Data management is organising, storing, and managing data to support business operations and decision-making. In organisations, data is typically managed by teams or departments responsible for collecting, storing, and organising data and ensuring its quality, accessibility, and security. Data management involves several activities, including data modelling, warehousing, governance, and analysis. By effectively managing data, organisations can ensure access to the information they need to make informed decisions, improve operations, and drive business success.
Data security systems and processes
Data security systems and processes are the tools and methods used to protect data from unauthorised access, use, or disclosure. In organisations, data security is critical for ensuring the confidentiality, integrity, and availability of sensitive information. Data security systems and processes can include technical measures, such as encryption and access controls, as well as organisational policies and procedures, such as data classification and retention policies. By implementing effective data security measures, organisations can protect their data from external threats, such as hackers and cyberattacks, and internal threats, such as unauthorised access or data misuse.
Data management systems
Data management systems are used by organisations in all sectors to collect, store, and manage data in support of their operations and decision-making.
- Private sector: In private organisations, data management systems are typically used to support various business functions, such as marketing, sales, finance, and operations. These systems can help organisations improve efficiency, reduce costs, and drive business success by providing access to timely and accurate information.
- Public sector: In public organisations, data management systems are often used to support the delivery of services and the implementation of policies. For example, government agencies may use data management systems to track the performance of programs, monitor compliance with regulations, and engage with citizens.
- Third sector: In the third sector, data management systems are used by non-profit organisations to support their mission and achieve their goals. For example, charities may use data management systems to track donations, manage volunteer programs, and evaluate the impact of their activities. Overall, data management systems play a critical role in supporting the operations of organisations across all sectors.
Principles of data security
The principles of data security involve the organisation’s workforce processing, controlling, and maintaining data and equipment to a required standard for particular purposes. These principles are designed to protect the confidentiality, integrity, and availability of data, and to prevent unauthorised access, use, or disclosure of data. Some fundamental principles of data security include:
- Confidentiality: Ensuring that data is only accessed by authorised individuals and is kept private and secure.
- Integrity: Protecting data from unauthorised changes, modifications, or deletions and ensuring that it is accurate and complete.
- Availability: Ensuring that data is accessible and usable by authorised individuals when needed.
- Access control: Limiting access to data and systems to authorised individuals and using authentication and authorisation mechanisms to control who can access what data.
- Security awareness and training: Providing employees with the knowledge and skills to understand and follow security policies and procedures.
- Risk management: Identifying, assessing, and mitigating risks to data security and developing contingency plans to deal with potential security breaches.
By following these principles, organisations can ensure that their data is secure and that their workforce can process effectively, control, and maintain data and equipment to a required standard.
Data management systems
Systems for data management are the tools and technologies used to collect, store, and manage data in support of business operations and decision-making. These systems can include various software packages and technologies, such as database management systems, data warehousing systems, data mining tools, and data analytics platforms. These systems typically provide a range of features and capabilities, such as data storage and retrieval, data processing and analysis, and data visualisation. In addition to these technical systems, organisations also need to have policies and protocols that define the limitations and restrictions on the use of data. These policies and protocols can include data retention policies, data access policies, and data security policies, which outline the rules and guidelines for how data can be used within the organisation.
Transaction process system (TPS): A transaction process system (TPS) is an information system used to process and manage business transactions. These transactions can include financial transactions, such as sales and payments, and non-financial transactions, such as data entry and updates to records. TPSs are typically used to support various business activities, including order processing, inventory management, and customer service. They are designed to be fast, efficient, and accurate, supporting the real-time processing of transactions. Some typical features of TPSs include data storage and retrieval, data validation and error checking, and the generation of reports and alerts.
Management information system (MIS): A management information system (MIS) is a type of information system used to support an organisation’s decision-making and operations. MISs typically consist of hardware, software, and people and are designed to give managers the information they need to make informed decisions and manage the organisation effectively. MISs can support various business functions, including finance, marketing, operations, and human resources. Some standard features of MISs include data storage and retrieval, data analysis and visualisation, and the generation of reports and alerts. MISs can help organisations improve efficiency, reduce costs, and drive business success by providing timely and accurate information to support decision-making.
Executive information system (EIS): An executive information system (EIS) is a type of information system used to support the decision-making and operations of senior executives and managers in an organisation. EISs are typically designed to provide executives with high-level, strategic information tailored to their specific needs and roles. They often include data visualisation, analytics, and personalised dashboards, allowing executives to quickly and easily access the information they need to make informed decisions. EISs can help organisations improve their performance and achieve their goals by providing executives with the information they need to make effective decisions and manage the organisation effectively.
Processes in data management
The processes in data management involve the activities and tasks that are carried out to collect, store, and manage data in support of business operations and decision-making. These processes can include a wide range of activities, such as data collection, data entry, data cleaning and validation, data warehousing, data analysis, and data reporting. Data management processes typically involve teams or departments responsible for data management, such as data quality, security, and governance. These processes are essential for ensuring that organisations have access to high-quality, accurate, and timely data that can be used to support decision-making and drive business success.
- Definition of the task: In data management processes, the definition of a task typically involves specifying the objectives, scope, and resources required to complete a particular piece of work related to data management. For example, a task might include collecting data from a specific source, cleaning and transforming it, and loading it into a data warehouse. The definition of the task would consist of the goals of the task (e.g., to improve the quality of the data), the scope of the work (e.g., which data sources are included), and the resources that are required (e.g., time, personnel, software). By defining the task in this way, data management teams can ensure that they have a clear understanding of what needs to be done and can plan and execute their work efficiently and effectively.
- The organisation of resources: In the context of data management, the organisation of resources refers to the allocation and management of the resources (such as personnel and technology) needed to support the various data management processes. This can include identifying the roles and responsibilities of individuals and teams within the organisation and procuring the necessary technology and infrastructure to support data collection, storage, and analysis. Effective organisation of resources is essential for ensuring the success of data management processes. By allocating the right resources to the right tasks and activities, organisations can ensure that their data management processes are efficient and effective and have the necessary capabilities to support their data needs. Additionally, the organisation of resources can also involve the development of data governance policies and procedures to ensure the quality, security, and integrity of the organisation’s data. This can include defining roles and responsibilities for data management, establishing standards and best practices for data quality and security, and implementing appropriate controls and safeguards to protect the organisation’s data assets.
- Control of the process(es): Control of processes in data management refers to the activities and measures put in place to ensure that data management processes are carried out effectively and efficiently. This can include setting goals and targets for data management processes, establishing clear roles and responsibilities, implementing policies and procedures, and monitoring and evaluating the performance of data management processes. Control of processes in data management is essential for ensuring that organisational policies and standards carry out data management processes and are aligned with the organisation’s business objectives and needs. It can help organisations identify and address any issues or challenges that may arise during the data management process and ensure that data management processes are carried out consistently and reliably. Control of processes in data management can be achieved through various means, such as implementing quality control measures, conducting regular audits and assessments, and using technology to automate and monitor data management processes. Additionally, organisations may need to establish dedicated teams or departments to oversee the control of data management processes and provide support and guidance to data management teams.
- Processing information: Data processing is essential to the data management process. It involves collecting, organising, and analysing data to extract useful information and support decision-making. Data processing typically involves various activities, such as data entry, cleaning and validation, transformation, and analysis. In the data processing stage, raw data is collected from multiple sources and cleaned to remove errors, inconsistencies, and duplicates. This process helps ensure that the data is accurate, complete, and ready for further analysis. Once the data has been cleaned and validated, it can be transformed into a suitable format for analysis and reporting. This may involve grouping the data into categories, aggregating the data, or applying algorithms and statistical models to the data to uncover trends and patterns. The final stage of data processing involves analysing the processed data to extract insights and generate reports. This may include running queries and statistical analyses on the data, visualising the data using charts and graphs, and presenting the findings clearly and concisely. The insights generated from data processing can be used to support decision-making, improve operational efficiency, and gain a competitive advantage.
- Considered reporting: Data reporting is one of the processes in data management that involves generating reports from data to provide insights and support decision-making. This can include summarising data clearly and concisely, using charts and graphs to visualise data, and providing commentary and analysis to help interpret the data. Data reporting typically involves extracting data from a data warehouse or other data repository, applying filters and transformations to the data as needed, and generating reports using specialised reporting software or other tools. The reports can provide information on various aspects of an organisation’s operations, such as sales, revenue, customer satisfaction, and market trends. Data reporting can be an essential part of data management processes, as it helps organisations make sense of their data and use it to support decision-making. Effective data reporting can help organisations identify trends, patterns, and anomalies in their data and take action based on the insights generated from the reports.
- Maintenance of the system(s): In data management, systems maintenance refers to the activities and tasks carried out to ensure that the systems used for collecting, storing, and managing data are functioning correctly and efficiently. This can include tasks such as monitoring the performance of the systems, identifying and fixing any issues or bugs, upgrading the systems to improve their capabilities, and backing up data to prevent data loss. The maintenance of systems is an essential aspect of data management, as it helps organisations ensure the reliability and availability of their data systems. This, in turn, can help organisations derive value from their data and make the most of their data assets. Additionally, regular maintenance of systems can help prevent disruptions to the data management process and ensure the smooth operation of business operations. Organisations need the necessary resources to effectively maintain data systems, such as skilled personnel and technology and robust policies and procedures. They also need to clearly understand the systems being used, their capabilities, and any potential issues or challenges that may arise. Regular monitoring and maintenance of systems can help organisations ensure the ongoing success of their data management processes.
Factors in managing and securing data
Managing and securing data is a critical aspect of data management. It involves various activities and processes to ensure data quality, integrity, and security. Multiple factors can affect the success of data management and security efforts, including the nature of the data being collected and managed, the systems and technologies used, and the policies and procedures in place.
Cost of systems
One of the factors to consider in managing and securing data is the cost of the systems and technologies used for data management and security. These costs can include the initial purchase or licence of the systems and ongoing maintenance and support costs. They can also include the cost of upgrading or replacing systems and the cost of training personnel to use the systems effectively.
The cost of data management and security systems is essential for organisations to consider, as it can impact the overall budget and resources available for data management and security efforts. Organisations must carefully evaluate the costs and benefits of different systems and technologies and determine which systems are most suitable for their needs and budget.
In some cases, the cost of data management and security systems may be a significant investment for organisations, especially for large or complex systems. In these cases, organisations may need to carefully assess the potential return on investment and consider alternative solutions that offer better value.
Relationship between data, information and knowledge
In managing and securing data, it is essential to understand the relationship between data, information, and knowledge and how they impact the department or organisation.
Data is raw, unprocessed facts and figures collected from various sources. Data can be in any form, such as numbers, text, images, or audio, and it can be structured or unstructured. Data is the foundation for all information and knowledge, but on its own, it has little value or meaning.
Information is data that has been processed and organised to make it meaningful and valuable. Information can be in the form of reports, summaries, or insights derived from the analysis and interpretation of data. The information allows individuals and organisations to make decisions and take action based on the collected data.
Knowledge is information that has been absorbed, understood, and applied by individuals or groups and used to inform and guide their actions and decisions. Knowledge is not just a collection of facts but the result of experience, learning, and understanding. Knowledge allows individuals and organisations to innovate and adapt to changing circumstances.
Access to resources
Access to resources is an essential factor in managing and securing data. To succeed, data management and security efforts require using various resources, such as skilled personnel, technology, and infrastructure. Organisations need to have access to these resources to collect, store effectively, and manage their data and protect it from unauthorised access, use, disclosure, disruption, modification, or destruction.
Access to the necessary resources is essential for organisations to maintain their data quality, integrity, and security. Without access to the right resources, organisations may be unable to collect, store, and manage their data effectively and efficiently. This can lead to poor-quality data, increased risks to data security, and potential disruptions to business operations.
To ensure access to the necessary data management and security resources, organisations need to have the appropriate budget and allocation of resources. They also need the essential personnel, technology, and infrastructure to support data management and security efforts. Additionally, organisations need to regularly review and assess their resource needs and make adjustments to support the ongoing success of their data management and security efforts.
Training implications for the workforce
The workforce’s training implications are critical in managing and securing data. Ensuring that the workforce has the necessary knowledge, skills, and awareness to manage and secure data effectively is essential for the success of data management and security efforts.
Training the workforce on data management and security can help organisations achieve several benefits. It can help employees understand the importance of data management and security and their role in ensuring data quality, integrity, and security. Training can help employees understand and follow data management and security policies and procedures and identify and report potential issues or concerns.
Additionally, training the workforce on data management and security can help organisations ensure compliance with relevant laws and regulations, such as the General Data Protection Regulation (GDPR), and prevent potential penalties or reputational damage. Training can also help organisations protect their data from security threats and to avoid data breaches or other security incidents.
To effectively train the workforce on data management and security, organisations need to clearly understand their training needs and the skills and knowledge that employees need. They also need to have appropriate training programs and materials in place and ensure that employees can learn and apply their knowledge on the job. Regular training and updates can help organisations ensure that the workforce has the necessary skills and awareness to support the success of data management and security efforts.
Use of systems
Using internal systems, external systems, and outsourcing can impact the security, speed of access to data, and costs of managing and securing data.
Internal systems are systems that are owned and operated by the organisation itself. These systems can give the organisation more significant control over its data and security, as it can directly manage and monitor the systems and implement appropriate security measures. However, internal systems can also be more expensive to maintain and operate and may require the organisation’s technical expertise and resources.
External systems are systems that are owned and operated by third parties. These systems can provide the organisation access to advanced technology and expertise without investing in and maintaining the systems themselves. However, external systems can also introduce additional risks, such as the loss of control over the security of the data and the potential for vendor lock-in.
Outsourcing is contracting with a third party to provide services the organisation would otherwise perform. This can include outsourcing data management and security functions, such as data entry, analysis, and backup. Outsourcing can provide the organisation with access to specialised expertise and resources and can help reduce costs. However, it can also introduce additional risks, such as losing control over the data and potential disruptions to the data management process.
Data protection linked to current legislation, such as the General Data Protection Regulation (GDPR), is a critical factor in managing and securing data. The GDPR is a regulation in EU law on data protection and privacy for all individuals within the European Union (EU) and the European Economic Area (EEA). It applies to all organisations that process the personal data of individuals within the EU and EEA and sets out several principles and rights for individuals regarding their data.
Compliance with the GDPR and other data protection legislation is essential for organisations that collect and process personal data. This can help organisations ensure the security and integrity of their data and protect the rights of individuals concerning their data. It can also help organisations avoid fines and other penalties for non-compliance with data protection legislation.
To comply with GDPR and other data protection legislation, organisations must clearly understand their obligations and individuals’ rights. They also need to implement appropriate technical and organisational measures to protect personal data security and notify individuals and authorities of any data breaches. Regularly reviewing and updating policies and procedures can also help organisations ensure compliance with data protection legislation.