What is Data Management?

Data is a valuable resource for modern organizations. With access to large volumes and different data types, organizations invest significantly in data storage and management infrastructure. Organizations use data management systems to automate operational business processes and analyze data to inform business decisions. Here are some further specific benefits of data management.

Operational efficiency

Data management systems help organizations to process large volumes of transactions and operational data efficiently. They make sure that transactions are captured accurately and consistently, minimizing errors in financial records, inventory updates, customer accounts, and other operational workflows. Beyond transaction processing, these systems can automate routine business operations and provide reliable record-keeping, offering the consistency required for real-time activities. Through these efficiency benefits, data management systems help organizations deliver seamless customer experiences, maintain trust, and keep day-to-day processes efficient and scalable.

Increase revenue and profit

Data analysis gives deeper insights into all aspects of a business. You can act on these insights to optimize business operations, gain insights that promote better-informed decisions to increase revenue, and reduce costs. Data analysis can also predict the future impact of decisions, improving decision-making and business planning. Hence, organizations experience significant revenue growth and profits by improving their data management techniques.

Reduce data inconsistency

Data inconsistencies in transaction processing can lead to errors such as duplicate records, incorrect account balances, and mismatched inventory, which disrupt operations, undermine customer trust, and increase remediation costs. Inconsistencies in data analytics can result from data silos.

A data silo is a collection of raw data within an organization that only one department or group can access. Data silos create inconsistencies that reduce the reliability of data analysis results. Data management solutions integrate data and create a centralized data view for better decision-making and improved collaboration between departments.

Meet regulatory compliance

Laws such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are designed to safeguard customer data. These data protection laws include mandates that need:

• Consent to capture data
• Strict controls over data location and use
• Secure data storage and deletion on request

Hence, organizations need a data management system that is accurate and confidential to help protect data and still maintain data accuracy.

The practice of data management spans the collection and distribution of high-quality data, in addition to data governance to control data access.

Data governance includes the policies and procedures that an organization implements to manage data security, integrity, and responsible data utility. It defines data management strategy and determines who can access what data. Data governance policies also establish accountability in the way teams and individuals access and use data. Data governance functions typically include:

Data profiling

Data profiling is the diagnostic process of analyzing data to determine its structure, quality, and characteristics. This is the first step in understanding an existing dataset, to decide whether it needs refactoring before use.

Data lineage

Data lineage tracks data flows across an organization. Time-stamped data lineage is used to determine where a piece of data originated, how it has been used, and when and how it has been transformed. This data management process is particularly important in auditing processes.

Data catalog

Data catalogs are a collection of the organization’s data assets and related metadata. By storing all data-related information in a central catalog, it becomes the main data registry within the organization. Users can expect the data catalog to contain the most up-to-date information on all data assets.

Data security and access control

Data governance helps prevent unauthorized access to data and helps protect data from corruption. Data security and access control cover all aspects of data protection, such as the following:

• Preventing accidental data movement or deletion
• Securing network access to reduce the risk of network attacks
• Verifying that the physical data centers that store data meet security requirements
• Keeping data secure, even when employees access data from personal devices
• User authentication, authorization, and the setting and enforcement of access permissions for data
• Helping make sure that the stored data complies with the laws in the country where the data is stored
• Adding extra layers of controls for sensitive data

Data compliance

Data compliance policies reduce the risk of regulatory fines or actions. Meeting compliance laws such as the GDPR and CCPA is essential to operations.

Compliance activities focus on data modeling, software controls, and employee training so that adherence to laws happens at all levels. For example, an organization collaborates with an external development team to improve its data systems. Data governance managers verify that all personal data is removed before passing it to the external team to use for testing purposes.

Data lifecycle management

Data lifecycle management refers to the process of managing data throughout its lifecycle. 

For instance:

• Data must be verified on ingestion and at regular intervals
• Data must be held for specific time periods for auditing purposes
• Data must be erased when it is no longer needed

Data quality management

Users of data expect the data to be sufficiently reliable and consistent for each use case.

Data quality managers measure and improve an organization's data quality. They review both existing and new data and verify that it meets standards. They might also set up data management processes that block low-quality data from entering the system. Data quality standards typically measure the following:

• Is key information missing, or is the data complete? (for example, the customer leaves out key contact information)
• Does the data meet basic data check rules? (For example, a phone number should be a certain number of digits)
• How often does the same data appear in the system? (for example, duplicate data entries of the same customer)
• Is the data accurate? (for example, the customer enters the wrong email address)
• Is data quality consistent across the system? (for example, date of birth is dd/mm/yyyy format in one dataset but mm/dd/yyyy format in another dataset)

Data distribution

Endpoints for data distribution

For most organizations, data has to be distributed to (or near) the various endpoints where the data is needed. These include operational systems, data lakes, and data warehouses. Data distribution is necessary because of network latencies. When data is needed for operational use, the network latency might not be sufficient to deliver it promptly. Storing a copy of the data in a local database resolves the network latency issue.

Data distribution is also necessary for data consolidation. Data warehouses and data lakes take data from various sources to present a consolidated view of information. Data warehouses are used for analytics and decision making, whereas data lakes serve as a consolidated hub from which data can be extracted for a variety of use cases, while increasingly also supporting analytics directly on the data stored within them.

Data replication mechanisms and impact on consistency

Data distribution mechanisms have a potential impact on data consistency, and this is an important consideration in data management.

Strong consistency results from synchronous replication of data. In this approach, when a data value is changed, all applications and users can see the changed value of the data. If the new value of the data has not been replicated as yet, access to the data is blocked until all the copies are updated. Synchronous replication prioritizes consistency over performance and access to data. Synchronous replication is often used for financial data.

Eventual consistency results from asynchronous replication of data. When data is changed, the copies are eventually updated (usually within seconds), but access to outdated copies is not blocked. For many use cases, this is not an issue. For example, social media posts, likes, and comments do not require strong consistency. As another example, if a customer changes their phone number in one application, this change can be cascaded asynchronously.

Comparing streaming with batch updates

Data streams cascade data changes as they occur. This is the preferred approach if access to near-real-time data is required. Data is extracted, transformed, and delivered to its destination as soon as it is changed.

Batch updates are more appropriate when data has to be processed in batches before delivery. Summarizing or performing statistical analysis of the data and delivering only the result is an example of this. Batch updates can also preserve the point-in-time internal consistency of data if all the data is extracted at a specific point in time. Batch updates through an extract, transform, load (ETL or ELT) process are typically used for data lakes, data warehousing, and analytics.

Master data management

Master data management is the process of managing the consistency and synchronization of essential business data. Examples of master data include customer data, partner data, and product data. These fundamental data are mainly persistent and do not change often. Examples of this data in use include Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) software.

Master data management is essential to help make sure that this data is accurate across systems, including synchronization and data integration on updates.

Cloud data management (CDM) is the management of enterprise data in the cloud, when data is at rest, in processing, and in transit. Many of the same practices of traditional data management apply to managing data in the cloud.

As cloud environments are different from standard on-premises environments, the way data is handled is slightly different. Cloud storage, cloud compute, and cloud networking work together, alongside modern cloud data management services, to meet data management expectations.

Cloud storage

Cloud service providers offer data storage across multiple products and services, such as operational databases, data lakes, and cloud data warehouses. These data storage solutions are cloud-native, run on cloud instances, and offer virtualized storage configurations to fit any use case. Cloud storage instances must be configured to meet data standards.

Cloud compute

Cloud compute instances are designed to process stored cloud data. These compute instances also offer many different configurations, each for slightly different types of workloads, such as transaction processing, process automation, business intelligence, analytics, machine learning, and AI. Cloud compute instances must be configured for internal rules surrounding cloud data management.

Cloud networking

Cloud networking solutions such as virtual private clouds (VPCs) and virtual private networks (VPNs) offer software-based networks. Cloud networking provides isolation by segmenting resources and making sure that workloads are securely separated from one another and better protected against unauthorized access. Data in transit over these networks must be managed with a combination of product controls and network security products.

Cloud data management tools

Each cloud provider offers different solutions for cloud data management across your environment. These data management capabilities can include:

• Data unification services, such as data lakes and data warehouses
• Data security services, such as compliance management
• Data quality services to check for valid and high-quality data
• Data inventory solutions to identify sensitive data using AI and machine learning

Each cloud data management solution is designed to complement the fundamental data storage, processing, and transfer services offered in the cloud.

The Shared Responsibility Model

Security and Compliance are shared responsibilities between the cloud service provider and the customer. AWS calls this the Shared Responsibility Model. 

This shared model can help relieve the customer’s operational burden as the cloud provider operates, manages, and controls the components from the host operating system and virtualization layer down to the physical security of the facilities in which the service operates. Cloud data management providers and customers must understand their data management and security obligations under the model.

For instance, cloud providers must take steps to secure the underlying infrastructure that supports customers' cloud instances. Cloud providers make sure that the hardware is patched and operating as expected. Customers must then make sure that the operating system running on the instance is up-to-date.

Customers must make sure that they have adequate instance replications across zones and data backups. This helps in data consistency and makes the data retrievable in the case of an event requiring disaster recovery.

Data management best practices form the basis of a successful data strategy. The following are common data management principles to help you build a strong data foundation.

Team collaboration

Business users and technical teams must collaborate to help ensure that an organization's data requirements are met.

Automation

A successful data management strategy incorporates automation in most of the data processing and preparation tasks. Performing data transformation tasks manually is tedious and also introduces errors in the system. Even a limited number of manual tasks, such as running weekly batch jobs, can cause system bottlenecks. Data management software can support faster and more efficient scaling.

Cloud computing

Businesses require modern data management solutions that provide them with a broad set of capabilities. A cloud solution can manage all aspects of data management at scale without compromising on performance. For example, AWS offers a wide range of functionalities, such as databases, data lakes, analytics, data accessibility, data governance, and security, from within a single account.