Data management can be defined as the process businesses follow to collect, organize, and use data. Data management aims to strike a balance between a company’s efficiency, security, and cost savings.

A practical data management approach establishes official policies and workflows, rather than leaving information handling to individual employees or departments. This helps develop a consistent standard across the business and ultimately enables organizations to use their data at scale.

Key components of a data management strategy.

1. Collection. This involves gathering data from internal (e.g., CRM systems, transactional databases) and external sources (e.g., social media, market research, IoT devices). Data may be structured or unstructured, and the collection process must ensure that relevant and high-quality data is available.

2. Organization. Includes structuring and categorizing data to facilitate easy access and analysis. This usually involves building databases, using data lakes, and implementing taxonomies and metadata to enhance data retrieval and processing efficiency.

3. Storage. Ensuring secure storage of data to maintain its availability and integrity. This can include traditional databases, data warehouses, cloud-based solutions, and data lakes, based on the data type and access needs.

4. Protection. Using security features like encryption, access controls, firewalls, regular security audits, and backup systems to safeguard data against breaches, loss, or corruption.

5. Utilization. Making data usable by setting up tools and processes for access, processing, and analysis enables organizations to retrieve insights that inform decision-making, improve operations, and enhance digital customer experience. It can also help identify trends, patterns, and anomalies.

6. Data Governance. This involves setting up policies for managing data consistently and in compliance. Data governance enables control over who can access, modify, and use data. It ensures data quality, accuracy, and regulatory compliance, reducing risks associated with poor data management.

This post will cover:

• Benefits of data management.
• Data management challenges.
• Data management best practices.
• Types and examples of data management.
• Manage data with Adobe Real-Time CDP

Benefits of data management.

Without data management processes, there’s no framework to help you make sense of this data. This means your business could waste precious time, resources, and critical data that could improve operations.
Effective data management enables companies to transform raw data into actionable insights, allowing them to derive more value from their data with less effort. Businesses that invest in data management enjoy several benefits, including:

1. Visibility. What information is available across your business? Data management processes consolidate all data sources into a single feed, providing comprehensive visibility and control in a unified, big-picture view.
2. Reliability. With effective data management, there’s no need to ask employees to sift through information to find what’s accurate and what isn’t. It allows businesses to ensure reliability while decreasing time to value.
3. Security. Unmanaged data poses a significant security risk. Data management processes can help you secure your information from unauthorized access by bringing it under control. It’s one of the best ways to prevent the expensive headaches of data breaches.
4. Scalability. The good news is that your business doesn’t have to manage data manually. Data management solutions enable the automatic and scalable management of data. This helps to ensure data consistency and security across your enterprise.
5. Profitability. Data can help your business become more profitable, but only if you mobilize it. Effective data management strategies enable you to uncover valuable insights and make more informed, profitable decisions. They can help to remove data redundancies and eliminate errors that could lead to costly mistakes. Data management can also optimize storage resources, allowing your company to avoid unnecessary expenses.
6. Transparency. Research shows that 70% of consumers trust companies with which they do business to protect their data. Building trust with your customers takes time, but being transparent with how you use their data can help. Sharing your policies with customers can make it easier for them to utilize your information while earning their trust. Enhancing customer data management with an effective strategy can help organizations gain deeper insights into customer behavior, preferences, and needs. For instance, a retail company could use analytics to track customer purchasing trends and adjust its marketing strategy accordingly to boost sales.
7. Consistency. Inconsistent information can lead to misunderstandings. But with effective data management processes, everyone sees a unified, centralized view of your raw data in one place.
8. Compliance. Businesses are required to give consumers control over their data. Data management allows you to stay compliant with GDPR, CCPA, and other data privacy regulations. This can help you avoid expensive regulatory fines while improving customer relationships.

Data management challenges.

Data management has benefits, but it isn’t easy to execute. The continued expansion of available personal and customer data makes it even more challenging to interpret and utilize in creating actionable targeting strategies.

Here are some of the most common challenges to consider:

• Lacking data insight. Businesses can collect more information than ever. However, having terabytes upon terabytes of data can make it challenging to manage, spot trends, and gain actionable insights.

• Maintaining data-management performance levels. As databases accumulate more information, maintaining performance becomes increasingly challenging. The challenge is to maximize data integrity at scale without compromising the level of quality.
• Complying with changing data requirements. Ever-changing compliance requirements make it hard for businesses to commit to a data management strategy. As soon as you achieve compliance, new requirements might make your past data management practices unusable. Furthermore, businesses targeting an international audience must comply with a complex web of global, national, and local requirements.
• Processing and converting data with ease. Raw data usually lacks value on its own. Processing and converting it into the right formats makes it more actionable, but doing so at scale can be difficult.
• Storing data effectively. Data warehouses can store data, but it isn’t unusual for businesses to store their information in multiple warehouses or data lakes. Data scientists might need to reformat data to perform an analysis, but its stored format can limit analysis. Security concerns also make it difficult for companies to store data in the cloud effectively.
• Optimizing its agility and costs continually. Data storage comes at a price. The more data you store, the more you pay. Larger quantities of data can also affect your business’s data agility. It’s up to your IT department to manage the agility of this data while balancing costs.
• Drawing value from new analytics and data. As you collect larger volumes of data, finding meaning in all your information can become difficult. If businesses don’t have the right management solutions, they risk missing out on insights from new analytics and data.
• Integrating disparate databases. Most data management platforms pull information from multiple sources. Bringing data into a single repository is helpful, but not all software or storage solutions integrate seamlessly. Integration headaches can result in inaccurate, incomplete, or incorrectly formatted data, which can harm accuracy and productivity.
• Hiring and training employees. Regardless of your employees’ expertise, there is a good chance they may not possess the necessary skills to handle all aspects of data management. Businesses either need to hire new employees with specific skill sets or train their internal teams in data management. That requires time and effort, which can impact your time-to-value.

Data management best practices.

While data management has challenges, businesses can mitigate the effects by embracing standard best practices. For example:

Types and examples of data management.

Every business is unique, so there are various data management methods. Companies are free to create their 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 bulk up 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 needing manual data entry.

ETL/ELT (Extract, Transform, Load/Extract, Load, Transform).

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 them optimize their 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 that 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 ecommerce 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 content, 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. This 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. 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-site 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 utilizing 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. They process the data in a distributed manner, which enables them 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 to implement cloud computing resources to scale up during peak demand and keep operational costs low during off-peak periods.

Manage data with Adobe Real-Time CDP.

Businesses that fail to adapt to new practices risk falling behind in a world where data is a critical asset. A solid data management strategy ensures that data is organized, secure, and unlocks its full potential for driving informed business decisions. Data management evolves as quickly as data technology, so businesses must develop a plan for managing large amounts of data.

Instead of simply collecting more and more information, use data management to regain control and generate value from it. Effective data management can help you develop a strategy for collecting, analyzing, and utilizing information to benefit your business.

When you’re ready to get started, check out the advanced features of Adobe Real-Time Customer Data Platform. Adobe Real-Time CDP collects B2C and B2B data, unifying it into real-time profiles ready for activation across any channel.

Watch the Adobe Real-Time CDP overview video.

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