This technical article is revised by one of the leading data handling firms – MiniTool. It explains several commonly used data handling-related terms such as product data management, data handling services, big data management, data risk management, data center infrastructure management, etc.

What Is Data Handling?

Data Handling encompasses all fields related to managing data as a valuable asset. Emerging in the 1980s, data handling refers to the technology transitioning from sequential processing (initially punch cards, followed by magnetic tape) to random access storage.

Today, it's feasible to store individual facts and quickly retrieve them using random access disk technology. Consequently, many people believe that data handling is more significant than business process management. However, since software programs have evolved into interactive and real-time use, it's evident that both management processes are essential. If the process is not clearly defined, it becomes impossible to meet user needs. Similarly, if the data isn't well-defined, it may be misused in applications.

Topics Related to Data Handling

1. Data architecture

• Data architecture
• Data flow

2. Database and storage management

• Data maintenance
• Database administration
• Database management system
• Business continuity planning
• Data sub-setting

3. Data security

• Data access
• Data erasure
• Data privacy
• Data security

4. Reference and master data

• Master data management (MDM)
• Reference data

5. Data integration and interoperability

• Data integration
• Data interoperability
• Data movement (extract, transform, load)

6. Documents and content

• Document management system
• Records management

7. Data quality

• Data discovery
• Data cleansing
• Data integrity
• Data enrichment
• Data quality
• Data quality assurance
• Secondary data

Evolution of Data Handling

In contemporary management terminology, the term "data" is progressively being replaced by "knowledge" or "information" in non-technical contexts. As a result, data handling has transformed into knowledge handling or information handling.

This shift obscures the raw data processing and makes interpretation implicit. The difference between data and derived value is illustrated by the information ladder. Nonetheless, data has regained its prominence with the advent of big data, which involves the collection and analysis of vast datasets. Several organizations have established data handling centers (DHC) for big data handling.

Some Common Terms Regarding Data Handling

1. Master Data Handling

Master data handling (MDH) is a technology-supported discipline where business and information technology collaborate to ensure the uniformity, consistency, accuracy, semantics, stewardship, and accountability of the enterprise’s official shared master data assets.

2. Data Handling Platform

A data handling platform (DHP) is a software platform utilized to gather and manage data. It enables businesses to identify audience segments, which can be applied to target specific users and contexts in online advertising campaigns.

3. Enterprise Data Handling

Enterprise data handling (EDH) is the capability of an organization to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication. It focuses on creating accurate, consistent, and transparent content.

4. Data Lifecycle Handling

Data lifecycle handling (DLH) is a policy-driven framework or model through which data flows to optimize its useful life. It is represented by a sequence of steps or stages that typically start with data collection or creation and conclude with data destruction or recycling.

The three primary objectives of data lifecycle handling are data security and confidentiality, continuous availability, and long-term structural integrity. The six phases of the lifecycle of data are data capture, data maintenance or storage, data usage, data publication or release, data archiving, and data cleaning.

5. Data Handling Software

Data handling software (DHS) is a type of tool to assist users in collecting, storing, editing, transferring, copying, as well as deleting or removing data. Generally, a single data handling program cannot perform all the mentioned operations; most data handling services can accomplish one or more tasks. For instance, MiniTool ShadowMaker is one of the data handling tools that primarily creates a backup of data to ensure data security. This is also beneficial for data risk handling and data security handling.

6. Data Center Handling

Datacenter handling is the set of tasks carried out by those responsible for managing the ongoing operation of a data center. This includes business service management and planning for the future.

Historically, data center handling was considered something performed by employees with the aid of tools collectively referred to as data center infrastructure handling (DCIH) tools.

7. Customer Data Handling

Customer data handling (CDH) pertains to how businesses keep track of their customer information and survey their customer base to obtain feedback. CDH encompasses a variety of software or cloud computing applications designed to provide large organizations with rapid and efficient access to customer data.

8. Product Data Handling

Product data handling (PDH), also known as product information handling (PIH), is the business aspect typically included in product lifecycle management. It is responsible for the management and publication of product data. In software engineering, PDH is known as version control.

9. Data Handling Plan

A data handling plan (DHP) can refer to a formal document outlining how data are managed during a research project and after the project is completed. The aim of DHP is to consider various aspects of data handling, metadata generation, data preservation, as well as analysis before the project begins.

10. Data Handling Strategy

A mobile data handling strategy, for example, is a structure imposed on a complex data model that is navigated by a user on a mobile device. It is a relatively new process that arose from the popularity of mobile apps requiring a flexible and in-depth navigation structure.

11. Integrated Data Handling

Integrated data handling (IDH) is a process to facilitate data handling and enhance performance. IDH comprises an integrated, modular environment to manage enterprise program data and optimize data-driven apps over its lifetime. Below are the purposes of IDH.

• Improve data access, speed iterative testing.
• Simplify app upgrades, consolidation, and retirement.
• Produce enterprise-ready software faster.
• Simplify and automate processes.
• Offer contextual intelligence across the solution stack.
• Accommodate new initiatives without expanding infrastructure.
• Facilitate alignment, consistency, and governance.
• Empower collaboration between architects, developers, and database administrators (DBAs).
• Achieve service level targets consistently.
• Support business growth.
• Pre-define business standards and policies; extend, share, and apply throughout the lifecycle.

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