Data Inventory

Introduction

In today’s data-driven landscape, effectively managing and understanding your data assets is crucial. This guide explains the concept of “data inventory.”

Data inventory is a methodical way of organizing and comprehending data stored in different databases and storage systems. By creating a data assets inventory, organizations can improve data management and decision-making processes.

We will learn how to do data management using built-in tools in common databases and specialized software. The main focus will be on managing various data types, such as images. This article will help you learn how to start analyzing your own data assets with practical examples and insights.

What is Data Inventory?

Data inventory involves organizing and examining an organization’s data assets to determine their type, location, usage, and governance. This systematic approach helps organizations manage their data efficiently, comply with regulations, and harness their data for strategic decisions.

The Importance of Data Assets

Analyzing data assets effectively gives a complete view of an organization’s data, leading to better business strategies and operational efficiencies. It helps in data governance, risk management, and the optimization of data storage and retrieval processes.

Popular Databases Workflow

SQL-Based Systems

Many relational databases, like MySQL and PostgreSQL, offer tools and commands for conducting data inventories. For example, to list all databases on a MySQL server, you can use:

SHOW DATABASES;

The result will be a list of all databases managed by the MySQL server. Similarly, PostgreSQL users can retrieve a list of all database names using:

\l

Data Inventory with SQL Server

SQL Server provides a rich set of tools for data inventory. Using Transact-SQL, you can query metadata to obtain information about database objects. For instance, to find details about the tables in a database, use:

SELECT * FROM INFORMATION_SCHEMA.TABLES;

This command lists all tables along with schema details, helping you understand the structure of your data environment.

NoSQL Systems

Databases like MongoDB handle data assets uniquely because they do not have a set structure. This means that users can store and manage data in a more flexible manner.

Users have the freedom to define the structure of their data as they see fit. This allows for greater customization and adaptability in handling data assets. MongoDB offers commands such as:

show dbs
show collections

These commands list all databases and collections, respectively, providing a basic overview of the stored data.

Dedicated Software for Data Inventory

Beyond native database tools, dedicated data inventory software offers advanced features for managing and visualizing data assets. These tools often support multiple database types and provide deeper insights through data discovery, classification, and data lineage features.

DataSunrise

DataSunrise offers a wide range of features for managing data inventory, including activity monitoring and sensitive data discovery. Utilizing dedicated software has demonstrated clear advantages over native or non-commercial tools, thanks to its rich feature set. Proper maintenance and auditing of the data inventory are also crucial. Dedicated software typically integrates all necessary tools for these tasks.

DataSunrise also offers an intuitively simple web-based user interface. Beginners easily grasp its major features.

Apache Atlas

Apache Atlas is a popular open-source tool designed for data governance and metadata management across various data environments. It enables users to perform comprehensive data inventories by automatically classifying data and managing metadata.

Handling Image Data in Data Inventories

Image data poses unique challenges for data inventory processes. Unlike textual or numerical data, images require metadata to be fully searchable and manageable. To create a data inventory for image data, you need to extract metadata. You may also need to use image recognition technologies to label and categorize the image content.

Example: Inventory of Image Data

Consider a database storing image files along with metadata in a NoSQL system like MongoDB. One way to simplify searching and managing files is by using a script. The script can extract metadata such as file size, type, and creation date. You can store this metadata in a separate collection. It is worth mentioning here that DataSunrise includes built-in functionality to make OCR tasks for sensitive data discovery.

Implementing Data Inventory

Implementing a data inventory process involves several key steps:

1. Identifying all data sources.
2. Cataloging the data types and structures.
3. Analyzing the usage and access patterns of the data.
4. Implementing tools and scripts to automate the inventory process.

For a SQL database, you might start by creating a user specifically for data inventory purposes:

CREATE USER 'inventory_user' IDENTIFIED BY 'password';

This user can then run queries to catalog data without affecting the operational integrity of the database.

To collect, automate, and visualize data inventory results effectively, you can follow these concise steps:

1. Data Collection: Identify and catalog all data sources using scripts or data inventory tools. For SQL databases, utilize queries to extract metadata; for NoSQL, use commands to list databases and collections. For image data, you should extract relevant data from images using OCR tools.
2. Automation: Set up automated scripts or employ data inventory software like DataSunrise or Apache Atlas to regularly update your data catalog. Use cron jobs for periodic assessments or triggers in databases to log changes.
3. Use tools like Tableau, Power BI, or custom web-based dashboards to create visual representations of your data. These visualizations can depict the volume, distribution, and types of data across the organization, providing insights at a glance.

To improve data governance, organizations should follow these steps to keep an updated and easily accessible inventory.

Conclusion

Effective data management begins with a thorough data inventory. Understanding your data, knowing where you store it, and understanding how you use it can help you make better decisions. It can also help you meet legal requirements and improve how you handle data.

Modern organizations need to conduct a data inventory using either native database tools or dedicated software. This guide provides a starting point for those looking to understand and implement data inventory techniques in their operations.

Discover the power of efficient data management with DataSunrise’s suite of data discovery and compliance features. We invite you to visit DataSunrise Team Online and experience our live demo. See firsthand how our tools can enhance your data security, compliance, and governance efforts.

Don’t miss the opportunity to simplify your data operations. Come join us online today to see how DataSunrise can assist you.