Understanding Data Automation: 5 Critical Aspects

Businesses typically generate and store colossal amounts of data from which they derive meaningful insights for faster and better decision-making using Business Intelligence (BI). Because of the variety and complexity of this data, efficient and cost-effective data analytics is required.
Automating data is an important process that can be implemented/incorporated to achieve this objective. 

In this article, you will be introduced to Data Automation and its elements. You will learn about the strategy, different types of data access and Ownership deployments, and the its advantages. 

What is Data Automation?

Data automation entails leveraging technology and software to integrate and automate tasks and processes related to an organization’s data with minimal manual effort. It uses algorithms, scripts, and tools to automatically collect, process, transform, and analyze (ETL) data without requiring manual human intervention. It enables businesses to automate repetitive and time-consuming tasks, such as data entry, validation, cleansing, integration, and analysis, thereby increasing efficiency, accuracy, and productivity.

 It helps to automatically perform tasks related to data, such as: collecting, transforming, storing, and analyzing.

It can help reduce manual work and improve data quality and accuracy. It can also: 

• Enable real-time insights.
• Speed up data-driven decision-making.
• Generate visual representations of data, such as charts and graphs 

Elements of Data Automation

Extract, Transform, and Load, or ETL, are the three main components of automating data are described below: 

1. Extract

• It is the procedure for extracting data from a single or several source systems.

2. Transform

• It is the process of modifying your data into the required structure, such as a CSV flat-file format. This might incorporate things like replacing all state abbreviations with the full state name.

3. Load

• It is the process of transferring data from one system to another, in this case, the open data portal.
  Each of these steps is necessary for fully automating and properly completing your data uploads.

Understanding Data Automation Strategy

It’s critical to have a general Data Automation plan for your company. Having a strategy in place ahead of time can assist you in engaging the right people at the appropriate moment within your company.

Procedure to Develop a Data Automation Strategy 

Here are some steps that can be undertaken to develop your strategy for automating data: 

• Identification of Problems
• Classification of Data
• Prioritization of Operations
• Outlining Required Transformations
• Execution of the Operations
• Schedule Data for Updates
• Track performance

1. Identification of Problems

• Determine which of your company’s core areas could benefit from automation. Simply consider where automating your data might be useful.
• Consider this: how much of your data operatives’ time is spent doing manual work? Which components of your data operations are consistently failing? Make a list of all the processes that could be improved.

2. Classification of Data

• The initial stage is to sort source data into categories based on its importance and accessibility.
• Look through your source system inventory to see which sources you have access to. If you’re going to use an automated data extraction tool, ensure it supports the formats that are important to your business.

3. Prioritization of Operations

• Use the amount of time consumed to estimate the importance of a process.
• The greater the amount of time spent on manual labor, the bigger the impact of automation on the bottom line.
• Factor in the time it will take to automate a procedure as well. Quick wins are the way to go because they keep everyone’s spirits up while demonstrating the value of automation to business owners.

4. Outlining Required Transformations

• The following stage entails determining whatever transformations are required to convert the source data to the target size.
• It could be as simple as turning tough acronyms into full-text names or as complicated as converting relational database data to a CSV file.
• Identifying the necessary transformations to achieve the intended results is critical; otherwise, your entire dataset might get corrupted.

5. Execution of the Operations

• The execution of data strategies is technically the most difficult component.
• We’ll look at how to implement three separate processes: better reporting, better engineering pipelines, and better machine-learning procedures.

6. Schedule Data for Updates

• The next step is to schedule your data so that it gets updated regularly.
• It is advised that you use an ETL product with process automation features such as task scheduling, workflow automation, and so on for this stage.
• This ensures that the process is carried out without the need for manual intervention.

7. Track Performance

• Monitor key performance indicators (KPIs) such as data accuracy, processing speed, and time saved.
• Set up automated alerts to quickly identify any performance issues or bottlenecks.
• Use dashboards to visualize performance and trends over time.

What are the Steps for Data Automation?

You can begin implementing your automation strategy once you have a better understanding of the data processing automation environment within your firm. To get started, follow these steps:

• Step 1: Identification of Data
• Step 2: Determination of Data Access
• Step 3: Selection of Tools and Platforms
• Step 4: Defining Transformations and Operations
• Step 5: Developing and Testing ETL Process
• Step 6: Scheduling the Automated Work
• Step 7: Delineate the Objectives and Test the Procedure

Step 1: Identification of Data

• Determine which source systems you already have access to by looking at your source system inventory.

Step 2: Determination of Data Access

• Determine how the data will be obtained by either the central IT organization or the department/agency. Is it going to be an SQL query, a CSV download, or something else?
• This stage will require the participation of the Data Custodian [They are responsible for maintaining data on the IT infrastructure by following the business requirements], as they are the best resource for gaining access to a dataset’s source system.

Step 3: Selection of Tools and Platforms

• Choose dependable, well-supported automation tools like Python’s NumPy, Pandas, and SciPy packages.
• The goal of these programming languages’ development is to make studies easily shareable among academics and analytics practitioners (as exemplified by the Jupyter project).
• This approach promotes collaboration by making it easy to move code and processes between humans. These packages, when used in conjunction with other tools, can automate a wide range of data analytics tasks.

Step 4: Defining Transformations and Operations

• Outline any necessary transformations for the dataset. It could be something as easy as converting long acronyms to full-text names, or as sophisticated as converting a relational database to a flat CSV file.
• Work with the Data Steward and Data Custodian to determine which fields should be extracted and how they should be structured for publishing.

Step 5: Developing and Testing ETL Process

• Select an ETL publishing tool and publish the dataset to the Open Data Portal based on the requirements stated in stages 2 and 3.
• Verify that the dataset was successfully loaded or modified without any issues through your procedure. Iterate, test, and develop.
• After you’ve prototyped an automated procedure, thoroughly test it. Automation should lower the amount of time spent on repetitive tasks.

Note: A failed or propagating error-prone automated analytics system can wind up costing more time and resources than a manual solution.

Step 6: Scheduling the Automated Work

• Schedule your dataset to be updated regularly.
• You can refer to the metadata fields you collected as part of your data inventory or dataset submission packet concerning data collection, refresh frequency, and update frequency.

Step 7: Delineate the Objectives and Test the Procedure

• Set clear goals and expectations for the automation process ahead of time to help teams collaborate and understand each other as the process progresses.
• Implement the automated procedure and keep track of its progress.
• Most automated data analytics systems include recording and reporting features, allowing them to operate with little supervision until failures or adjustments are required.

Advantages of Data Automation

1. Reduction in Processing Time

• Processing vast data volumes coming in from disparate sources is not an easy task. Data extracted from different sources vary in format.
• It has to be standardized and validated before being loaded into a unified system.
• Automation saves a lot of time in handling tasks that form a part of the data pipeline.
• Additionally, it minimizes manual intervention, which means low resource utilization, time savings, and increased data reliability.

2. Ability to Scale and Performance Improvement

• It ensures better performance and scalability of your data environment. For instance, by enabling Change Data Capture (CDC), all the changes made at the source level are propagated throughout the enterprise system based on triggers.
• On the contrary, manually updating data tasks consumes time and requires significant expertise.

3. Cost Efficiency 

• Automated data analytics saves time and money for businesses. When it comes to data analysis, employee time is more expensive than computing resources, and machines can execute analytics quickly.

4. Better Allocation of Time and Talent

• Data scientists can focus on generating fresh insights to support data-driven decision-making by automating tasks that don’t require a lot of human originality or imagination.
• Many members of a data team profit from data analytics automation. It allows data scientists to work with complete, high-quality, and up-to-date data.
• It also frees analysts and engineers from fundamental reporting and business intelligence activities, allowing them to focus on more productive tasks like adding additional data sources and broadening the scope of analysis.

5. Improved Customer Experience

• Offering an excellent product or service isn’t enough.
• Customers anticipate a positive experience with you as well.
• From your accounting team to customer care, Data Automation software ensure that your staff has the relevant data at their fingertips to satisfy the demands of your clients.

6. Improved Data Quality 

• Manually processing vast amounts of data exposes you to the risk of human mistakes, and depending on obsolete, poorly integrated technology to keep track of data exposes you to the same danger.
• Automated Data processing is best suited to technology that is error-free and never gets tired.

7. Sales Strategy and Management

• To identify the proper prospects and reach them through tailored campaigns, your sales and marketing teams rely on accurate data.
• Data Automation tools can help you keep your data consistent and up to date at all times, providing you with the highest chance of success.

Some examples of data automation include:

• Fraud detection
• Extract, Transform, and Load (ETL)
• Quality assurance checks
• System backups

Factors to Consider While Choosing Data Automation Tools

• Ease of Use: Opt for a tool with an intuitive, no-code interface that simplifies the automation process for all users.
• Scalability: Ensure the tool can handle large and complex datasets while adapting to growing business needs.
• Security: Look for features like encryption, access control, authentication, and auditing to protect sensitive data.
• Integration: Choose a tool that integrates well with data warehouses, analytics platforms, and various data formats.

List of data automation tools:

• Hevo: A no-code data pipeline tool that automates the ingestion, integration, and transformation of data, providing real-time analytics with minimal effort.
• Zapier: A no-code tool that automates workflows by connecting over 7,000 apps, enabling seamless data movement between platforms.
• Talend: A data integration platform that automates the process of extracting and transforming data from various sources into a unified format.
• Alteryx: A platform designed for automating data engineering, analytics, reporting, and machine learning tasks.
• Panoply: A data management solution that simplifies syncing, storing, and analyzing data from multiple sources.

Conclusion 

In this article, you learned about Data Automation, its elements, the strategy behind its implementation, different types of Data Access, and Ownership deployments. You also learned about the process of implementing automation for your company and the various advantages that come with setting it up.

Integrating and analyzing data from a huge set of diverse sources can be challenging; this is where Hevo comes into the picture. Hevo Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully automated and secure manner without having to write the code repeatedly. Hevo, with its strong integration with 150+ sources, allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready in a jiffy.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share your experience learning about data automation in the comments section below!

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