Every business is different, so data management methods vary. Organisations can mix and match practices to suit their needs, but these techniques are the most common:
Data pipelines
A data pipeline is a channel that lets businesses automatically transfer information between two or more different systems. For example, you might connect your sales enablement software to your website analytics to enrich your lead profiles. During the exchange process, the pipeline may modify or enhance the data – or 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 lets the company 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 it to comply with privacy regulations (transform), then loads it into a central data warehouse for analysis (load).
For ELT, a social media platform might extract user activity data (extract), load it into a data lake (load), then process the data to generate user engagement insights (transform).
Data architecture
This involves designing the overall framework for how data flows through an organisation’s systems – covering everything from data storage and usage to compliance. A well-defined data architecture ensures information is managed efficiently and consistently.
Example: A financial institution designs its data architecture to store customer transactions securely and meet industry regulations. Data is held in a secure data warehouse, with defined access control policies for each department.
Data modelling
This technique uses visual diagrams to map the structure of data and the relationships between data elements – within a single system or across multiple systems. Data models help teams understand how data flows and is organised, supporting more effective data management and analysis.
Example: A logistics company creates a data model to visualise the relationship between warehouses, inventory items, and shipping routes. This helps the company optimise inventory management by giving clearer visibility into how products move through the system.
Data catalogues
These act as inventories of an organisation’s data assets, with metadata that makes essential information searchable and easy to find. For instance, a data catalogue can store information about the location, format, and quality of various datasets.
Example: A large university maintains a data catalogue, giving researchers easy access to datasets across a range of academic fields. The catalogue includes metadata such as dataset descriptions, formats, and usage restrictions.
Data governance
This covers the rules, policies, and procedures an organisation follows to standardise data – maintaining its quality, security, and compliance. Data governance typically involves setting up a dedicated team to oversee data policies and uphold accountability.
Example: A pharmaceutical company puts data governance practices in place to keep clinical trial data accurate, consistent, and compliant with regulatory standards. A dedicated team oversees these practices, enforcing proper documentation and audit trails.
Data security
The core goal of data security is to protect an organisation’s information from breaches, theft, and unauthorised access. This IT function typically involves creating and enforcing policies covering software, access controls, backups, and storage.
Example: An e-commerce company encrypts sensitive customer data – such as credit card numbers – and applies two-factor authentication for employees accessing the system, so only authorised individuals can retrieve the data.
Data life cycle management
This means monitoring and managing data across its entire lifecycle – from creation or collection through to deletion or archiving. Setting clear policies for each stage ensures data is handled appropriately, stays relevant, and remains secure.
Example: A government agency sets a policy to archive old citizen data after 10 years. The goal is to keep active data readily accessible while minimising the storage costs of older, less-relevant data.
Data processing
This is the process of converting raw data into a usable, actionable format. Data processing can involve cleaning, transforming, and integrating data to draw out meaningful insights.
Example: A media company collects raw data from multiple video streaming platforms, processes it to filter out irrelevant information, and structures it in a database to deliver personalised recommendations to viewers.
Data integration
This process pulls together data from multiple disparate sources into a single, unified view. It’s essential for businesses that rely on a range of systems across different operations, giving them a complete understanding of their data.
Example: An airline brings together data from its booking system, customer service platform, and social media channels. The aim is to deliver a unified, complete view of each customer’s interactions and preferences – improving both customer service and overall marketing efforts.
Data migration
This covers transferring data between different systems or platforms – typically when upgrading to a new database solution or moving data to the cloud. The aim is to shift 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 supports real-time tracking and better scalability as the business grows.
Data storage
Data storage is a core part of data management — it involves securely saving data in a chosen location, whether on physical servers or in the cloud. Choosing the right storage solution comes down to factors such as data volume, access frequency, and security requirements.
Example: A media company stores video files on high-capacity cloud storage, making it easy to scale as content production grows. Data is backed up regularly to guard against data loss.
Master data management (MDM)
Master data management ensures that core business data — such as customer or product information — is accurate, consistent, and shared across the organisation. This reduces duplication and errors, giving the business a single source of truth for critical data
Example: A global retailer uses MDM to maintain a single, consistent record of all products across its stores. This reduces errors in product listings and improves inventory management worldwide.
Big data management
As data volumes continue to grow, big data management techniques become essential for handling and analysing vast amounts of data from a wide range of sources — often including unstructured or semi-structured data. This typically involves technologies such as data lakes and specialised processing frameworks.
Example: A tech company uses big data management tools to analyse user behaviour across millions of devices. Processing the data in a distributed manner gives the company insight into user preferences and helps improve product recommendations.
Cloud data management
As more organisations shift their data to the cloud, cloud data management has become a critical area. This involves managing data within cloud-based environments, taking full advantage of 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 allows the company to scale up cloud computing resources during peak demand and keep operational costs low during off-peak periods.
