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. Data Automation 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 Data Automation Strategy, different types of Data Access and Ownership deployments, and the Advantages of Data Automation.
Table of Contents
- Introduction to Data Automation
- Elements of Data Automation
- Understanding Data Automation Strategy
- Understanding Data Access and Ownership
- Steps for Data Automation
- Advantages of Data Automation
Introduction to Data Automation
Data Automation is defined as the process of uploading, handling, and processing data using automated technologies rather than conducting these processes manually. The long-term viability of your data pipeline mechanism depends on automating the data ingestion procedure. Any data that is manually updated runs the danger of being delayed because it is an additional task that an individual must complete, along with their other responsibilities. In the data ecosystem, Data Automation replaces manual labor with computers and procedures that do the work for you.
Without the need for human intervention, this process collects, transforms, stores, and analyses data using intelligent processes, infrastructure, artificial intelligence, and software. Data sourcing can be automated to save time and money while also increasing corporate efficiency. Data Automation also aids in reducing errors by ensuring that data is loaded in a structured fashion. For your firm to advance in the proper path, you’ll need to collect key business insights from your data. As a result, having an automated data analytics process allows business users to focus on data analysis rather than data preparation.
Elements of Data Automation
Extract, Transform, and Load, or ETL, are the three main components of Data Automation and are described below:
It is the procedure for extracting data from a single or several source systems.
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.
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. A typical ETL procedure is depicted in the diagram below:
A detailed Guide on ETL can be found here.
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. Without a solid Data Automation strategy in place, your firm will stray from the route it should be on, wasting time and resources. It could also cost you extra money in terms of lost revenue. As a result, your data process automation plan should be in line with your business goals.
Procedure to Develop a Data Automation Strategy
Here are some steps that can be undertaken to develop your Data Automation Strategy:
- Identification of Problems
- Classification of Data
- Prioritization of Operations
- Outlining Required Transformations
- Execution of the Operations
- Schedule Data for Updates
1. Identification of Problems
Determine which of your company’s core areas could benefit from automation. Simply consider where Data Automation 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 in Data Automation 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. Make careful to factor in the time it will take to automate a procedure. Quick wins are the way to go because they keep everyone’s spirits up while demonstrating the value of automation to the 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 during Data Automation 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 on a regular basis. 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.
Understanding Data Access and Ownership
Different groups will own different elements of the ETL process, depending on your team arrangement:
- Centralized Data Access and Operation
- Hybrid Data Access and Operation
- Decentralized Data Access and Operation
1. Centralized Data Access and Operation
The entire ETL process, as well as any Data Automation, is owned by the central IT department.
2. Hybrid Data Access and Operation
The extract and transform procedures are typically owned by separate agencies/departments, while the loading process is often owned by the central IT organization.
3. Decentralized Data Access and Operation
Each agency/department will be in charge of its own ETL process.
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Steps for Data Automation
You can begin implementing your automation strategy once you have a better understanding of the environment of Data Automation 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
Choose a couple of high-value datasets for which gaining access to the source systems will be simple. (In other words, start with the easy stuff) 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. If it is 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 in accordance with 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.
Automated analytics solutions may be available on cloud platforms that host enterprises’ data warehouses. Google Analytics, for example, has a built-in Analytics Intelligence tool that employs machine learning to detect anomalies in time series data with a single click.
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. 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 on a regular basis. 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
Since data analytics is frequently cross-functional, several teams, including marketing, operations, and human resources, may need to be involved in the planning process. 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
A business can benefit greatly from Data Automation. These advantages have been explained in detail below:
- Reduction in Processing Time
- Ability to Scale and Performance Improvement
- Cost Efficiency
- Better Allocation of Time and Talent
- Improved Customer Experience
- Improved Data Quality
- Sales Strategy and Management
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
Data Automation 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.
With automated data integration tools, loading data and managing CDC simultaneously is just a matter of dragging and dropping objects on the visual designer, without writing any code. Analytical speed can be improved through automation. When an analysis requires little or no human input, a data scientist can do analytics more rapidly, and computers can efficiently execute jobs that are complex and time-consuming for humans. The key to efficiently evaluating huge data is automation.
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 solutions 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. 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 solutions can help you keep your data consistent and up to date at all times, providing you the highest chance of success.
In this article, you learned about Data Automation, its elements, the Data Automation Strategy, different types of Data Access and Ownership deployments. You also learned about the process to implement Data Automation for your company and the various advantages of setting up Data Automation.Visit our Website to Explore Hevo
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