Enterprise Data

What is Enterprise Data?

Enterprise Data is digital information that flows through a company's systems, including both structured data (such as spreadsheets and databases) and unstructured data (images, videos, graphics and is shared by the users of an organization, across departments and/or geographic regions.

Enterprise Data can be used to gain insights into operations, network security threats and risks, strategic initiatives such as sales reporting or customer relationship management (CRM), IoT sensor information, or weather trends. It is essential for businesses to understand their enterprise data in order to make informed decisions that will benefit their organization. Data modeling techniques are often used by enterprises when dealing with large amounts of enterprise data in order to make sense of it all more quickly and efficiently.

Enterprise data can have a significant impact on the financial well-being of an organization. Enterprises often implement security measures such as encryption technologies in order to protect their valuable enterprise data from potential threats like cyberattacks or accidental loss due to human error. Enterprise data loss can result in serious consequences such as loss of revenue, customer trust, or even legal action against the organization.

Enterprise Data can be classified into three categories:

  • Structured data (spreadsheets and databases): This type of data is organized in a logical way and is easy to understand, search, and manipulate. It typically takes the form of tables, rows, columns, and datasets. Examples include customer orders or transaction records.
  • Unstructured data (images, videos, graphs, etc.): This type of data is not organized in a logical way but can still be used for analysis purposes if it's properly tagged or labeled with metadata tags that describe its contents in detail. Examples include images or videos captured by security cameras or drones during an aerial survey mission.
  • Application-specific data (GPS tracking info from transportation apps): This type of information originates from specific applications within an enterprise's ecosystem that are designed to meet specific needs such as IoT sensors collecting weather data for use in forecasting operations decisions.

Characteristics of Enterprise Data[1]

A key asset component, enterprise data is subdivided into internal and external data categories, which are classified according to organizational processes, resources, and/or standards.

There is no precise standard for what defines enterprise data from small or medium-sized businesses. However, once an organization gets to the point where it has many operating units in different locations, its needs clearly get more complicated as compared to the one-location business with a single IT department.

Enterprise Data characteristics include:

  • Integration: Ensures a single consistent version of enterprise data for sharing throughout an organization
  • Minimized redundancy, disparity, and errors: As enterprise data is shared by all of an organization's users, data redundancy and disparity must be minimized. Data modeling and management strategies are directed toward these requirements.
  • Quality: To ensure data quality, enterprise data must follow organizational or other identified standards for varying internal and external data components.
  • Scalability: Data must be scalable, flexible, and robust to meet different enterprise requirements.
  • Security: Enterprise data must be secured via authorized and controlled access.

Types of Enterprise Data[2]

To resolve data issues, it is important to understand different types of enterprise data. Its importance lies in the fact that these different types of data serve their specific purpose and there are different approaches to managing them. Another important reason to understand different types of Enterprise Data is that it helps in adopting a modular and agile approach to fix data issues.

  • Master Data: It represents non-transactional information about business objects. It is a consistent and uniform set of identifiers that describe core entities of the business operations. Usually, master data defines unique entities in Products, Customers, Vendors, etc. domains. For example, a specific vendor which has a standardized definition and properties is a master data whose unique ID is used in transactional systems for business activities.
  • Transactional Data: It describes core business activities and transactions. Transactional data may contain information about your procurement, production, selling, etc. activities. The description of a sale of a product is transactional data.
  • Analytical Data: Analytical (or reporting) data is an aggregated compilation of transactional data which can be sliced and diced with the help of master data for business analysis.
  • Reference Data: It is a standardized subset of master data which is less volatile than elements of master data. As the name suggests, it is used as a reference across the organization to maintain common standards. For example, a list of countries and corresponding states and cities is reference data.
  • Metadata: It provides descriptive, structural information about other data related to business activities. For example, the Name of a person may have 3 components First Name (mandatory), Last Name (mandatory), and Middle Name (optional). This information is metadata about the name of a person.

Model Enterprise Data[3]

This process creates the Enterprise Data Model (EDM), which is a conceptual or semantic data model for business use. Its objective is to synthesize common entity meanings across agency business functions and to move the agency toward interoperable data architecture through stable, non-redundant shared data and reusable information exchange structures.

  • The Enterprise Data Model is a “living” model. As such, it provides reusable data artifacts to new projects and, during the course of project logical data modeling, is updated with new and discovered data artifacts whose make-up suggests their potential for reuse across multiple business-line information processes.
  • The Enterprise Data Model serves as the reference for the ideal description and security designation of important business data entities.

The following processes occur during enterprise data design activities:

  • Define Enterprise Business Entities
  • Define Entity Relationships
  • Analyze Entity States
  • Determine Entity Identifiers
  • Define Enterprise Attributes

How is Enterprise Data Managed?

  1. Identify the types of data your organization handles. This includes financial information, inventory numbers, photos, graphics, videos, and social media data as well as mobile data and internet of things (IoT) data.
  2. Organize this data into separate streams in order to make it easier to access when needed.
  3. Secure all of this information so that it is protected from unauthorized access or use.
  4. Make sure that all systems are up to date and running smoothly in order to facilitate quick retrieval of any desired piece of data quickly and easily without any issues or delays in performance.

How is Enterprise Data Secured?

  1. Identify the types of data you need to protect and determine who has access to it.
  2. Create a data security policy that outlines what is allowed and what is prohibited, as well as the consequences for violating the policy.
  3. Implement strong encryption methods for sensitive data, such as passwords, credit card numbers, and personal information, using a key or password that only authorized individuals know about.
  4. Monitor access to Enterprise Data by tracking who has been accessing it and when they accessed it, in order to detect any unauthorized activity quickly.
  5. Ensure that all devices used by employees have secure access points so no one can gain unauthorized access from outside sources like public Wi-Fi networks or unsecured Bluetooth connections.

See Also

Enterprise Data Management (EDM)