WHAT IS DATA PROCESSING?

Data processing, is the Manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data can be included under data processing. In the commercial world, data processing refers to the processing of data required to run organizations and businesses.

TYPES OF DATA PROCESSING

1.Time Sharing

This kind of Data processing is entirely based on time. In this, one unit of processing data is used by several users. Each user is allocated with the set timings on which they need to work on the same CPU/processing Unit.

Intervals are divided into segments, and thus to users, so there is no collapse of timings which makes it a multi-access system. This processing technique is also widely used and mostly entertained in startups.

2.Multi Processing

This is the most commonly used data processing technique. However, it is used all over the globe where we have computer-based setups for Data capture and processing.

As the name suggests – Multiprocessing is not bound to one single CPU but has a collection of several CPUs. As the various set of processing devices are included in this method, therefore the outcome efficiency is very useful.

The tasks are broken into frames and then sent to the multiprocessors for processing. The result obtained is expected to be in less time and the output is increased. The additional benefit is every processing unit is independent, thus failure of any will not impact the working of other processing units.

3.Online Processing

This data processing technique is derived from Automatic data processing. This technique is now known as immediate or irregular access handling. Under this technique, the activity by the framework is prepared at the time of operation/processing. It can be viewed easily with the continuous preparation of data sets. This processing method highlights the fast contribution of the exchange of data and connects directly with the databases.

4.Data Virtualization

Data virtualization techniques are another important development in real-time data processing, where the data remains in its source form, the only information is pulled for the needs of data processing. The beauty of data virtualization is where transformation is not necessary, it is not done, so the error margin is reduced.

Data virtualization and stream processing mean that data analytics can be drawn in real-time much quicker, benefiting many technical and financial applications, reducing processing times and errors.

Other than these popular Data processing Techniques there are three more processing techniques which are mentioned below-

5.Batch Processing

To save computational time, before the widespread use of distributed systems architecture, or even after it, stand-alone computer systems apply batch processing techniques. This is particularly useful in financial applications or where data requires additional layers of security, such as medical records.

Batch processing completes a range of data processes as a batch by simplifying single commands to provide actions to multiple data sets. This is a little like the comparison of a computer spreadsheet to a calculator in some ways. A calculation can be applied with one function, that is one step, to a whole column or series of columns, giving multiple results from one action. The same concept is achieved in batch processing for data. A series of actions or results can be achieved by applying a function to a whole series of data. In this way, computer processing time is far less.

Batch processing can complete a queue of tasks without human intervention, and data systems may program priorities to certain functions or set times when batch processing can be completed.

Banks typically use this process to execute transactions after the close of business, where computers are no longer involved in data capture and can be dedicated to processing functions.

Methods of data processing in brief

• Manual Data Processing – Manual data procession is totally depended upon human being’s brain power and efforts. Not a single or simplest device is used to calculate data. Every calculation is done by the staff and there is a big chance of errors. When you are running a complete setup, you need to switch yourself among different jobs. This increases the chances of error. Manual data processing is slow and there is a higher chance or errors and omissions.
• Mechanical Data Processing – Secondly, there is another kind of data processing where all of the calculation is done by calculator. This type is called mechanical data processing. Though manual and mechanical data processing are obsoleted now a day, there are a few small organizations which cannot afford to have a complete computerized setup. This requires them to use mechanical data processing.
• Electronic Data Processing – The third, modern and most common form of data processing is called electronic data processing. In this all the data is processed and calculated through computers and there are negligible chance of errors and mistakes. There are various applications which are designed to process data and this technique is the fastest of all three kinds. Electronic data processing methods makes it more organized, accurate, eases data analysis and is less time-consuming as well.

Automatic versus Manual Data Processing

It may not seem possible, but even today people still use manual data processing. Bookkeeping data processing functions can be performed from a ledger, customer surveys may be manually collected and processed, and even spreadsheet-based data processing is now considered somewhat manual. In some of the more difficult parts of data processing, a manual component may be needed for intuitive reasoning.

The first technology that led to the development of automated systems in data processing was punch cards used in census counting. Punch cards were also used in the early days of payroll data processing.

The Rise of Computers for Data Processing

Computers started being used by corporations in the 1970s when electronic data processing began to develop. Some of the first applications for automated data processing in the way of specialized databases were developed for customer relationship management (CRM) to drive better sales.

Electronic data management became widespread with the introduction of the personal computer in the 1980s. Spreadsheets provided simple electronic assistance for even everyday data management functions such as personal budgeting and expense allocations.

CHALLENGES OF DATA PROCESSING

Breach of Privacy and Security Is a Big Issue for Stakeholders

A breach of privacy is the loss of, or unauthentic access or disclosure of,  confidential personal data. A few common threats to privacy occur when personal information gets stolen, mistakenly shared, or lost.

Breach of privacy can also occur due to operational breakdowns and business failures. Breach of privacy occurs for stakeholders when business ventures employ weak and flawed security measures. Even though the hackers are legally liable for the acts of the breach, still, prevention of the security breach is the most crucial responsibility of the data processing system and organization.

To combat the breach of privacy risk in data processing, the organization needs to invest in high-quality antivirus and antimalware software and use the strictest methods of encryption to ensure a secure connection throughout the entire cycle of data processing from collection to storage.

2. The Anonymity of Every Stakeholder Is Almost Impossible

Data anonymization is a kind of data sanitization that ensures the protection of the privacy of data in the cycle of data processing. It involves the process of encryption of data or removal of personally identifiable data from public datasets so that the users to whom the data attributes can remain securely anonymous.

Re-identification of stakeholders with the employment of anonymous data in public datasets is a possibility now. However, with a computer and internet connection, re-identification has become quite easy and personal information is no longer confidential. This re-identification might not be simple. However, private presence and identity theft on the internet can be prevented with more stringent encryption and security measures.

3. Analytics Is Not Completely Accurate

Analysis of data processing is not entirely accurate and this is a big problem for stakeholders.

In the data processing cycle, there is a colossal volume of data involved, and as such, a huge amount of error creeps into the analyzed information with only a marginal flaw that might occur in the processing stage of data. This becomes a huge issue for stakeholders.

What needs to be done by organizations is to work on 100% accuracy of data processing and use trusted tools and techniques of analytics that will guarantee maximized accuracy.

4. Problems of Stakeholders with E-Discovery

E-discovery is the search of digital data to be used as evidence in legal proceedings in court. Even the government and the court can order for e-discovery in the form of ethical hacking to facilitate the search for critical evidence.

However, owing to the fact that data processing involves a huge amount of data in databases, e-discovery is extremely difficult and so is in compliance with legal requirements and restrictions. Besides, e-discovery is highly expensive. This poses to be a grave issue for stakeholders. However, organizations can negotiate the costs of e-discovery and eliminate the issue encountered by stakeholders for the same.

5. Shortage of Talent for Data Processing

Business organizations often lack talent, and as such, stakeholders face grave issues in data processing and data analytics.

Data analytics and data processing is a complex field of computations and becomes even more intricate when deep learning, machine learning, and other components of artificial intelligence are employed to process and analyze the data.

The intricateness puts a colossal and growing demand for data scientists who are skilled in a wide range of fields owing to the fact that the job of data processing and analytics are heavily multi-disciplinary. This demand will continue to grow with increasing avenues of data processing using advanced analytics and artificially intelligent methods.

As a result, it will become a serious issue for stakeholders as organizations find it extremely difficult to look for and recruit data scientists and thus face a grave loss in the data processing sector.

6. Restricted Technical Capacities for Data Processing

Despite the sky-high technological advancement in the development of better and faster data processors for the enhancement of computational abilities, technical capacities are being constantly challenged by the surging demand for even faster data processors that will be able to process huge volumes of data.

This has become an issue for stakeholders as technological development takes a toll on the organization’s wallet. For startup ventures and small and medium scale businesses, technological development is synonymous with large and unaffordable sums of financial capital.

On the other hand, AI algorithms are complex and require thousands of complicated calculations and more powerful processors for the implementation of data processing using AI-driven analytics and methods.

This is a serious challenge that stakeholders encounter as now businesses find it difficult to secure data from hundreds of non-relational databases that are continuously transforming.

7. Interpretation Might Become an Issue of Ethics for Stakeholders

Interpretation of information after data processing might become an issue of ethics for stakeholders.

The foremost reason behind data processing and analytics is to reach a decision on the basis of the information obtained after the data is processed. However, it is anything but prudent to rely on digital data alone and not concern oneself with the impact that the decision might have on the people and the environment.

This poses to be a significant ethical issue for stakeholders who prefer to be concerned with data processing alone. It is important for everyone to be aware of the fact that data processing and analytics are done to be of help to everyone. The decision-making needs to be based on how they will affect the ones involved from the grass-root level to the top and not solely on numbers and figures in a spreadsheet.

8. Organizational Resistance to Data Processing

Data processing methods are advanced and require technical as well as cultural transformations to which most organizations tend to resist.

Organizational resistance is a major impediment for stakeholders. In a survey conducted by New Vantage Partners on a large number of corporate firms, the statistics revealed that about 86 percent of the firms were dedicated to the creation of a data-driven and intelligent culture, but only 37 percent of the firms had actually been successful.

This huge disparity between what the companies are driving at and the outcomes attributed to four different reasons:

• Insufficiency in organizational alignment
• Shortage in the adoption of mid-level management and lack of understanding
• Lack of human capital and financial resources
• Organizational resistance

To enable organizations to capitalize on the myriads of opportunities offered by the subjectively huge volumes of data to data processing and analytics, organizations need to bring about a cultural shift and do everything differently.

Such changes are tremendously painstaking for large-scale organizations to implement thus making organizational resistance a major impediment for stakeholders. The report suggests that to bring about any improvement in the decision making capability of the company, the organizational HRs needs to recruit and invest in human capital that understands the opportunities and challenges and knows how to act on them.

9. Integration of the Data for Data Processing

The colossal volumes of data secured for data processing are obtained from a huge number of data sources. Integration of the disparate sources of data proves to be a challenge for the stakeholders of an organization.

The large volume of data is secured from enterprise applications, email systems, social media streams, documentation created by employees of the organization, and so on. Combination of all the data and reconciliation of the same to build reports can be tremendously difficult.

Even though there is a variety of tools and techniques available in the digital world for the integration of a huge amount of data, several organizations claim that they are yet to solve the problems of data integration.

However, the organizations are resorting to new and advanced technological solutions for the combination of data. As per the statistics of the survey conducted by the IDG, about 89 percent of the firms surveyed have disclosed that they are planning to purchase integration technologies next to data processing and analytics software.

10. Timely Generation of Insights is Almost an Impossibility

Organizations aren’t necessarily seeking to store the data after data processing and analytics. It is the goal of every organization to analyze the data and make a powerful decision on the basis of the data. In accordance with the statistics revealed by the survey conducted by NewVantage Partners, the most primary objectives of organizations include:

• Decreasing the organizational expense through the introduction of cost-effective operations
• Establishment of a new organizational culture powered by data and decision-making
• Creation of novel opportunities for innovation in technology and disruption
• Acceleration of the speed of deployment of services
• Introduction of new products and services

All of these objectives can augment any organization but that depends on one and only condition, and that is a timely extraction of information after data processing and analysis of big data and then acting on that information to reach sound decisions.

To achieve speed in information extraction and decision-making, organizations are seeking a new generation of ETL, technologies and analytics tools, and methods to bring about a dramatic reduction in the time taken in the generation of reports. Stakeholders of different companies are investing in software and technologies with the capabilities of real-time analytics that will enable them to respond to fluctuating market trends instantaneously.

As a bottom line, the challenges of data processing are many, but every day, a new solution is getting added to the immense world of data and information. All that it takes for stakeholders is to invest in resourceful and rich human capital and then drive the organization to success.

IMPORTANCE OF DATA PROCESSING

Importance of data processing includes increased productivity and profits, better decisions, more accurate and reliable. Further cost reduction, ease in storage, distributing and report making followed by better analysis and presentation are other advantages. The need to process data is now widely realized and reflected in every field of work. Let the work be done in a business atmosphere or for educational research purpose, data management systems are used by every business. It is a multidimensional process which is involved in almost every field of human life. Generally speaking, the term “Data processing” is used where you have to collect innumerable data files from different sources. You have to arrange them in a way that can be practically beneficial for the purpose you have gathered all that material. It is a task of synchronizing collected data from different sources and convert it to an organized form . This makes it easy to understand and retrieve the specific information anytime. There are various data processing methods which include manual data processing, mechanical data processing and electronic data processing. Data processing is one of the most important daily tasks especially when dealing with big data and performing data mining All those fields where we can expect a huge data available to settle down like education, banking or transportation now realises the importance of data processing. With the emergence of fields like data science, data analysis, big data etc. the need to process data and to understand the importance of processing the data is crucial.

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