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Industry Basics

Data Management vs Data Governance

Data management and data governance are two interrelated but distinct concepts in the field of data handling and processing.  Understanding their differences is essential for organizations seeking to optimize their data practices and maintain the quality, security, and usability of their data assets.

 

Data Management is focused on the technical and operational aspects of handling data, while Data Governance deals with the strategic policies and guidelines that ensure the proper usage and maintenance of an organization's data.  Both concepts are crucial for organizations to derive value from their data, maintain data quality and security, and make informed decisions based on reliable and accurate data.  By implementing robust Data Management and Data Governance practices, organizations can unlock the full potential of their data assets, leading to better decision-making, increased operational efficiency, and improved competitive advantage.

 

Data Management

Data Management refers to the processes, practices, and technologies used to collect, store, process, and maintain an organization's data.  It covers the entire lifecycle of data, from creation to deletion, and includes aspects such as data architecture, data modeling, data warehousing, data integration, data quality, and data security.

 

  • According to a study by IBM, poor Data Management costs businesses around $3.1 trillion per year in the United States alone.

  • The Data Warehouse Institute estimates that poor data quality costs US businesses approximately $600 billion per year.

 

Data Governance

Data Governance, on the other hand, focuses on the policies, processes, and frameworks that govern how an organization's data is accessed, utilized, and maintained.  It involves establishing rules and responsibilities to ensure data quality, accuracy, security, and compliance with applicable laws and regulations.  Data Governance helps organizations make better decisions by ensuring their data is trustworthy.

 

  • A survey by the Data Governance Institute found that 62% of organizations see data governance as a critical or high priority.

  • According to a study by Experian, organizations that invest in Data Governance initiatives see an average improvement of 40% in data quality and a 33% increase in the speed of decision-making.

How did data archiving first evolve from data storage?

The evolution of data archiving from data storage can be traced back to the need for organizations to store large volumes of data for long periods while ensuring data accessibility, security, and regulatory compliance.  Data storage solutions were initially designed to provide secure and reliable storage of data, but they were not optimized for long-term data retention or regulatory compliance.

 

The evolution of data archiving can be traced to the advent of tape storage technology in the 1950s.  Tape storage technology provided a cost-effective way to store large volumes of data for extended periods.  However, the accessibility and retrieval of data from tape storage were slow and cumbersome, making it unsuitable for applications requiring frequent access to data.

 

As the volume of data being generated increased, organizations needed a more efficient and scalable way to store data for long periods.  The evolution of data archiving was driven by the need to develop storage solutions that could provide long-term data retention, rapid retrieval of data, and regulatory compliance.

 

The emergence of disk-based storage solutions in the 1990s marked a significant shift in the evolution of data archiving.  Disk-based storage solutions provided a more accessible and faster way to store data, making it easier for organizations to retrieve and access archived data.  The development of specialized software solutions that enabled organizations to manage and access archived data further advanced the evolution of data archiving.

 

Today, data archiving solutions are designed to provide long-term data retention, rapid retrieval, regulatory compliance, and data integrity.  These solutions typically involve a combination of disk-based storage, specialized software, and backup and recovery technologies. The evolution of data archiving from data storage has enabled organizations to manage their data effectively while ensuring its accessibility, security, and compliance with regulatory requirements.

The Different Types of Data

Structured, semi-structured, unstructured, and dark data differ in their level of organization and accessibility.  Structured data is highly organized and searchable, while semi-structured data is partially organized and partially unstructured.  Unstructured data lacks any identifiable structure or format, and dark data is collected but not utilized for any analytical or operational purposes.

 

Structured Data
Structured data is highly organized and easily searchable, and it is typically stored in a database or spreadsheet.  Structured data is characterized by a predefined format, such as rows and columns, and a well defined schema.  Examples of structured data include customer information, transaction records, and inventory data.
 

Semi-Structured Data
Semi-structured data is partially organized and partially unstructured.  It may have some identifiable structure or tags, but it lacks a formal schema.  Examples of semi-structured data include emails, web pages, and social media posts.
 

Unstructured Data
Unstructured data is data that has no identifiable structure or format, and it can be difficult to analyze or organize.  Examples of unstructured data include text documents, images, audio and video files, and social media comments.
 

Dark Data
Dark data refers to data that is collected, processed, and stored by organizations but is not used for any analytical or operational purposes.  Dark data can be structured, semi-structured, or unstructured, and it can be generated by various sources such as customer interactions, sensor data, or employee communications.  Dark data is typically left unanalyzed because it is difficult to access, process, or analyze or because it is deemed irrelevant or redundant.

F.H.I.R. + ZNZ Data Processing

FHIR is an acronym that stands for "Fast Healthcare Interoperability Resources."  FHIR is a set of standards and specifications developed by the healthcare industry to facilitate the exchange and interoperability of electronic health information.

 

FHIR is based on the widely adopted HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources) standard.  FHIR is designed to enable the exchange of healthcare data in a standardized format, making it easier for different healthcare systems and applications to share information seamlessly.

 

The goal of FHIR is to promote interoperability, meaning the ability of different healthcare systems, devices, and applications to communicate and exchange data effectively.  By using standard data formats and application programming interfaces (APIs), FHIR enables healthcare providers, researchers, and other stakeholders to access and share patient information securely and efficiently.

 

FHIR provides a framework for representing and exchanging healthcare data elements, such as patient demographics, clinical observations, medications, and other relevant information.  It defines a set of resources and data types that can be used to describe different aspects of healthcare information.  These resources can be accessed and manipulated through RESTful web services, allowing developers to build applications that interact with healthcare data using standard HTTP protocols.

 

Overall, FHIR plays a crucial role in advancing healthcare interoperability and facilitating the exchange of electronic health information among different systems, contributing to improved patient care, research, and innovation in the healthcare industry.

 

ZNZ Data Processing is an necessary and evolutionary step in the lifecycle of data.  There is no hope for FHIR without actionable data.  

Why is data archiving so much more expensive than data storage?

Data archiving is often more expensive than data storage because it involves additional features and services beyond the basic storage of data.  While data storage simply involves storing data in a secure location, data archiving involves storing data for long-term retention while ensuring its accessibility, security, and regulatory compliance.  

 

The following are some of the reasons why data archiving is more expensive than data storage:

 

Longer Retention Periods:  Data archiving requires the storage of data for longer periods compared to data storage. This means that data archiving solutions must provide storage systems that can maintain data integrity and accessibility over extended periods, which involves more advanced technologies and more robust storage infrastructure.

 

Compliance Requirements:  Data archiving must comply with regulatory requirements related to data retention, which means that archiving solutions must provide features such as data encryption, authentication, and audit trails. These features require additional investments in infrastructure, security, and compliance.

 

Accessibility:  Archived data must be accessible for retrieval and analysis when needed, which requires the implementation of specialized data retrieval technologies and services that are more expensive than basic storage.

 

Preservation of Data Integrity:  Data archiving solutions must ensure the preservation of data integrity by avoiding data corruption and data loss over the long term. This requires the use of specialized storage media and advanced data backup and recovery technologies that add to the cost of the archiving solution.

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