What is Automated Data Processing?

Introduction

Automated data processing (ADP) went a long way from punched cards to rows of computers. It refers to processing large amounts of information with minimal manual input, then showcasing the results in a comprehensible way. What are its strong points, and where is it being used?

What Is ADP For?

For any kind of enterprise, optimization of business processes and increase in productivity is a priority. But it isn’t possible without analysis of all aspects of its activities. One can sink a lot of time into company analysis because of the huge amount of information they need to collect – and that tends to be a problem. Why?

Collecting a large amount of raw data manually will lead to inaccuracies and errors. Taking those into account when making important decisions is risky, as it can negatively impact the management strategy and overall workflow.

The automated data processing systems help with this immensely. They can provide results much quicker, all while using up fewer resources and being way more accurate at the same time.

Figure 1: Data processing subfields. Source: Data Processing

What is Data Processing?

Data Processing, as a term, usually refers to all the steps taken in handling data – collection, storage, sorting, processing, analysis, and presentation. "Processing" by itself is a broad term to use. What can it translate to?

• Aggregation: combining multiple pieces of information.
• Validation: making sure that the information is correct and relevant.
• Conversion: translation of information to a different medium or language.

Data analysis and sorting can also be listed as subparts of data processing.

There are lots of different tools for data processing. The variety of software is associated with the specifics of each industry where ADP is used. There are apps for organizing databases, matching queries, filtering information, graphing, etc.

A Simple Example

A simple example of a data processing system is the process of maintaining a cash register. Transactions get recorded as they occur, and they are summarized to determine a current balance. The data recorded in the register is compared with an identical list of transactions - one processed separately by the bank.

A more sophisticated system can further identify the transactions - by source or by type. This information can be used to form statistics, like the total of all earnings for the year. All transactions are recorded consistently, and the same method of bank reconciliation is used each time.

Figure 2: Cash register data processing

ADP with AI and Deep Learning

Another example of ADP, one of those relying on AI and Deep Learning, can be Intelligent Document Processing, or IDP. It's the automation of collecting data from unstructured sources and converting it into usable documents. IDP takes advantage of natural language processing, sentiment analysis, entity recognition, and other technologies to achieve near-perfect accuracy of its results. Challenges presented to IDP systems include recognizing document types (such as trying to tell a shipping label from an invoice) and extracting data outside of fixed layouts (dates and addresses can be anywhere on the document). For instance, companies who receive a large number of candidate resumes can take advantage of Natural Language Processing for resume parsing and job matching - relieving the talent specialists of having to read through each application.

Figure 3: Extracting structured candidate info from resumes.

An important subset of IDP is Optical Character Recognition - conversion of text-based images into text format. The transcription works one symbol at a time. Basic OCR can't identify context or work away from a template (that's where IDP comes in), and handwritten text still presents a problem.
IDP and OCR sometimes get used interchangeably but there is a significant difference between the two.

For example, banks might be interested in developing an OCR cheque scanner to automatically parse pictures of cheques, so that the banking app users can cash cheques without having to visit the bank.

Figure 4: OCR cheque scanning for banks.

Closely related is Automated Speech Transcription. Instead of images, it extracts text from audio files, most often in real-time. Think auto-generated subtitles for video streaming services. Manually typing out the transcripts is nearly impossible to do in real-time, not to mention the cost of the process. Meanwhile, automated solutions are quick and affordable.

What Can Be Automated

The operational capabilities of the modern technology used in these systems make it possible to automate a lot of labor that used to be manual. The technical level determines the possibilities of automatization of the following procedures in the management process:

- Organization: modeling of organizational management structures and imitation of production processes under various parameters to select the optimal ones;

- Coordination and regulation: giving commands to workplaces according to the plan or instructions made for certain types of work or operations;

- Control: monitoring the state of the controlled subject in all parameters, as well as the timely and complete execution of management commands;

- Accounting: collection and systemic processing of all relevant reliable information about the availability and movement of resources, as well as about the states and phenomena that take place in the production;

- Analysis: comparison of normative, planned, and actual indicators characterizing certain operations or processes, identification of deviations from the specified parameters, indicating the reasons for these deviations;

- Reporting: automatic generation (based on primary data of summary indicators for typical forms of established accounting, statistical and other reporting using special transfer arrays) reference books, - as well as the simultaneous creation of machine media with summary indicators of reporting for transmission via communication channels to external institutions of the highest level.

Figure 5: Data reporting

Conclusion - What Are the Benefits?

Following all that, ADP systems help with:

• quality-of-life improvements based on increasing the level of information support for specialists with the help of accumulated data;
• reducing the costs of managing by reducing the labor intensity of collecting, transferring, and processing information at all levels of management, optimizing common information resources;
• raising the level of qualifications of employees through the introduction of new technologies to support the educational process, increasing the level of information and reference services for the population.

Scalability is difficult without automation, and growing is impossible without learning from data. This makes Automated Data Processing a crucial aspect of any business whose goal is to grow, learn from mistakes, and leave all the boring work to computers, keeping humans engaged with interesting and challenging work.