Automated Data Collection

Introduction 

Data Collection is a critical element of any business. It contains information about product performance, trends in the market, customer behaviour and other attributes. When this data is collected manually it can be tedious, time consuming and also have a high possibility of errors.  

On the other hand, automated data collection methods not only eliminate these disadvantages but also provide faster and accurate results on a real time basis. The advanced AI and ML driven software enables collection and also analysis of data from multiple sources. 

Data Collection typically entails collecting structured or unstructured information and converting it into a format which can be easily read and processed by a computer.  

Typically it extracts information from digital or paper documents and converts the set into a structured format for storing, editing and processing.  

What is Automated Data Collection?  

Gathering data using technology without any need for human intervention may be termed as data collection. This can be achieved by using sensor software or other automated tools. The artificial intelligence combined with machine learning capabilities enable the program to quickly read the files, extract relevant content and store them into digital files. This information can be used for multiple purposes such as decision making, report generation, analytics and many more. 

How is Automated Data Collection useful for your business? 

Automated data collection offers a host of benefits when compared to the traditional manual data collection methods. These benefits include:  

• Time savings: Reduce your data extraction time from hours to minutes. Manual data collection is time consuming whereas automated data collection provides you with accurate and timely results.  

• High on accuracy: As automated data collection helps to minimize errors data collected through this method scores high on accuracy when compared with manual data collection.  

• Extensive data: Automatic data collection can collect data from a variety of sources such as social media, images, websites and databases. This equips you with extensive data for thorough analysis. 

How do AI and ML support data automation? 

AI and ML have increased the standards in the field of data collection. Through their unique features they allow machines to learn from data, analyze patterns and make decisions with zero human intervention.  

AI and ML algorithms automate data collection activities such as web scraping, data extraction and data analysis. Their advanced features are capable of identifying trends and patterns in data through which they provide useful insights about market trends and customer behaviour.  

AI and ML algorithms also perform data cleaning and processing tasks such as removing irrelevant data, identifying anomalies and formatting data for analysis. These features go a long way in cutting time and effort required for data processing. 

Categories of Automated Data Collection 

Broadly, the technique of automated data collection depends on whether your data is structured or unstructured. Let us discuss some of the important types of automated data collection methods. 

a) Web scraping  

Web scraping is a method used for obtaining data from websites. With the help of software that is designed to crawl websites, data is automatically extracted. When businesses want to collect information about current market trends, competitors and customer behaviour web scraping can be used. 

b) Barcode Reading 

One of the dependable and efficient processes is automation of barcode scanning. In this, the program scans barcode data, decodes it and also stores it in a computer or on cloud. Quality information can be extracted for further analysis. Barcode scanning helps in cost reduction as well as improvement in operations. It is predominantly used for inventory management, product tracking and similar activities. 

c) RFID 

RFID (Radio Frequency Identification) is a technology that detects and tracks objects with the help of radio waves. It is broadly used in supply chain management, inventory management and other areas. 

d) Data Collection through Sensors 

Sensor based automated data collection software  gathers data on various parameters such as temperature, humidity or pressure with the help of sensors. This technology is mainly used for environmental monitoring, manufacturing and other similar fields. 

e) OCR  

Optical Character Recognition (OCR) technology  

OCR is a game changer for businesses as it facilitates automated data extraction from scanned documents or image files or even from printed or written text. The data so extracted is converted into a machine readable format which enables data processing activities such as editing or search. 

f) ICR 

Intelligent Character Recognition (ICR) technology  uses artificial intelligence and machine learning to automatically extract the text from handwritten documents. 

Point Guide to Implement Automated Data Collection 

Automated data collection requires meticulous planning and thorough execution. Here is a 5 point guide to implement automated data collection in your business. 

1. Understand Your Data Collection Requirements 

The preliminary step in data collection capture is to understand your data collection requirements. It includes identifying what data needs to be collected such as structured or unstructured, what is the purpose of data collection such as reporting or analytics and the frequency of data collection like monthly quarterly or on a real time basis. 

2. Select Automation Tools 

Once you have freezed your data collection requirements you can proceed towards selecting the right automation tools as per your need. This can include software such as web scraping, sensors, AI and ML backed powerful OCR. 

3. Formulate a Data Collection Action Plan 

After deciding on the tools the next critical task is to formulate a data collection action plan. This plan should contain the specifics of how data will be collected, its frequency of collection and how it will be analyzed. A good data collection action plan should also include a backup strategy in anticipation of any system failures. 

4. Testing Data Collection System 

It is always a foolproof method to test your automated data collection system multiple times before its official launch. Any issues or errors that are thrown up during testing should be addressed and resolved immediately before going live. 

5. Training Your Employees 

Many employees have the apprehension that automated data collection methods are going to kill their jobs. However it is important to recognize that it can lead to requirement of new skill sets thereby creating job opportunities of a higher order. Therefore, for better adoption it is always critical to sufficiently train your staff on how to use the system and analyze and interpret the data collected. 

Common Challenges and Solutions 

In spite of the enormous benefits that automatic data collection offers, they are not free from challenges. Some of the most prevalent challenges in automated data collection technology include: 

Data Quality 

One of the biggest challenges of automated data collection is to ensure data quality. Though automated tools are efficient and collect data quickly, they may not always represent complete and accurate data. This can be overcome by proactively investing in data validation and verification tools to ensure data quality. 

Data Security 

One of the most daunting challenges in automated data collection is data security. Businesses have to ensure that the data is safe in the light of ever increasing cyber attacks. To achieve this, businesses must compulsory use encryption and other security measures to protect their data. 

Interoperability with Existing Systems 

As businesses use multiple ERP systems, an effective data collection tool should be capable of integration with existing systems. The biggest integration challenge occurs if a company is using Legacy systems. In such cases the ideal solution for system integration and interoperability is the use of middleware. 

Cost 

Although automated data collection tools come handy, the small and medium size businesses might find it expensive for adoption. This can be addressed by exploring the usage of open source data collection tools or utilizing cloud base solutions which offer higher flexibility. 

Scalability 

Businesses seeking growth and expansion must definitely ensure that their automated data collection tool is scalable. However if a company is using legacy systems or is operating on less resources scalability can be a challenge. One of the probable solutions can be cloud base solutions which have the pay as you go feature. 

Data Governance 

Data protection and data governance regulations are getting more stringent day by day. Data governance is one of the biggest challenges that businesses face. This issue can be addressed by successful implementation of data governance frameworks and ethical use to ensure data compliance. 

Data Volume 

Automated data collection tools enable collection of large volumes of data as they are collected from multiple data formats. Managing this huge amount of data can be overwhelming for businesses. This can be overcome by using data analytics tools which are capable of extracting insights from large data sets. 

Final Words: 

Data collection automation is a powerful tool for improving data collection efficiency and streamlining business processes. It saves time, improves accuracy, and makes faster decisions. 

It is important to note that implementing automated data collection requires careful planning and execution. Businesses that follow the comprehensive insights outlined in this blog can successfully implement automated data collection and reap its benefits.