Enterprise Data Management — types of data
Introduction
Let’s just start by saying the obvious, Data is the New Oil.
Just like oil, data needs to be refined, carefully, to get desired output. However, unlike oil, data keeps growing with its usage.
Combination of these two statements present the underlying challenge for today’s organizations:
Processing the ever-increasing volumes of data for their growth.
We have often experienced that every organization has different challenges while handling their data efficiently. Due to uniqueness of issues, at least to a certain degree, with every organization, it becomes difficult to have a standardized objective approach which can be used to understand and resolve the inherent challenges of enterprise data management.
This is a series of articles to present an objective and generic approach to identify data management issues and various solutions to resolve them. This, the first chapter, lays the foundation with a basic introduction about data management.
What is Data Management?
Before we deep dive into it, let’s quickly understand what Data Management is and why is it so important.
Data Management comprises all disciplines related to managing data as a valuable resource.
It includes acquiring, validating, storing, protecting, and processing required data to ensure the accessibility, reliability, and timeliness of the data for its users.
Enterprise Data Management (EDM) is the ability of an organization to manage their data assets, internal or external, for fact-based decision making.
EDM directly impacts data quality. Data quality is the foundation of data analytics, which reveals trends and metrics to identify risks and opportunities for the business. Improper data management impacts your business decisions and growth.
Before we can identify and quantify various data issues, let’s look at various types of enterprise data.
Types of Enterprise Data
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 manage 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 consistent and uniform set of identifiers which describe core entities of the business operations. Usually, master data defines unique entities in Products, Customer, Vendor 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. 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, list of countries and corresponding state and cities is reference data.
- Metadata: It provides descriptive, structural information about other data related to business activities. For example, Name of a person may have 3 components as First Name (mandatory), Last Name (mandatory) and Middle Name (optional). This information is metadata about name of a person.
Now that we have a basic understanding of what data management is all about and various types of enterprise data. In the next article, we will see how can we identify and classify data issues.
