What is Data processing

 

Data processing

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output.

Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

Six stages of data processing

1. Data collection

Collecting data is the first step in data processing. Data is pulled from available sources, including data lakes and data warehoses. It is important that the data sources available are trustworthy and well-built so the data collected (and later used as information) is of the highest possible quality.

2. Data preparation

Once the data is collected, it then enters the data prepration stage. Data preparation, often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing. During preparation, raw data is diligently checked for any errors. The purpose of this step is to eliminate bad data (redundant, incomplete, or incorrect data) and begin to create high-quality data for the best business intelligence.

3. Data input

The clean data is then entered into its destination (perhaps a CRM like Salesforce or a data warehouse like redshift), and translated into a language that it can understand. Data input is the first stage in which raw data begins to take the form of usable information.

4. Processing

During this stage, the data inputted to the computer in the previous stage is actually processed for interpretation. Processing is done using machine learning algorithms, though the process itself may vary slightly depending on the source of data being processed (data lakes, social networks, connected devices etc.) and its intended use (examining advertising patterns, medical diagnosis from connected devices, determining customer needs, etc.).

5. Data output/interpretation

The output/interpretation stage is the stage at which data is finally usable to non-data scientists. It is translated, readable, and often in the form of graphs, videos, images, plain text, etc.). Members of the company or institution can now begin to self-serve the data  for their own data analytics projects.

6. Data storage

The final stage of data processing is storage . After all of the data is processed, it is then stored for future use. While some information may be put to use immediately, much of it will serve a purpose later on. Plus, properly stored data is a necessity for compliance with data protection legislation like GDPR. When data is properly stored, it can be quickly and easily accessed by members of the organization when needed.

Data processing can be defined by the following steps

Data capture, or data collection,

Data storage,

Data conversion (changing to a usable or uniform format),

Data cleaning and error removal,

Data validation (checking the conversion and cleaning),

Data separation and sorting (drawing patterns, relationships, and creating subsets),

Data summarization and aggregation (combining subsets in different groupings for more information),

Data presentation and reporting.

There are different types of data processing techniques, depending on what the data is needed for. Types of data processing at a bench level may include:

Statistical,

Algebraical,

Mapping and plotting,

Forest and tree method,

Machine learning,

Linear models,

Non-linear models,

Relational processing, and

Non-relational processing.

These are methodology and techniques which can be applied within the key types of data processing.

What we’re going to discuss in this article is the five main hierarchical types of data processing. Or, in other words, the overarching types of systems in data analytics.

Data Processing by Application Type

The first two key types of data processing I’m going to talk about are scientific data processing and commercial data processing.

1. Scientific Data Processing

When used in scientific study or research and development work, data sets can require quite different methods than commercial data processing.

Scientific data is a special type of data processing that is used in academic and research fields.

It’s vitally important for scientific data that there are no significant errors that contribute to wrongful conclusions. Because of this, the cleaning and validating steps can take a considerably larger amount of time than for commercial data processing.

Scientific data processing needs to draw conclusions, so the steps of sorting and summarization often need to be performed very carefully, using a wide variety of processing tools to ensure no selection biases or wrong relationships are produced.

Scientific data processing often needs a topic expert additional to a data expert to work with quantities.

2. Commercial Data Processing

Commercial data processing has multiple uses, and may not necessarily require complex sorting. It was first used widely in the field of marketing, for customer relationship management applications, and in banking, billing, and payroll functions.

Most of the data caught in these applications is standardized and somewhat error proofed. That is capture fields eliminate errors, so in some cases, raw data can be processed directly, or with minimum and largely automated error checking.

Commercial data processing usually applies standard relational databases and uses batch processing. However, some, in particular, technology applications may use non-relational databases.

There are still many applications within commercial data processing that lean towards a scientific approach, such as predictive market research. These may be considered a hybrid of the two methods.

Data Processing Types by Processing Method

Within the main areas of scientific and commercial processing, different methods are used for applying the processing steps to data. The three main types of data processing we’re going to discuss are automatic/manual, batch, and real-time data processing.

3. 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