Complete In-Depth Guide to Automated Data Processing

Introduction 

It is called automated data processing when a device processes data without constant human supervision. Manual data processing occurs when a person analyzes, organizes, stores, retrieves, or manipulates data. A retail outlet manager, for example, who uses Microsoft Excel to calculate weekly sales averages is engaging in manual data processing. However, an automated data processing example is when a software such as Business Intelligence (BI) system can compile and retrieve monthly sales performance data and present them in the form of charts on a dashboard. 

Why Data Process Automation Is Crucial In The Modern World 

Data process automation uses intelligent software, artificial intelligence, and infrastructure to extract, store, transform, and analyze data. Data acquisition can be automated for saving time and money and also to achieve operational efficiency. It also reduces errors as data is captured in a structured manner. 

The automated data analytics enables your employees to focus on data analysis rather than data preparation. Data Automation replaces manual labor in the data environment with computers and processes that perform the function. 

According to Statista, the total amount of data created, captured, copied, and consumed around the world is expected to skyrocket, reaching 64.2 zettabytes by 2020. Over the next five years, up to 2025, global data creation is expected to exceed 180 zettabytes. 

Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025(in zettabytes)

Benefits of Automated Data Processing System 

The data processing, handling, and hosting industry in the United States is expected to generate $197.8 billion in revenue by 2024. Automated data processing systems are becoming increasingly popular because they help organizations manage data more efficiently. 

Many industries are currently enjoying the benefits of data automation that include: 

Reduced processing time 

Processing massive amounts of data from multiple sources is not an easy task. Before incorporating data from multiple formats into a unified network, it must be formalized and evaluated. Automation saves a significant amount of time when dealing with tasks that are part of the data pipeline. Furthermore, it reduces manual intervention, implying less number or resources, time savings, and improved data reliability. 

Cost effectiveness 

For businesses, automation of data analysis saves time and money. Employees spend more time in data analysis when compared to automated processes. also devices can perform analytics quickly and in an accurate manner. 

Better Time Management 

By automating tasks that do not require a lot of human creativity, analysts can focus on producing new insights to support decision-making. Data analytics automation benefits several members of a data team. It enables data scientists to work with data that is of high quality, exhaustive, and up to date. 

Enhanced CX (Customer Experience) 

It is not just sufficient to provide an excellent product or service. Long-lastng customer experience is the order of the day. Data Automation ensures that your employees have the relevant data at their fingertips to meet the needs of your clients, from your accounting board to consumer care. 

Better Data Quality 

When data is manually processed, especially with massive amounts of data, it exposes you to high risk of human error. According to a recent study, data entry errors can reach 4%. That means the error rate for data entered once and not verified further is 400 per 10,000 entries, which is an alarming number that affects even small datasets. 

Also, if your technology is outdated, poor integration of data exposes you to the same risk. This is where data processing lends itself well to error-free technology. 

Sales Management and Strategy 

Your marketing and sales staff depend upon detailed data to identify good prospects and reach them through targeted campaigns. Data Automation can help you keep your data up to date and in a consistent manner, giving you the best chance of success. 

Understanding The Basics Of Automated Data Processing 

The three central components of Data Automation are ETL - Extract, Transform, and Load 

Extract: Here, data is extracted from one or more source systems. 

Transform: Data is transformed into the necessary structure, such as a CSV flat-file format. A simple example could be transforming all state abbreviations with their full names. 

Load:  Here, the open data portal transfers data from one operation to another. 

To fully automate and successfully complete your data automation, each step is essential. 

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Definition Of Automated Data Processing 

The combination of processes, procedures, methods, people, equipment, and tools used to perform data operations in an automated environment is referred to as automated data processing (ADP). In the IT world, it is also called "Automatic Data Processing,"  

5 Automated Data Processing Tools 

 
Depending on the data requirements for your business vertical, you can choose from among the 5 automated data processing tools: 

1. Batch Processing 

When your system processes homogeneous data points in batches, this is referred to as batch processing. There are three types: 

Batch processing is typically used for finance and accounting data, confidential health information or other highly confidential data. 

2. Real-time processing 

When your system processes data on a real-time basis,, it is almost instantaneous. Businesses can use real-time processing to deal with data that requires constant monitoring  analysis, such as inventory status data or location tracking data from field and sales staff. 

3. DDP or Distributed Data Processing 

DDP is the process of dividing a dataset into sections for processing with different devices. DDP is used by businesses that need to quickly analyze large datasets. This is also useful as part of your disaster management strategy where data processing disruption can be minimized by taking devices offline. 

4. Multi-processing 

This includes the use of multiple processors from the same system on the same dataset at the same time. Multiprocessing is commonly used to process very large datasets. 

5. Time-sharing 

When employees across your business interact with a single processor at the same time, this is referred to as time-sharing. Here, the processor assigns each user a "time slot," and each slot is processed sequentially on a first-come, first-served basis. Users first enter a query, then wait for a response. Time-sharing is a low-cost processing technique that is commonly used for non-time-sensitive queries.

How Can You Automate Data In Your Business 

To benefit from automation of data processing in your business, you must ensure that proper processes are in place. The following are the steps to getting started with data automation: 

1. Determine the data: 

Determine which data must be automated. Choose the datasets from which you want to draw data and make sure you have permission to access or edit the data. 

2. Choose the right data automation platform. 

Make sure you have the right tools for data collection, analysis, and reporting. Ensure that the platform you choose integrates with all your business software and includes workflow automation to easily automate mundane data tasks. This relieves employees of additional responsibilities, allowing them to focus on strategy and implementation. 

3. ETL Process Development and Testing 

Make a list of all the steps involved in data processing. Know which data sources to connect, which variables to select, what format of values to use, and what results to expect. 

With rule-based workflows, a proper ETL process can streamline data automation. 

4. Automated Work Scheduling 

Schedule daily updates to your dataset. Determine the refresh frequency, data collection, and update frequency. You can refer to the metadata areas compiled as part of your data inventory. 

Establishing clear objectives for the automation procedure, can help teams collaborate effectively during implementation and also helps in tracking its progress. 

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What Information Should You Automate 

More the merrier! The "automate by default" strategy for data uploading ensures efficiency and cost-effectiveness in the long run to maintain high data quality. Here are some pointers for locating potential datasets for automatic uploads: 

Is the dataset updated quarterly or more frequently? 

Is it necessary to modify or manipulate the dataset after it has been uploaded? 

Is the dataset large (more than 250MB)? 

Is it possible to get only the changed rows for each subsequent update rather than the entire file? 

Is it known that data should be obtained from the source network rather than from an individual? 

If your response is a "yes" to any of the preceding questions, then they are excellent candidates for automation, as they eliminate the risk of errors and inefficiency in the long run. 

Challenges of Automated Data Processing 

Automated Data Processing can assist businesses in lowering costs and increasing efficiency. Yet, there are significant challenges that businesses face when automating business processes. 

Common challenges faced by most businesses are: 

Determining the relevant business processes to automate.  

The difficulty for managers to determine these processes originates from their understanding of the current manual processes and assigning resources to areas where they are most needed. 

Legacy system integration and compatibility 

A successful automation can be achieved only when you are able  to integrate it with your company's various business line applications. Challenges occur when older systems do not allow for integration, then you will most likely end up with human intervention, negating the goal of your attempt. 

Security and privacy Issues 

Data Security and privacy are two topics that everyone cares about. Automation of Data Processing calls for a secure flow of information and data between departments. Because of recent data breaches and data manipulation, these two concepts have come to the forefront . Most businesses need to ensure that their systems are secure, but it could be an expensive affair to fund on their own 

Final Words 

The world is becoming increasingly digitized, and data is being produced at a rapid pace. Data is being used by organizations to identify trends, predict future outcomes, and make better decisions. One of the primary drivers of this trend has been data automation. It has aided organizations in gaining insights from their data, improving decision-making processes, and assisting in day-to-day operations.