection and analysis of financial data, such as transactions, budgets, and performance metrics. This can help improve the efficiency and accuracy of financial reporting and decision-making.
4. Retail: In the retail industry, data automation can be used to automate the collection and analysis of customer data, such as purchase history, preferences, and demographics. This can help improve customer targeting, personalization, and loyalty programs.
5. Government: In the government sector, data automation can be used to automate the collection and analysis of data related to public services, such as social welfare programs, tax collection, and voter registration. This can help improve the efficiency and effectiveness of government operations.
6. Education: In the education sector, data automation can be used to automate the collection and analysis of student data, such as grades, attendance, and enrollment. This can help improve the efficiency and accuracy of student record-keeping and support student learning and development.

Conclusion

In this article, we have discussed how a company can implement data automation to enhance its internal and external processes. Collecting and analyzing data from different sources can be overwhelming manually. Thus data automation can help an organization achieve improved data security and data quality over time. Even if the data size scales, it can easily be managed by automated tools. Hence, if you are looking for better returns on the investment made in the data infrastructure, automation of data management can be a great step to start with.

To learn more about data handling, you can read this article on data modeling tools. You might also like this article on Csharp and Sequel programming languages.

Disclosure of Material Connection: Some of the links in the post above are “affiliate links.” This means if you click on the link and purchase the item, I will receive an affiliate commission. Regardless, I only recommend products or services I use personally and believe will add value to my readers.