What is automated data processing? A primer for modern analysts

If you have been keeping an eye on the future of data analytics, you’ve likely learned that many organizations and businesses rely on software for daily operations, and while doing so, they generate vast and ever-growing amounts of data. Even without digging into the sources, we are certain that manually managing such data comes with many challenges: Errors, time spent, and inefficiency. It requires a lot of labor while bringing cost-sensitive outcomes in case of mistakes.

Automated data processing, short for ADP, entails using technology to process, organize, and manage data automatically without human input. This technique's speed and accuracy improve businesses' data handling efficiency, enabling better and more informed decision-making.

In this article, we will introduce you to automated data processing, explain why you need it, how it works, and discuss the common types of ADP systems. We’ll then list the benefits of automated data processing from a business standpoint, the use cases for automated data processing across various industries, and we will conclude by introducing you to your first ADP software.

But first, let’s answer a few questions

What? This depository is a rapid curation of everything you need to know about data processing automation, which is essential in modern data analysis. It also serves as an entry point into AI-driven data platforms.

Who? This repository addresses professionals at all levels, from data analysts and scientists, business intelligence (BI) teams, information technology (IT), and data engineering groups to those in finance and banking, healthcare and medical research, e-commerce and marketing, logistics and supply chain management, executive and decision-making, and AI and automation.

When? After achieving solid business grounds, you need to scale data operations, or you need to transform digital initiatives, explore automation, and seek fast insights for decision-making. Or, it could be that you want to pick a data processing tool or reduce costs and errors in data operations.

Why? Whether you’re an analyst, business leader, or tech enthusiast, immersing yourself in modern data pipelines can help you if you’re struggling with workflows. It can also keep you ahead in your industry through more insights in less time, effort, and resources. Let’s dive in.

What is automated data processing?

Automated, also known as automatic, data processing is the interconnection between operations, techniques, tools, and the people using them to manipulate data. It is all about using software solutions to simplify data tasks. An example is a manager who uses Microsoft Excel to calculate weekly product conversions from their blog. The process is manual and tiring, and this calls for automation to ease the process.

The need for data automation software still rises because organizations want to handle data smoothly. Besides top industry tech firms, smaller providers are showing specialty addressing the needs across different industries like healthcare, governments, financial services, etc. To be specific, organizations seek ADP to improve efficiency, avoid data silos, and improve the integrity and security of data.

As we gather from IBISWorld Industry Reports, revenue from data processing, hosting, and management in the United States (U.S) is $383.8 billion at the time of writing, indicating a compound annual growth rate of 9.2% in the last half-decade.

Why should you use automated data processing software?

On the note of efficiency, handling data manually is time-consuming, thus expensive in the long run. If a team spends lots of time handling data, it robs them of the focus on revenue-generating activities. Some tools, like calculators, speed up the process to some degree, but when you have to create a pie chart with hundreds of entries, for instance, even with the use of powerful platforms like Power BI, the success of your business stalls if it depends on you creating such charts hourly. With automation, efficiency increases as you handle repetitive tasks.

Expert or not, to err is human. Business intelligence is sourced from data manipulation, in which there are many points where errors can be introduced. For example, a data expert could merge incorrect data into a base, integrate it at the wrong termination points, or use the wrong source in a visualization setup. While threats to data integrity are rarely accidental, insecure storage, migration, or manipulation pose a risk. It is by automating the handling process that businesses preserve integrity and reduce the risk of breach by malicious actors.

In the absence of an automated data processing system, data silos become the norm, where collections of data live in inaccessible locations and aren’t of any valuable use to your organization. Fragmentation of data is a scenario that accompanies data silos. Consider an organization using email software and analytics in the marketing department, a customer relationship management (CRM) tool to handle sales, and an in-house software for financial operations. Strategic business reports require the company to use data from all three sources if it is to derive meaningful insights. This prevents the full utilization of data, wastes resources, and discourages collaboration, all due to data inconsistency and incompatibility. Only by switching to automated data processing solutions can the problem be solved.

How automated data processing works

Essential data tasks where automation replaces traditional methods include the following across a majority of the data analytics lifecycle:

• Collection — Accessing data from various sources forms the base of ADP. It is where raw data is gathered from several sources, such as online forms, databases, sensors, and APIs. Automated systems integrate different data sources for comprehensive coverage in data collection.
• Cleaning — Once collected, raw data is likely to contain errors, duplicates, or inconsistencies. At this point, data cleaning tools help prepare the data by identifying potential issues, correcting them, and occasionally removing irrelevant data to ensure accuracy and reliability.
• Validation — Not to be mistaken for cleaning, validation checks for accuracy and completeness of data. The checks verify data formats, missing values, and its integrity. The primary goal is to ensure that data conforms to expected rules by verifying data types, formats, validity, and if numbered, it is in the expected range.
• Transformation — This step slightly overlaps with the last two data operations and transforms validated data into usable formats for analysis or storage. The involved steps are removing inconsistencies, filtering irrelevant data, and aggregating for ease of processing. Automation helps standardize data into usable formats.
• Storage — Validated data, processed or not, is up and eligible for storage to ease accessibility and future reference. This checkpoint uses automation to store data in digital repositories like cloud solutions that support scalability and flexibility to accommodate the ever-growing volumes of data. Automation is also key in organizing data through structured formats such as databases or warehouses for easy retrieval.
• Processing — Being the core of ADP, this procedure generates meaningful insights. It could also be where specific operations are done on data, including complex computations, analysis, or the involvement of machine learning algorithms. Automation seeks to use advanced processing methods to derive actionable insights from data.
• Analysis — Closer to the final step of data operations, where meaningful insights are extracted from data. Where large data sets are the norm, automation leverages artificial intelligence and machine learning algorithms to analyze and give actionable enlightenment.
• Output — This step is the automation’s output in a usable form for decision-making and further action. Effectual output ensures the accessibility of insights and information, taking the form of reports, visualizations, alerts, and automated actions.

The above steps explain how automated data processing turns raw data into meaningful insights. Now, we need to know the types of automated data processing techniques.

Common types of automated data processing systems

Many techniques are used in ADP systems, each with its specific application and use case. Below are examples:

• Batch processing — A method to collect and process huge data groups in batches at scheduled time frames. It optimizes resource usage by scheduling operations for off-peak hours, making it ideal for tasks that don’t need immediate results, such as reports or payrolls.
• Stream/real-time processing — A technique that processes data instantaneously, or, put differently, handles data at the time of generation. It applies to domains requiring immediate action/feedback, such as monitoring systems, online transactions, market trading, and IoT data.
• Multiprocessing — An approach that breaks down large data sets into small chunks, which are then processed simultaneously. It is a good fit for heavy computation applications such as data analysis, where parallel processing speeds up processing time.
• Distributed processing — A processing technique where data is spread across multiple servers, machines, or computers that are all interconnected. It is a way to achieve fault tolerance, improve performance, and enhance the efficiency and reliability of digital systems working with large data sets. It is often used in cloud computing and big data environments and is the foundation of blockchain technology.
• Online transaction processing — A method that processes many simple, real-time transactions, such as ticket reservation systems, order management systems, and ATM network operations.

Benefits of automated data processing for analysts

Automated Data Processing tools transform the way organizations perceive and handle data. Some of the benefits are:

• Enhanced operational speed — By quickly processing large volumes of data, automated data processing allows workers to shift their focus to more strategic business aspects. An automated system could handle thousands of customer orders while simultaneously generating critical insights.
• Greater data precision — By removing the chances of human error in data work, automation leads to improved accuracy and consistency, which is reliable in decision-making. In machine learning, accurate data is valuable for training business models and agents.
• Reduced operational costs — Repetitive and time-consuming tasks, once automated, save labor and operational costs for organizations. Now that there are no costs associated with reworking or error handling, organizations dedicate fewer resources to data operations, which leads to more savings for companies.
• Accelerated insights and responses — Automation promotes fast decision-making through quality business insights by providing real-time access to data analytics. At executive levels, businesses can promptly respond to evolving landscapes and incoming opportunities.
• Elevated customer satisfaction — Companies achieve personalized responses and services using automation, enhancing the overall customer experience. Quick handling of customer information and queries leads to faster service delivery, a core component of customer satisfaction.
• Robust data security — Because automated systems come with advanced security features, organizations can use them to prevent unauthorized access to data workflows and maintain their integrity. To protect company data, ADP systems use audits, encryption, and access control.
• Scalability — An organization’s growth goes hand in hand with increased amounts of data to process. ADP systems are at the heart of companies that want to scale without proportionally allocating more resources to handling data.

Use cases of automated data processing in various industries

We can mention many industries where automated data processing enables efficient operations, faster insights, and improved customer service. Let’s gloss over a few:

• Finance and banking — Financial institutions accumulate, process, and analyze large data sets. Automation allows them to precisely process transactions, forecast market trajectories, and analyze risks, among other tasks. A good example is using ADP to review loan data, income, and credit history to evaluate the risk of offering a loan to a customer. This FMEA analysis template from Quadratic is a good example.
• Healthcare — Handling medical data, patient records, and insurance claims is now easy with ADP. Besides streamlining administrative tasks, healthcare organizations can pull in research from diverse sources to make more informed data decisions for patient outcomes. It also allows for healthcare breakthroughs when the data is available for better research procedures and utility.
• Manufacturing — Using ADP, manufacturers can enhance product lines, monitor equipment performance, track materials, and improve the supply chain. As a result, automation helps cut down downtime and enhance the quality of products.
• Telecommunications — Automation helps telecommunication companies handle extensive data set operations such as transactions, customer account management, network performance monitoring, and real-time customer support.
• Retail — This being the most granular level of businesses, retailers have ADP systems to help them with supply chain operations, inventory tracking, customer data analysis, and other operations. Consequently, retailers, too, provide personalized experiences while optimizing their workflows.
• E-commerce — Nearly any product on the internet is a buy-and-sell setup. For specialized marketplaces such as e-commerce applications, ADP is a gateway to insights into customer patterns and preferences. Automation is a lever to understanding customer browser history if one wants to give personalized product recommendations.
• Smart cities — ADP systems help manage traffic, monitor air quality, and optimize energy consumption in modern cities. These systems improve the sustainability and efficiency of city navigation where there are many vehicles.

Since this discussion has been all about the theoretical aspects of automated data processing up to now, for the remainder of this post, we will focus on the practical aspects of automated data processing. We will introduce you to an automated data processing tool that can help you kick-start your journey.

Quadratic: AI-powered data processing for modern analysts

Quadratic: AI Spreadsheets with AI, Code, and Connections! (FULLY FREE)

Quadratic is a modern, interactive, browser-based application that integrates programming languages like Python, JavaScript, and SQL with AI functionalities into spreadsheets. It blends the simplicity of spreadsheet interfaces (such as Google Sheets and Excel) with the power of code, AI, and data connections to make complex data analysis more friendly in a collaborative setup for scientists, engineers, and business analysts.

Its unique value proposition is that it allows users to perform complex data operations using either natural language or a technical approach using code. It is ideal for modern teams that seek to move fast without writing complete standalone data scripts in JavaScript, Python, or SQL. It also bridges the gap between spreadsheet usability and advanced data science functionalities. Writing formulas is convenient with Quadratic’s AI formula generator, where you can opt for one-liners or an expanded view that spans as many lines as you see fit to ensure a smooth workflow.

Quadratic's built-in support for third-party libraries allows you to pull open-source tools directly into spreadsheets for enhanced functionalities. This means you could bring in a Python library, like Plotly, to create visualizations from data. It also allows you to work with external APIs to fetch data, with an automatic update in the cells.

Moreover, Quadratic is AI-powered, so you can leverage code autocompletion. In terms of speed and comfort, Quadratic performs at 60 frames per second and runs calculations on your device by default, making computations quick even when dealing with large data sets. It is also built using a security-first approach to ensure your data is securely stored. And above all, Quadratic is a spreadsheet that supports real-time collaboration, an ideal requirement in modern workflows.

Conclusion

We can't emphasize the importance of automated data processing and its cruciality in modern businesses. ADP systems automate data processing to ensure smooth collection, cleaning, validation, transformation, storage, processing, and other operations carried out on data.

If you’re going to incorporate an ADP into your digital setup, then some of the best practices you can follow are: define clear objectives, ensure data quality before automation (or enact it in the process), and lastly, monitor and optimize your workflow.

Successful data processing and management at the business level starts with choosing the right tool for the job. In many of the tools sought after for automated data processing, Quadratic is an outstanding choice, providing you with a unified platform that connects to all of your data sources and enables you to clean, analyze, and visualize your data in seconds.

Try Quadratic for free to automate data processing at your organization today.