Data Automation Overview

How Data Automation Enhanced Organizational Productivity?

As promising as data collection and analytics sound for business growth, it entails the difficult task of data entry and management. If the jobs related to recording customer data and analyzing it is done manually, the results might not be as promising. They are bound to be erroneous and molded by human judgment.

Fortunately, with the invention of Data Automation technology, dealing with vast masses of data and analyzing it has become more accessible. The reports generated can be uploaded directly to collaborative platforms like sprint planning software for further breakdown, deliberation, and team decisions. Today, you can find several devices that facilitate data recording and analysis at the same source. That way, the organizational decisions are not misled due to errors of redundancy or inconsistency.

Read on to discover more about data automation, its examples, and how it benefits the current organizational landscape.

What is data automation?

The process of inserting and updating information regarding consumers, visitors on a website or retail store, on a database management system is known as data entry. When the automation technology is used to enter this data rather than feeding it in manually, the process becomes data automation.

An open data program is supposed to make all up-to-date information available to its users without delays. Thus, data automation is crucial for the sustainability of a public data program. Also, with the help of automation done by machines, an individual can avoid errors of redundancy and human manipulation.

Not to mention, it lightens the workload of employees so that they can focus on other vital operations.

Extract, Transform, and Load is the three essential elements of data automation.

The term Extract defines the process of extraction or collection of data from source systems such as a website's traffic. This necessary information is then transformed into a standard structure and file format so that it can be recorded. The standardization of data also includes abbreviating all state names, converting all dimensions in centimeter, etc.

Data automation ends with loading each entry on the open data program so that it is accessible to a majority of interested individuals.

Various Data Automation Strategies Adopted by Organizations

The data automation strategies of organizations may vary even if they are present in the same industry. No matter what, the one you select for your organization should serve the purpose of enhancing organizational productivity and engaging the right people at the right time.

The strategy depends upon who controls the process of data automation. It can either be centralized, hybrid, or decentralized.

1. A centralized model of data automation refers to the structure when the departmental source systems are used to extract the data. The entire ETL process takes place at the central IT organization.

2. The hybrid model may differ in structure but entails that the agencies not only deliver the extracted information from the source but also transform them into the required format. Once the Central IT organization receives the data, it can load them on the program for analysis.

3. A decentralized model is when an organization receives the finished data programs from the agencies or departments. In such cases, there is the minor role of central IT in the processing of data.

Source Data Automation

Much like automation of data that is done by extracting data from source systems, there is source data automation. It involves inserting data on the same basis as using Bar Code Readers at the supermarkets. This enables the store owners to have all the information needed to manage sales and inventory in one place so that they can make next quarter's inventory decisions.

It is a preferred method of data-entry because it eliminates human efforts and delays. The traditional data entry methods involve an extra step of collecting information on paper and transferring it to the computerized database management software for analysis. Human work is less likely to be free of errors, inaccuracy, redundancy, and inconsistent data leads to faulty analysis.

Hence, the Source Data Automation devices are used to feed data instantly, so that you have ready to process data available instantaneously. One cannot doubt the accuracy of this process because computers maintain calculations and consistency.

What is an Example of Source Data Automation?

Automating data at the source has made commercial data-entry more accurate and accessible, saving huge costs on employing individuals who would do the job for you with inevitable inaccuracy.

For instance, when individuals place their orders at restaurants, the charges are directly recorded in the database through touch screens. In this way, the data is not supposed to be recorded twice by a restaurant. Most retail stores and fast-food chains we come across use these displays on their workstations. Apart from generating accurate bills, source data automation is the purpose of these machines.

Added advantages of source data automation include little time spent on the checkout counter by each customer by eliminating the need for manual inputs. All supermarkets can place bar codes on their products and then scan them at the time of checkout, record all the essential information, and generate the bills. The data collected will provide information about which product is selling faster than others in the inventory, giving the owner enough time to restock.

The checks also have Magnetic coding, which is decoded by MICRs, making the check processing easier and cost-effective for banks.

The time saved by counter operators in dealing with each customer can be used to extend services to more customers every day, helping organizations grow. Here are some devices used for source data automation.

Source data-entry devices are meant for reading the data in a consistent format and feed it into the computer directly. Some of them are:

Data-entry devices

• Scanners

A scanner uses the light-sensing technology to read the image placed in front of it and store it in the computer in a digital form.

• Bar-code Readers

A Bar Code Reader, as the name suggests, is used to read and comprehend bar codes. These bar codes are advanced coding symbols containing all the information about the product and its price. Once the reader scans the code, it translates it into a digital format that is stored on the computer.

• Radio Frequency Identification (RFID)

RFID reads the tags with microchips on them. Each microchip has its power source and contains code numbers that are scanned by RFIDs. This is a more advanced technique for data automation and has started to replace bar code readers in several scenarios.

• MICR — Magnetic Ink Character Recognition

These are marked recognition devices that read magnetized ink, such as that printed on the bottom of bank checks.

• OMR — Optical Mark Recognition

Optical mark recognition is used to store candidates' scores in a test and involves pencil marks on special OMR sheets. It uses light and opacity of blanks to register data.

• OCR — Optical Character Recognition

Various organizations that have their customers fill feedback forms manually need an email address to increase their mailing list more than reviews. They can use OCR software to convert their handwritten words into computer editable text. The device looks like a handheld scanner and converts data into a digital format that can be stored in the computers.

What is Big Data Automation?

Big Data has revolutionized the digital and organizational landscape in the way in which they operate. The analytics has emerged to question all the disparities in employee performance or a particular product in the market. This is a highly-polished technology that allows organizations to discover patterns in the version, whether it is correcting or appreciating it.

The collection of Big Data, however, might pose issues for an organization because there are limited human resources and monetary resources. Fortunately, data automation has come to the rescue of businesses, allowing data collection without involving manual efforts. In this way, the job of predictions can be done without having to go through an added step for the correction of manual efforts.

Research Approves of Big Data Automation too!

Recently, the prestigious MIT researched Automation by eliminating the human factor from the processing of Big Data completely. The result of placing various Data Science machines to contest with humans was that the machines performed a lot better than their human competitors. The accuracy of automated processing at the end of the study was measured at 96%, which is impressive because social skills could hardly reach 90%.

Another factor that led to the victory of machines was that they took merely a few hours in completing a task that took expert data-enthusiasts months to complete.

This research shows that the automation process can prevent human intervention delays and inaccuracies.

Benefits of Automation for an Organization

Many organizations rely upon a team of data scientists to work on data warehouses to identify patterns of increasing demand or product failure. This process can take weeks and involves a lot of human effort that costs an organization. However, when automation is brought in an organization, the operational costs decrease significantly. Besides, efficiency and accuracy are achieved in lesser time as compared to manual analysis.

By employing automation, the scalability of this technology also permits the organization to look out for more expansion opportunities.

Here are some roles played by Big Data Automation

• Analysis of Time-Varying Data

It involves employing automation services to analyze big data over some time. This is facilitated by the segmentation of data that can be seen as a pragmatic approach. The segments vary according to the needs of an organization, such as according to different periods and features that need to be addressed in the analysis.

• Role in Data Preparation

Data automation can be used to simplify the format of data used for analytics and predictions in the future. This is an issue often faced by analysts because they cannot deal with complex sets of incomprehensible data. Also, with the reduction in time taken for predictive analysis, an organization can look up for better expansion opportunities, way before their competitors.

• Convenient Representation of Data

The automation data processing technology crunches enormous volumes of data so that it can be processed to present in a more measurable format. Now, the data scientists can use this quantifiable information to identify problems and share the analysis with various stakeholders. With the use of predicted issues in data science, the data scientist will be able to bring enhanced precision in the process.

By employing data science automation, organizations can become self-reliant and will not have to invest in outsourcing the job. They can hire a few experts, and the responsibility of analysis will be done more efficiently.

What are some examples of automation?

Data automation in the real world is used by manufacturing organizations to study the demand for a particular niche of products in the market. If it is high, they can also invest in the production to meet the supply, hence expanding their business scope. AI automation in manufacturing takes data automation to the next level by leveraging advanced algorithms and machine learning techniques to analyze vast amounts of production data in real-time, enabling manufacturers to optimize their operations, identify patterns, and make informed decisions to meet market demand efficiently while ensuring cost-effectiveness and business growth.

Another scenario where automation works are when an organization has to hire employees. Instead of going through resumes manually, they can have them fill in forms and collect their details. Only the ones who qualify for the job can be called for an interview, saving the organization's time.

When business owner employs data scientists, they want to enhance machine learning, model building, data cleaning, artificial intelligence, feature engineering, and other processes related to their data warehouse.

Conclusion

The automation has reduced the organizational dependency on human intelligence, resulting in enhanced accuracy when it comes to data, whether it is data warehouse used by companies operating on a large scale or smaller enterprises like superstores. The business owners can now leverage their resources without engaging in the complexities of recruiting a full-fledged team of Data Scientists. This has also resulted in saved costs and time. Moreover, the data scientists employed by the organization can now focus on the core tasks like studying discrepancies rather than indulging in time-consuming acts of analyzing real data.