What Is Data Processing?

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Every day, vast amounts of data are generated through activities like online shopping, social media interactions, and transactions. Statista predicts that by 2025, global data creation will reach 175 zettabytes. As digital interactions continue to grow, managing and understanding this data becomes crucial. Data processing plays a key role in turning raw data into valuable insights.

In this article, we will explain what data processing is, look at its different types, and give examples to show how it turns data into useful information for making decisions.

What Is Data Processing?

Data in its raw form is useless to any organization. Data processing is the method of collecting raw data and translating it into usable information. It is usually performed step by step by a team of data scientists and engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.

Data processing is essential for organizations to create better business strategies and increase their competitive edge. By converting the data into readable formats like graphs, charts, and documents, professionals can understand and use the data.

Now that we've defined what data processing is, let's explore the data processing cycle, which outlines the steps involved in transforming raw data into valuable insights.

Six Stages of Data Processing

The data processing cycle consists of a series of steps where raw data (input) is fed into a system to produce actionable insights (output). Each step is taken in a specific order, but the entire process is repeated in a cyclic manner. The output of the first data processing cycle can be stored and fed as the input for the next cycle, as the illustration below shows.

Generally, there are six main steps in the data processing cycle:

Step 1: Collection

The collection of raw data is the first step of the data processing cycle. The type of raw data collected has a huge impact on the output produced. Hence, raw data should be gathered from defined and accurate sources so that the subsequent findings are valid and usable. Raw data can include monetary figures, website cookies, profit/loss statements of a company, user behavior, etc.

Step 2: Preparation

Data preparation or data cleaning is the process of sorting and filtering the raw data to remove unnecessary and inaccurate data. Raw data is checked for errors, duplication, miscalculations or missing data, and transformed into a suitable form for further analysis and processing. This is done to ensure that only the highest quality data is fed into the processing unit. 

The purpose of this step is to remove flawed data (redundant, incomplete, or incorrect data) to begin assembling high-quality information so that it can be used in the best possible way for business intelligence.

Step 3: Input

In this step, the cleaned and prepared data is converted into a machine-readable format and entered into the processing system. This can involve data being input manually through a keyboard, scanned from physical documents, or imported from other digital sources such as APIs or databases. The input phase ensures that the data is properly structured and ready for the next stage of processing.

Step 4: Data Processing

In this step, the raw data is subjected to various data processing methods using AI and machine learning algorithms to generate a desirable output. This step may vary slightly from process to process depending on the source of data being processed (data lakes, online databases, connected devices, etc.) and the intended use of the output.

Step 5: Output

Finally, the data is transmitted and displayed to the user in readable form, such as graphs, tables, vector files, audio, video, documents, etc. This output can be stored and further processed in the next data processing cycle. 

Step 6: Storage

The last step of the data processing cycle is storage, where data and metadata are stored for further use. This allows for quick access and retrieval of information whenever needed and also allows it to be used directly as input in the next data processing cycle.

Once the data has gone through the various phases of the processing cycle, it is important to understand the different types of data processing methods that can be applied to achieve specific objectives and outcomes. Let us go through each of them.

Types of Data Processing

Different types of data processing exist based on the source of data and the steps taken by the processing unit to generate an output. There is no one-size-fits-all method for processing raw data.

• Batch Processing

In batch processing, data is collected over a period and then processed in batches. It’s suitable for handling large amounts of data where immediate output is not necessary. A common example of this is a payroll system, where data is collected throughout the month and processed at the end.

• Real-time Processing

Real-time processing handles data instantly as soon as the input is received. It's ideal for scenarios requiring quick responses and works best with small volumes of data. A typical use case is withdrawing money from an ATM, where the system needs to respond within seconds.

• Online Processing

This type of processing involves data being automatically fed into the CPU as soon as it becomes available. It’s used for continuous and immediate data processing, making it perfect for applications like barcode scanning at checkout counters.

• Multiprocessing

Also known as parallel processing, multiprocessing breaks down data into smaller frames and processes them simultaneously using two or more CPUs within a single computer system. A real-world example of this is weather forecasting, which demands high processing power and speed.

• Time-sharing

Time-sharing is a form of computing in which computer resources and data are divided into time slots, allowing several users to work together simultaneously. This allows systems to be utilized by multiple users simultaneously without conflict.

After the data has been successfully input into the system, the next step involves applying various data processing methods to transform this raw data into meaningful and actionable insights. Let us learn more about the methods.

Data Processing Methods

Five data processing methods exist: manual, mechanical, electronic, distributed, and automatic. Let's learn more about each of them.

1. Manual Data Processing

This data processing method is handled manually. The entire process of data collection, filtering, sorting, calculation, and other logical operations are all done with human intervention and without the use of any other electronic device or automation software. It is a low-cost method and requires little to no tools, but produces high errors, high labor costs, and lots of time and tedium.

2. Mechanical Data Processing

Data is processed mechanically through the use of devices and machines. These can include simple devices such as calculators, typewriters, printing presses, etc. Simple data processing operations can be achieved with this method. It has much fewer errors than manual data processing, but the increase of data has made this method more complex and difficult.

3. Electronic Data Processing

Data is processed with modern technologies using data processing software and programs. A set of instructions is given to the software to process the data and yield output. This method is the most expensive but provides the fastest processing speeds with the highest reliability and accuracy of output.

4. Distributed Processing

Distributed processing refers to distributing the processing power across multiple computers or devices. This methodology increases the speed and reliability of your operations by drawing on the collective strength of numerous systems. It’s particularly effective for handling large-scale processing tasks that a single computer might struggle with.

5. Automatic Data Processing

Automatic data processing relies on software to carry out routine operations without human intervention. By automating repetitive tasks, this method not only boosts efficiency but also reduces the chances of human error. It allows teams to focus more on strategic efforts rather than manual data handling.

Once the appropriate data processing methods have been implemented, various data processing tools are utilized to streamline and enhance the efficiency of transforming raw data into valuable insights. Let us learn about 5 such data processing tools.

Data Processing Tools

Here are some of the most popular data processing tools that help businesses manage, process, and analyze large amounts of data:

• Apache Hadoop

Apache Hadoop is an open-source tool used to store and process large datasets across many computers. It can handle huge amounts of data and process it quickly. Hadoop uses a system called MapReduce to split tasks into smaller chunks, making it faster and more efficient for big data projects.

• Apache Spark

Apache Spark is another open-source tool that processes data quickly. It’s known for its speed because it works in memory rather than writing data to disk. Spark can handle both batch and real-time data, making it a versatile tool for different kinds of data tasks. It also works well with machine learning and other tools like Hadoop.

• Google BigQuery

Google BigQuery is a cloud-based tool that allows users to analyze large datasets quickly. It can process massive amounts of data in seconds and integrates well with other Google Cloud services. BigQuery is scalable, meaning it can grow with the data needs of any business.

• Talend

Talend is a tool for connecting and managing data from different sources. It helps businesses clean, process, and move data easily. Talend is known for its user-friendly interface, allowing data professionals to design and manage data processing tasks without complex coding.

• Microsoft Azure Data Factory

Microsoft Azure Data Factory is a cloud-enabled service that allows companies to design and govern data pipelines. It has the capability to handle data in both batch and streaming modes, and is well-integrated with various services in Microsoft Azure. The tool’s drag-and-drop interface makes it easy to design data tasks without needing to write code.

Examples of Data Processing

Data processing occurs in our daily lives whether we are aware of it or not. Here are some real-life examples of data processing:

1. Stock Trading Platforms: These platforms process real-time market data, analyzing thousands of transactions per second to generate actionable insights like stock trends and price predictions.
2. E-commerce Personalization: Online stores use customer browsing and purchase history to process data, offering personalized product recommendations that enhance user experience and drive sales.
3. Ride-Hailing Apps: Apps like Uber process geolocation and traffic data in real time to optimize routes, set dynamic pricing, and match drivers with passengers efficiently.

The Future of Data Processing

Data processing has moved towards cloud computing. It offers faster, more efficient, and cost-effective ways to handle large volumes of data. Cloud platforms bring everything together instead of relying on separate systems, making data easier to manage and update. These platforms also support growth, allowing both large and small businesses to handle increasing data needs. As data grows, cloud computing will keep playing a significant role in managing, processing, and storing it with greater speed and reliability.

Data Science vs. Big Data vs. Data Analytics

 

Data is everywhere and part of our daily lives in more ways than most of us realize. The amount of digital data that exists—that we create—is growing exponentially. According to estimates, global creation of data will top 180 zetabytes.

Therefore, there is a need for professionals who understand the basics of data science, big data, and data analytics, and can do comparisons such as data science vs data analytics, which help differentiate between the various data processing disciplines.

These three terms are often heard frequently in the industry, and while their meanings share some similarities, they have some profound differences. This article will give you a clear understanding of the meaning, application and skills required to become a data scientist, big data specialist, or data analyst.

Let’s begin by examining each concept separately.

What Is Data Science?

Data science is a field that deals with unstructured, structured data, and semi-structured data. It involves practices like data cleansing, data preparation, data analysis, and much more.

Data science is the combination of: statistics, mathematics, programming, and problem-solving;, capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data. This umbrella term includes various techniques that are used when extracting insights and information from data.

What is Big Data?

Big data refers to significant volumes of data that cannot be processed effectively with the traditional applications that are currently used. The processing of big data begins with raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.

Big data is a buzzword used to describe immense volumes of data, both unstructured and structured, that can inundate a business on a day-to-day basis. Big data is used to analyze insights, which can lead to better decisions and strategic business moves.

In summary, Gartner provides the following definition of big data: “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

What is Data Analytics?

Data analytics is the science of examining raw data to reach certain conclusions.

Data analytics involves applying an algorithmic or mechanical process to derive insights and running through several data sets to look for meaningful correlations. It is used in several industries, which enables organizations and data analytics companies to make more informed decisions, as well as verify and disprove existing theories or models. The focus of data analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

Now, let’s explore the applications of data science, big data, and data analytics.

Applications of Data Science

• Internet Search

  Search engines make use of data science algorithms to deliver the best results for search queries in seconds.

• Digital Advertisements

  The entire digital marketing spectrum uses data science algorithms, from display banners to digital billboards. This is the main reason that digital ads have higher click-through rates than traditional advertisements.

• Recommender Systems

  The recommender systems not only make it easy to find relevant products from billions of available products, but they also add a lot to the user experience. Many companies use this system to promote their products and suggestions in accordance with the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.

Applications of Big Data

• Big Data for Financial Services

  Credit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment banks all use big data for their financial services. The common problem among them all is the massive amounts of multi-structured data living in multiple disparate systems, which big data can solve. As such, big data is used in several ways, including:

1. Customer analytics
2. Compliance analytics
3. Fraud analytics
4. Operational analytics

• Big Data in Communications

  Gaining new subscribers, retaining customers, and expanding within current subscriber bases are top priorities for telecommunication service providers. The solutions to these challenges lie in the ability to combine and analyze the masses of customer-generated data and machine-generated data that is being created every day.

• Big Data for Retail

  Whether it’s a brick-and-mortar company an online retailer, the answer to staying in the game and being competitive is understanding the customer better. This requires the ability to analyze all disparate data sources that companies deal with every day, including the weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data.

Applications of Data Analytics

• Healthcare

  The main challenge for hospitals is to treat as many patients as they efficiently can, while also providing a high. Instrument and machine data are increasingly being used to track and optimize patient flow, treatment, and equipment used in hospitals. It is estimated that there will be a one percent efficiency gain that could yield more than $63 billion in global healthcare savings by leveraging software from data analytics companies.

• Travel

  Data analytics can optimize the buying experience through mobile/weblog and social media data analysis. Travel websites can gain insights into the customer’s preferences. Products can be upsold by correlating current sales to the subsequent browsing increase in browse-to-buy conversions via customized packages and offers. Data analytics that is based on social media data can also deliver personalized travel recommendations. 

• Gaming

  Data analytics helps in collecting data to optimize and spend within and across games. Gaming companies are also able to learn more about what their users like and dislike.

• Energy Management

  Most firms are using data analytics for energy management, including smart-grid management, energy optimization, energy distribution, and building automation in utility companies. The application here is centered on the controlling and monitoring of network devices and dispatch crews, as well as managing service outages. Utilities have the ability to integrate millions of data points in the network performance and gives software engineers the opportunity to use the analytics to monitor the network.

How Are These Technologies Impacting the Economy?

Data has become the engine that drives almost all of today’s activities, no matter if they’re in the fields of healthcare, technology, education, research, or retail. Additionally, business orientation has evolved from a product-focused model to a data-focused one. Companies of all sizes value information, no matter how trivial that data may seem at first glance. Information analysis and data visualization helps marketers and analysts acquire actionable business insights. This demand has created a need for experts who can pull useful, meaningful insights out of the terabytes of data available today.

While big data helps banking, retail, and other industries by supplying important technologies like fraud-detection and operational analysis systems, data analytics enables industries like banking, energy management, healthcare, travel, and transport develop new advancements by utilizing historical, and data-based trend analysis. Data science expands on that in more ways by enabling companies to explore new strategies in scientific discovery, medical advancements, web development, digital advertisements, ecommerce – literally, anything you can imagine.

What Does a Data Scientist, Big Data Professional and Data Analyst Do? 

In an effort to better understand the whole data science vs. data analytics comparison, let’s take a look at what each occupation does.

Data scientists work closely with business stakeholders to gain an understanding of their goals, and figure out how to use data to meet those goals. They are responsible for cleaning and organizing data, collecting data sets, mining data for patterns, refining algorithms, integrating and storing data, and building training sets. 

As for Big Data professionals, well, the term “Big Data” is no longer a “big” thing when describing a career or job position. Big Data professionals are now known more as analytics professionals who review, analyze, and report on the massive amounts of data stored and maintained by the company. These professionals identify the challenges of Big Data and devise solutions, employ fundamental statistical techniques, improve the quality of data for reporting and analysis, and access, modify, and manipulate the data.

Finally, data analysts collect, clean, and study data sets to turn them into actionable resources to help solve problems or meet goals within the organization. 

If it seems that the three occupations have a significant amount of overlap, that’s because they do! Each business has its own structure and procedures, and you are bound to see some blurring of the distinctions between these positions. Perhaps, in some companies, the data scientist wears multiple hats.

Skills Required to Become a Data Scientist

• Education: 88 percent have master’s degrees, and 46 percent have PhDs
• In-depth knowledge of SAS or R. For data science, R is generally preferred.
• Python coding: Python is the most common coding language that is used in data science, along with Java, Perl, and C/C++.
• Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Having some experience in Hive or Pig is also beneficial.
• SQL database/coding: Although NoSQL and Hadoop have become a significant part of data science, it is still preferred if you can write and execute complex queries in SQL.
• Working with unstructured data: It is essential that a data scientist can work with unstructured data, whether on social media, video feeds, or audio.

Skills Required to Become a Big Data Specialist

• Analytical skills: These skills are essential for making sense of data, and determining which data is relevant when creating reports and looking for solutions.
• Creativity: You need to have the ability to create new methods to gather, interpret, and analyze a data strategy. Mathematics and statistical skills: Good, old-fashioned “number crunching” is also necessary, be it in data science, data analytics, or big data.
• Computer science: Computers are the backbone of every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
• Business skills: Big data professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business and its profits.

Skills Required to Become a Data Analyst

• Programming skills: Knowing programming languages,  such as R and Python, are imperative for any data analyst.
• Statistical skills and mathematics: Descriptive and inferential statistics, as well as experimental designs, are required skills for data scientists.
• Machine learning skills
• Data wrangling skills: The ability to map raw data and convert it into another format that enables more convenient consumption of the data
• Communication and data visualization skills
• Data intuition: It is crucial for a professional to be able to think like a data analyst.

Salary Trends

Although they are in the same domain, each of these professionals—data scientists, big data specialists, and data analysts—earn varied salaries.

Data Scientist Salary

According to Glassdoor, the average base salary for a data scientist is over $117,000 per year.

Big Data Specialist Salary

According to Glassdoor, the average base salary for a big data specialist is over $104,000 per year.

Data Analyst Salary

According to Glassdoor, the average base salary for a data analyst is over $69,000 per year.

Of course, these are just averages and will vary based on several factors. Many professionals earn—or have the potential to earn—higher salaries with the right qualifications.  For more details, you can also check out this salary calculator. 

No matter which path you ultimately decide to take, Simplilearn has dozens of data science, big data, and data analytics courses available online. If you’d like to become an expert in data science, data analytics or big data, check out our Data Scientist, Data Analyst, and Data Engineering courses.

With industry-recommended learning paths, exclusive access to experts in the industry, hands-on project experience, and a master’s certificate awarded upon completion, these online courses will give you what you need to excel in these fast-growing fields and become an expert.

ಬಿಗ್ ಡೇಟಾ ಅನಾಲಿಸಿಸ್ ಅಂದ್ರೆ ಏನು?

 Digitization ಅಥವಾ computerisationನ ಬೆಳವಣಿಗೆಯಿಂದ ಜಗತ್ತಿನಲ್ಲಿ ಪ್ರತಿ ಕ್ಷಣಕ್ಕೆ ಎಷ್ಟು data ಉತ್ಪತ್ತಿ ಆಗುತ್ತದೇನುದನ್ನು ಊಹಿಸಲೂ ಅಸಾಧ್ಯ.

ಈ ಡೇಟಾವನ್ನು ಅರ್ಥೈಸಲು, ಅಂದರೆ analyse ಮಾಡುವ ಮೊದಲು ಈ ಉತ್ಪತ್ತಿ ಆಗುತ್ತಿರುವ ಡೇಟಾ ದ ಸೈಜ್ ಹಾಗೂ ಅದನ್ನು handle ಮಾಡಲು ಬೇಕಾಗುವ ಇನ್ಫ್ರಾಸ್ಟ್ರಕ್ಚರ್ ಅನ್ನು ತಿಳಿದುಕೊಳ್ಳುವುದು ಮುಖ್ಯ. ಅದರ ಒಂದು ಉದಾಹರಣೆ ಇದು:

ಇಲ್ಲಿ ಕಾಣುವಂತೆ, ಪ್ರತಿ ಕ್ಷಣ ಡೇಟಾ generate ಆಗುವ ಮೊತ್ತ ಅಗಾಧಕರ. ಇದನ್ನು ಮಾಮೂಲಿ traditional data ಸ್ಟೋರೇಜ್ ಹಾಗೂ ಡೇಟಾ ಪ್ರೊಸೆಸಿಂಗ್ ಇನ್ಫ್ರಾಸ್ಟ್ರಕ್ಚರ್ ಮೂಲಕ manage ಮಾಡುವುದು ಅತೀ ಕಠಿಣ.

ನಿಮಗೆ ಅನಿಸುತ್ತಿದೆಯೇ, Quora Kannada ದಲ್ಲಿ ಪೋಸ್ಟ್ ಆಗುವ ಪ್ರಶ್ನೋತ್ತರಗಳನ್ನು ಕೇವಲ conventional ಡೇಟಾಬೇಸ್ಗಳಾದ Oracle, SQL ಗಳಲ್ಲಿ ಸ್ಟೋರ್ ಮಾಡಬಹುದೆಂದು? ಖಂಡಿತ ಆಗುವುದಿಲ್ಲ.

ಈ ರೀತಿಯ ಬೃಹತ್ ಗಾತ್ರದ ಡೇಟಾವನ್ನು ಬಿಗ್ ಡೇಟಾ ಎಂದು ಕರೆಯುತ್ತಾರೆ.

ಬಿಗ್ ಡೇಟಾದ ವಿಶಿಷ್ಟಗಳು ಹೀಗಿವೆ ಹಾಗೂ ಅವುಗಳನ್ನು Four V ಎಂದು ಕರೆಯುತ್ತಾರೆ:

1. Volume

ಇದು Obvious

2. Velocity

Healthcare ಹಾಗೂ ವಾಹನಗಳ rfid tags ಇಂದ ಕ್ಷಣ ಕ್ಷಣಕ್ಕೂ ಉತ್ಪತ್ತಿಯಾಗುವ ಡೇಟಾ

3. Veracity

ಆ ಡೇಟಾ ದ ಸತ್ಯಾಸತ್ಯತೆ.

4. Variety

ಯಾವ ಥರದ ಡೇಟಾ ಜನರೇಟ್ ಆಗ್ತಿದೆ?

---

ಸಧ್ಯಕ್ಕೆ ಹಲವಾರು ಫ್ರೇಂವರ್ಕ್ಸ್ ಬಳಕೆಯಲ್ಲಿದೆ ಬಿಗ್ ಡೇಟಾವನ್ನು analyse ಮಾಡಲು. ಅವೆಲ್ಲದರಳಲಿ ಅತಿ ಖ್ಯಾತಿ ಹೊಂದಿರುವುದು Hadoop.

Best Data Backup Free Software For Windows

These days keeping backup of the data is really important as the data consist of very important things related to our life. In case the data is deleted due to any reason then it can’t be recovered unless you’ve kept a backup. For your Windows PC or laptop there are lots of software available which can keep the backup of all your data. These software keep the data safe and secure and in situations like you’ve lost your data then these software allow restoring that data back again.

There are lots of software available which you can try out but when you look for best ones then I’m sure that you’ll get links to paid software. If you don’t want to pay for the backup software then I’m here with list of best data backup free software for Windows.

GFI Backup

This free backup software is the best one and you’ll find all the require tools right there within the UI. You can take the backup to the FTP or external drive or on your PC’s hard drive. The Backups can be scheduled easily and the UI is very simple and impressive.

EASEUS Todo Backup

Another best free backup software for Windows as it allows lots of options related to backup. The software also provides tool for disk partition.
The UI is good and it supports the 64bit architecture as well. It has a good feature that it creates backup instantly in case of any disaster and restore it quick.

Cobian Backup

This best backup free software for Windows allows backing up of data to FTP, external drive or to the network drive. This software also allows encryption of the data you did backup. The UI is simple but not impressive to me. Still you can try out as it got good features.

Backup Maker

This software has two modes, standard and expert mode. If you’re a new user and don’t know anything about what backup is all about then try the standard mode and if you’re an expert then try the expert mode.
The UI is extremely simple and it has got lots of features that are must to have in any backup software. In short, you’ll feel like you’ve purchased this software.

Comodo Backup

This software is very much popular due to its simple UI and better features than all other backup softwares. It allows FTP backup, external network or drive backup and it also provides e-mail notifications. You can create unlimited size of backup with this software as it doesn’t have any limitation.

DeltaCopy

Free software developed under open source license to let users create useful backup of their valuable data. You can keep the same data on multiple sources using it and also schedule backups.

Protect your PC from data theft by USB or CD, with “URC Access Modes”

If you are a business, public venue, or even a normal user concerned with the illegitimate copying or transfer of files or data from your computer(s) via USB or CD, then this program is for you.

‘URC Access Modes’ is a FREE program that will allow you to shut off and password protect USB and CD access on any machine that you have administrator access to.

If you would like your PC to be able to read from USB drives but not write onto them, “URC Access Modes” can do that by setting USB access to read only. It can also switch off RegEdit, preventing any user from going into and editing the registry illegitimately.

How to use:

Usage is simplicity itself. Install the the program and run it as administrator. The options in the main screen (pictured above) are all self explanatory. One thing to keep in mind: remember your password. If you forget it, the only way to recover it may be for you to contact the developer of the program and plead your case.

The verdict:

The program works really well and does exactly what is promised. We booted into safe mode and found that the settings still hold, and the interface is very straightforward.

But the program can be improved. I sent this to someone in a business who really does need to protect access to their computers, and got this feedback: “I think it is a Windows feature for which they made a friendly interface. What would be nice if they had a master password and then separate passwords for the other functions that are either time limited and/or can be changed so you can give someone the flash read-only password for short term access and after that you won’t have to change it again and remember a new password.”

I’m keeping my fingers crossed that they will keep on developing it and taking it further. As it is though, a very nice program if you need it.

Did you test this program? Do you know of other programs that provide this function? Let us know in the comments section.

This program is more powerful than I originally thought. In an interchange with the developer of this program. He pointed out that most other USB-restricting programs (like this one) can be worked around simply by manually re-enabling USB write access via a registry tweak (or using some 3rd party software). It would require that a user be sophisticated enough to do so, but it is possible. Not so with ‘URC Access Modes’, which will not accept any such workaround, and which can only be reversed by using your password (or re installing Windows).
URC Access Modes is now in version 2

It now includes a larger set of tools: USB Tool  &  CD/DVD Tool, Registry(regedit.exe) Tool, Command Prompt(cmd.exe) Tool, Group Policy Tool(gpedit.msc)  and Task Manager Tool(taskmgr.exe).

•  USB & CD Tools: can disable USB Mass Storage Devices such as Pen Drives , Hard Disk etc, without disabling peripheral devices such as your USB Mouse, Keyboard, Webcam etc. Protects from data theft and virusus.
• Registry & Command Prompt Tools: protect from unauthorized code execution via disabling the command prompt and registry. Also the URC ACCESS MODES password is stored in the registry hence disabling it will protect the program from being circumvented.
• Group Policy & Task Manager Tools: many important system settings are protected since many of these are in the Group Policy. Also important processes cannot be shut down from the task manger.