Database Administration – Different Approaches to Data Processing

Data processing is a crucial business operation now, and there are various methods of doing the same. This article will discuss different types of data processing methods that cover both scientific and commercial data processing. Fundamentally, data processing is the process of converting the given raw data into meaningful information for analytical and decision-making purposes.

Steps involved in the data processing

1. Data collection or data capture
2. Storage of data
3. Conversion of data by changing it to uniform, usable format
4. Cleaning of data to remove any potential errors
5. Validation of data by checking the conversions and cleanliness.
6. Separation of data and sorting based on patterns, relationships, and subset creation.
7. Summarization and aggregation of data by comparing the subsets in various groupings.
8. Presentation and reporting of data.

There are various types of processing techniques for scientific and enterprise data. Depending on the mode of processing, we can easily classify them into the below.

• Statistical data processing
• Algebraical data processing
• Plotting or mapping
• Forest and tree method of data processing
• Machine learning
• Linear and nonlinear models
• Relational data processing and,
• Non-relational data processing.

All these methodologies and techniques can be applied with various types of data processing approaches. We may further discuss some major hierarchical kinds of data processing, and in other words, these are the overarching types of systems in data analytics.

Scientific data processing

When data is used for research, scientific studies, or developmental works, the datasets may require a different method than enterprise data processing.Scientific processing is a particular type of data processing approach used in research and academic fields. Scientific data need to ensure that there are no significant errors that may contribute to wrongful conclusions.

Because of this, in scientific data processing, cleaning and validation steps may take a considerably higher amount of time than commercial data processing. Data processing scientifically also needs to conclude, so steps involved in summarizing and sorting it also may require careful consideration. It may use various processing tools to ensure that no selection bias or relationship errors are made. For identifying the most appropriate data processing model for your enterprise database, you may consult with a reliable remote administration services provider like RemoteDBA.com. 

Commercial data processing

This is a widely used approach to data Processing that has various uses. In the case of commercial data processing, it may not necessarily require any complex sorting of data. Initially, this approach was used widely in business marketing, customer relationship management, banking, payroll functions, etc. Most of the data captured for commercial processing applications is standardized and largely error-free.The standardized field may also help eliminate the errors, but raw data can also be processed directly with automation error checking in some cases.

Commercial processing of data may usually apply to relational databases and use batch processing of data. However, in some cases,technology applications may also use non-relational databases for commercial processing. There are many applications in commercial data Processing, which focus on scientific approaches like predictive market research. Most of the data processing methods now use a fine combination or a hybrid model of scientific and commercial data processing methods.

Manual vs. automatic data processing

It is not very common now, but still, people tend to use manual data processing methods. The conventional mode of data processing based on bookkeeping functions can be performed with the use of a ledger, and customer service data can be collected manually and processed. Even the spreadsheet-baseddata can be considered manual to some extent. Some of the most difficult parts of data processing 

The manual components may also be needed for intuitive reasoning.Technologies that have led to the development of automated data processing systems were initially like punch cards for attendance marking etc. Computers were started to be used by the corporates in the 1970swhen electronic data processing had evolved. Some of the early applications in automated data processing by way of specialized databases were developed for CRM to drive better results. Then electronic data processing became widespread with the introduction of personal computers in 1980. Further spreadsheets offered a very simple electronic assistance for everyday data management like personal budgeting and expenses etc.

Database management

Database management offered better automation of data processing functions. Users must manipulate all the data in databases and extract the data relationships and reports relatively from the databases. Autonomous databases are now seemingly the future data processing methods, especially in the commercial data processing. The development in this field of automated data processing and machine learning, and artificial intelligence, may also optimize the improvement in services in various sectors. Without any specialized data professionals in-house, enterprises can now derive actionable insights from their in-house databases through autonomous database management systems.

Batch processing

To save the computational time largely before distributing distributed data architectures, standalone systems applied batch processing methods.This was particularly useful in many applications related to financial processing or applications where the data required an additional security layer like healthcare records. Batch processing can help complete a range of data processes in batches by simplifying it with a single command to execute actions on multiple datasets. This is more like a comparison of the spreadsheet to a calculator. Calculations can be applied with one function for the whole column or series of columns giving multiple results from a single action. Batch processing can also complete a queue of tasks instantly without any human intervention. The data systems may program the priorities to certain functions or set times when that batch processing is completed.

As of late, for the commercial data processing needs, many large processing applications adopt real-time processing. With this, you get the results and insights from the data exactly as it streams in. You can see the most practical application of real-time data processing in the stock market and currency trends. Stream data processing is also a very common approach used in advance real-time processing of data.

Other modes of online processing, data visualization, multiprocessing, time-sharing, etc., are also widely used for effective data processing.