Every business is unique, so there are various data management methods. Companies are free to create their own mix of data management practices, but these techniques are the most common:
Data pipelines.
A data pipeline is a path that allows businesses to transfer information between two or more different systems automatically. For example, you might connect your sales enablement software to your website analytics to enrich your lead profiles. Sometimes, the data pipeline will modify or enhance your data during the exchange process, but it can also leave the raw data unchanged.
Example: A retail company uses a data pipeline to automatically transfer sales data from its CRM system into a cloud-based data warehouse. This allows the company to generate real-time sales reports without manual data entry.
These are specific types of data pipelines used for data integration. ETL involves extracting data from source systems, transforming it into a suitable format, and then loading it into a target system — often a data warehouse. ELT reverses the order of the last two steps, loading the raw data first and then transforming it within the target system.
Example: A healthcare provider extracts patient data from multiple clinics (extract), cleans and formats the data to comply with privacy regulations (transform), and then loads it into a central data warehouse for analysis (load).
For ELT, an example would be a social media platform extracting user activity data (extract), loading it into a data lake (load), and then processing the data to generate user engagement insights (transform).
Data architecture.
This involves designing the overall framework for how data flows through an organization's systems, covering everything from data storage and usage to compliance. A well-defined data architecture ensures that information is managed efficiently and consistently.
Example: A financial institution designs its data architecture to ensure that customer transactions are stored securely and comply with industry regulations. Data is stored in a secure data warehouse, with specific access control policies defined for each department.
Data modeling.
This technique involves creating visual diagrams that represent the structure of data and the relationships between different data elements within a system or across multiple systems. Data models enable teams to understand how data flows and is organized, facilitating more effective data management and analysis.
Example: A logistics company creates a data model to visualize the relationship between warehouses, inventory items, and shipping routes. This helps the company optimize its inventory management by providing a better understanding of how products flow through the system.
Data catalogs.
These serve as inventories of an organization's data assets, containing metadata that makes essential information searchable and easily discoverable. For instance, a data catalog can store information about the location, format, and quality of various datasets.
Example: A large university maintains a data catalog, enabling researchers to access datasets related to various academic fields easily. The catalog includes metadata like dataset descriptions, formats, and usage restrictions.
Data governance.
This encompasses the set of rules, policies, and procedures that an organization follows to standardize data, ensuring its quality, security, and compliance. Data governance often involves establishing a dedicated team to oversee data policies and ensure accountability.
Example: A pharmaceutical company implements data governance practices to ensure clinical trial data is accurate, consistent, and compliant with regulatory standards. A dedicated team oversees these practices, enforcing proper documentation and audit trails.
Data security.
The primary goal of data security is to protect an organization's information from breaches, theft, and unauthorized access. This IT function typically involves creating and enforcing policies related to software, access controls, backups, and storage.
Example: An e-commerce company encrypts sensitive customer data (such as credit card numbers) and implements two-factor authentication for employees accessing the system, ensuring only authorized individuals can retrieve the data.
Data life cycle management.
This involves monitoring and managing data throughout its entire life cycle – from its creation or collection to its eventual deletion or archiving. Establishing policies for each stage of the lifecycle ensures that data is handled appropriately, remains relevant, and remains secure.
Example: A government agency establishes a policy to archive old citizen data after 10 years. They aim to ensure that active data is readily accessible while minimizing the storage costs of older, less-relevant data.
Data processing.
This refers to the transformation of raw data into a more usable and actionable format. Data processing can involve cleaning, transforming, and integrating data to derive meaningful insights.
Example: A media company collects raw data from various video streaming platforms, processes it to remove irrelevant data, and structures it in a database to provide personalized recommendations to viewers.
Data integration.
This process brings together data from multiple disparate sources into a unified view. It is crucial for businesses that rely on various systems for different operations, as it provides a comprehensive understanding of their data.
Example: An airline integrates data from its booking system, customer service platform, and social media channels. The goal is to provide a unified, comprehensive view of each customer’s interactions and preferences, enhancing both customer service and overall marketing efforts.
Data migration.
This involves transferring data between different systems or platforms, often when upgrading to a new database solution or migrating data to the cloud. The goal is to move existing information to a new solution with minimal errors or formatting issues.
Example: A retail chain migrates its inventory data from an on-premises database to a cloud-based system. This enables real-time tracking and better scalability as the business grows.
Data storage.
This fundamental aspect of data management involves securely saving data in a chosen location, whether on physical servers or in the cloud. Choosing the right storage solution depends on factors like data volume, access frequency, and security requirements.
Example: A media company stores video files on high-capacity cloud storage to enable easy scaling as the company produces more content. The data is regularly backed up to protect against data loss.
Master data management (MDM).
Master data management focuses on ensuring that core business data, such as customer or product information, is accurate, consistent, and shared across the company. This reduces duplication and errors, providing a single source of truth for critical data
Example: A global retailer uses MDM to maintain a single, consistent record of all products across their stores. This reduces errors in product listings and improves worldwide inventory management.
Big data management.
With data volumes increasing over time, big data management techniques are essential for handling and analyzing vast amounts of data from various sources, often including unstructured or semi-structured data. This typically involves using technologies such as data lakes and specialized processing frameworks.
Example: A tech company uses big data management tools to analyze user behavior across millions of devices. The company processes the data in a distributed manner, which enables it to gain insights into user preferences and improve product recommendations.
Cloud data management.
As more organizations move their data to the cloud, cloud data management has emerged as a critical area. This involves managing data within cloud-based environments, leveraging the cloud's scalability, flexibility, and cost-effectiveness.
Example: A startup uses cloud data management to store and process large volumes of customer data in real time. This helps the company implement cloud computing resources to scale up during peak demand and keep operational costs low during off-peak periods.
