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In today’s competitive business landscape, data automation has become necessary for business sustainability. Despite the necessity, it also comes with a few challenges in order to order to get meaningful insights. Some of these challenges include: collecting, cleaning, and putting it together--to get meaningful insights.
Data automation is simply the handling of business data with automated tools. When businesses use automated tools to upload, handle, and process data, it is called data automation instead of manual processes.
The automated process involves three elements-- Extract, Transform, and Load (also called ETL). There are three steps involved in the process:
What you get out of data automation are efficiency and less time consumption, and lesser money outgo. Moreover, it minimizes errors through validation and loads data in a structured format. Some examples of data automation are customer support, desk support, purchases, employee management, and meeting scheduling.
It is a good idea to get a broader overview of the influence of data automation in your business. You can regard it as a productive and cost-effective solution for your business. Due to the automation of some processes, companies can save costs and increase productivity and efficiency.
It can help employees focus on their core jobs rather than engage themselves in repetitive tasks, thus increasing productivity. Furthermore, data automation helps maintain work quality and minimizes errors that usually arise out of manual processes. It also enables businesses to integrate data from multiple sources to a single location.
Data processing is a complex affair. Data format varies according to the sources, so data needs to be standardized and validated before you load into a unified system. Automation minimizes manual intervention to reduce resource utilization, saves time, and increases data reliability.
With automation, you can have better performance and scalability of data. By setting into motion the change data capture (CDC) feature, you can transmit all the changes you make at the source level throughout the enterprise. And that is a significant benefit compared to manual data updates that consume time and are prone to errors.
With automation tools, loading and managing CDC is a matter of dragging-and-dropping objects on the visual designer without writing any code.
Typically, you need to prioritize automating sales, customers, and inventory data. But, you can include any other data if need be. Automation will help you reduce your dependence on resources and maintain data integrity and quality.
To begin with, you can start assessing whether the data needs frequent updates, the volume is high, and it comes from various sources. And, if the answer is in the affirmative, it will help if you automated the data.
Data automation calls for the right strategy, without which there can be wastage of time and resources. It can also lead to money wastage.
The following are the practical steps to put your data automation strategy into action:
Firstly, categorize your source data in terms of priority and ease of access by referring to your source inventory and identifying the sources you can access quickly. Make sure that the data automation tools you use support the data formats that you are using.
After you classify the data, identify the transformations essential for converting the source data into the necessary size.
Select the Right ETL Tool
As per the requirements, you gather from the previous steps, select an appropriate ETL tool that bears the essential features for processing and updating data, maintaining quality.
Schedule Data Updates
Make a schedule for updating data timely. And choose an ETL tool with automation features like job scheduling and workflow automation for actuating data automation without any manual intervention.
Data automation has been gaining popularity across diverse industries as it brings efficiency and productivity. The popularity has also led to the
incorporation of data science techniques to bring out a new concept called
automated feature engineering, which extracts features from raw data through data mining techniques. Despite being a new method, automated feature engineering bears the potential of solving numerous complex problems using real-world data sets.
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