Data management is the administrative process of organizing and controlling data resources to assure optimal accessibility for use within an organization. Data management may include practices, procedures and processes that define the way data is collected, stored, secured and accessed with high levels of cost-effective efficiency and reliability.
Big data, which often requires the expertise of those with data science degrees to manage and analyze computationally, may inform decisions for organizations and enterprises by revealing patterns, trends and associations, especially about customer behavior and experiences. Harnessing the insight from data can give brands a competitive edge against rivals in their industry by verifying or disproving existing theories or models. This guide will help you understand the fundamentals and processes of data management.
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An Introduction to Data Management
Many organizations and enterprises now use big data and intangible assets to inform business decisions, making a formal data management strategy valuable to high functioning internal operation. The high level of organization of data management ensures that data resources are supervised and accessible from their point of creation or collection, through any changes along their life cycle, and sometimes to their retirement. Data management is typically a diverse but unified system, including:
- Data collection: Accessing or generating data.
- Data security and compliance: Data must be encrypted and secured for privacy, as well as stored and used in compliance with industry regulations.
- Data analytics: Data that is being mechanically or algorithmically processed for insight and storage.
- Databases: A collection of recorded data stored for processing.
- Data lakes: A pool of raw data with undefined purpose.
- Data warehouses: Data that has been analyzed, filtered, structured and processed for a specific purpose.
- Big data management systems: A more advanced database that may contain huge amounts of unstructured and semi-structured data, typically beyond the ability of a traditional database.
Principles of Data Management
Data is an intangible resource that may be vitally important to the operations of an organization and is crucial to business strategy. The principles of data management may include:
- Data policy
- Data ownership
- Data documentation and metadata compilation
- Data quality, standardization and harmonization
- Data life cycle control
- Data stewardship
- Data access and dissemination
- Data audits
A data policy is the first step in data management and is designed by IT professionals and those with advanced information systems degrees. The data policy will set a guiding framework for how all data is managed and may operate. All collected data should be organized into data ownership, dictating who has managerial, legal and financial control over the data. Data ownership includes the rights of use, maintenance and destruction of data.
All data should be identified for proper management and effective use. Data documentation and metadata compilation offer an accurate catalog of data and any related content details as a reference and roadmap for data location and use. The organization and documentation of data should meet data quality, standardization and harmonization to maximize the use of and value of the data. This may include standardizing data quality, definitions and formats, and ensuring that data meets the appropriate standards and procedures.
Good management of data may also include data life cycle control. Managing data throughout its life cycle may include justifying why new data is required rather than amending existing data, how data can be maximized for use and that it meets other possible requirements. Data life cycle control may also include assessing the cost of handling, storing and maintaining data, as well as archiving or final destruction of data.
Formal responsibility of data should be entrusted via data stewardship. Different sets of data, even in the same organization, may have different data stewards who are responsible and accountable for the management and care of the data assigned to them. The regulation of data access and dissemination delegates and controls who is granted the opportunity to view or interact with data, and optimizes accessibility and usability to the correct partners. Performing regular data audits ensures that all data management procedures are compliant with data policies and guidance, and works to improve the quality and accessibility of data assets.
The Data Management Process
The data management process may be complex with a variety of steps and considerations that may include:
- Creating a process for gathering data.
- Describing the type of data and metadata to be organized.
- Creating a data analysis plan.
- Assessing the needs and parameters of the data management plan.
- Assembling and leveraging a data management toolkit and applicable applications.
- Creating a data management plan with objectives and resources in mind.
- Organizing the standards and schemas that will be used to store or share the data.
- Selecting and organizing the data repositories.
- Selecting and organizing data stewardship.
- Ensuring proper data storage and preservation of access.
- Tracking data life cycles, preventing data loss.
- Controlling data security and compliance regulations.
- Performing data audits.
Data Management and Data Analytics
Data management and data analytics are both fields of study under data science. Data management encompasses the comprehensive plan of how to collect, organize, store, protect and use data. Data analysis is the science of examining raw data. This may include applying mechanical or algorithmic processes to discover insights, look for meaningful correlations, or find information.
Though data management and data analytics differ in form and function, they intersect and complement each other. One of the largest profits of data management is the insights and knowledge gained from data analysis. Analytical success from data—especially big data—requires data to be readily accessible, accurate and complete.
Getting Started with a Data Management Strategy
The digital age is producing vast amounts of data. The data economy is different from past economies where the value was distinctly represented by buyers and sellers or consumers, and this is because the value of data is often hard to measure. The data economy is transforming many economic activities, and the importance of measuring data and data flows is increasingly important, especially considering the values that data can offer businesses in augmenting business models, processes, production, distribution, marketing, coordination and improved products and services that consumers value.
A data management strategy is essential to growth in the modern economy regardless of whether it is a large scale data sharing enterprise or a smaller non-enterprise data sharing company. The five essential components of a data strategy are:
- Identify: Identifying a process to represent, access and reference, as well as store data.
- Store: The sophisticated methods of planning capacity and allocating storage to various systems.
- Provision: The packaging of and decoding of data to ensure optimal sharing and reuse while providing and abiding by rules and access guidelines.
- Process: When data is collected from both internal and external sources, it is generally obtained in a raw format. To make data ready to use it requires activities that manufacture it into a prepared and organized format.
- Govern: Organized data needs to be managed and accessed through information policies, rules and methods that ensure uniform data usage and management for all staff members, systems and applications.
Required Education and Skills for a Career in Data Management
At minimum a bachelor’s degree, and often a master’s degree in data science, computer science, computer engineering, or IT-related fields such as business analytics or information systems, is typically required to work in data management. The coursework for these degrees should cover data management, programming, big data developments, systems analysis and technology or data architecture. Business skills often associated with data architects and data management include:
- Analytical problem-solving skills for high-level data challenges.
- Communication skills to explain complex concepts to non-technical colleagues.
- Management, leadership and collaborative skills for advising or working on a team.
- Industry knowledge and expertise in the chosen industry to understand the specific aspects of data collection needs for analysis and utility.
- Operational systems analysis skills to help determine the best solutions.
- Familiarity with data management software such as:
- Application server software
- Database management system software
- User interface and query software
- Enterprise application integration software
- Development environment software
- Backup or archival software
Data Management Best Practices
Managing data and making data-driven decisions is often impacted and directed by the industry and field of the organization or business. By managing data correctly and in a way crafted toward the needs of the organization and industry, data management may offer more efficient data retrieval, storage, use and preparation for analysis. These best practices form a solid foundation for a start in data management:
- Ensure your data management strategy is in line with your business objectives. It is important to understand the data needs of the business and what purpose the data is meant to achieve. This will direct the planning, acquisition, processing, analyzing, storing and accessibility choices involved in creating and managing a data management strategy.
- Create clear roles and ensure stakeholders have access to the data they require. Defining roles of access and stewardship is essential to a data management strategy. Clearly defining what data needs to be accessible and how it is accessed will direct how data is stored and cared for.
- Continually assess your data. Considering data as an asset includes reassessing data gathering practices, as well as continual assessment and auditing through its life cycle. This practice ensures optimal results by assessing quality and compliance, as well as continually synthesizing data to present it in meaningful formats and appropriate standards.
- Optimize data security and have a data recovery strategy. Protecting data is critical to a data management plan. Performing regular audits, data activity monitoring and risk analysis to discover vulnerable assets. In addition to security measures, generate a data recovery strategy that protects compromised organizations and recovers maliciously encrypted data.