Data Management
Enterprise data management (EDM): The processes, policies, and technologies used to effectively manage an organisation’s data assets.
Core goals
Ensure data quality
Improve data accessibility
Reduce data management risk
Data management lifecycle (typical stages)
Collection
Storage
Processing
Integration
Analysis
Dissemination
Archiving
Governance and key disciplines
Data governance: Overall management of data availability, usability, integrity, and security.
Data quality: The degree to which data is fit for its intended use and free from errors/inconsistencies.
Master data management (MDM): Maintaining consistent and accurate master data across the organisation.
Data lineage: Tracing data origins and movement to support accuracy, completeness, and consistency.
Data stewardship: Data stewards oversee and coordinate data management across the organisation.
Technical capabilities and approaches
Data integration: Combining data from multiple sources/formats to create a unified view for reporting/analysis.
Data analytics: Using statistical/quantitative methods to extract insights from data.
Data warehousing: A centralised repository designed for reporting and analysis.
Data virtualisation: Real-time access/combination of data without physically moving or replicating it.
Data architecture: Design and structure of data systems (hardware, software, data models).
Data migration: Moving data between systems/locations; requires planning to protect integrity and continuity.
Platforms and technology trends
Cloud-based solutions: Increasingly used for scalability, flexibility, and cost-effectiveness.
Emerging technologies: AI, machine learning, and blockchain are expanding automation and analysis opportunities.
Risk, security, and compliance
Data security: Implement robust measures to prevent unauthorised access, theft, or loss.
Regulatory compliance: Adhere to laws/regulations related to data privacy and security.
Frameworks and best practices
Governance frameworks such as COBIT and DAMA-DMBOK provide guidance and best practices.
Operating model/collaboration
Effective EDM requires cross-department collaboration and communication (IT, business, and data users).
This is a simple explanation of "data management” compared to real estate management, as both require effectively organising, maintaining, and utilising valuable assets.
Let’s walk through each of these comparisons:
Data Asset: At the heart of the analogy lies the data asset, which corresponds to the building or property in real estate management. The data asset can also be perceived as a data product or a dataset. Both data and real estate management revolve around managing assets that generate value when properly governed and nurtured, but that pose risks and losses when mismanaged.
Data (Product) Ownership: A critical concept in data management is ownership — responsibilities may be delegated to others, but at the end of the day, one person or team should be the owner of the data. The same is true for a building, where the property owner or landlord would be the responsible party.
Data Steward: Data stewardship involves assigning responsibility for managing data assets to specific individuals or teams, for example, to ensure data quality. In real estate management, data stewardship is comparable to the role of property managers, who are responsible for the upkeep and maintenance of a property.
Data Consumers / Users: Various individuals and business processes may consume the data, internal and external to the organisation. This can be compared to the tenants that use the building for their respective purposes.
Data Monetisation: Data monetisation involves leveraging data assets to generate revenue, for example, by selling data to other organisations. In real estate management, this would be equivalent to generating income from a property, such as renting space to tenants or for an event, selling advertising space, or selling the property outright.
Data Contract: A data contract is a formal agreement between a data producer and a data consumer that specifies the data to be exchanged and the corresponding formatting and quality requirements. This can be compared to a lease agreement, in which the expectations of the landlord and the condition of the property upon availability are described. It also outlined what the property can be used for (and specifically, what cannot be done to or with it) — the data contract can be used for similar purposes.
Value Quantification: In both cases, it is worthwhile to estimate the asset's value. Just as the value of a property depends on its location, size and condition, the value of data depends on its relevance, accuracy and accessibility.
Data Security and Access Controls: Data security refers to protecting data assets from unauthorised access, use, or disclosure. In real estate management, data security is like using locks, alarms, and security systems to protect a property from theft or vandalism.
Data Architecture: This is like a property's blueprint, defining the layout, design, and construction of the building. Similarly, data architecture involves the design and structure of data storage and retrieval systems. Architecture standards can provide guidelines and best practices for how buildings are constructed, and data architecture standards do the same for data assets.
Data Domains: Just as a city is divided into neighbourhoods, data can be divided into domains based on its subject matter. Any property belongs to a single neighbourhood, and together, all neighbourhoods include all properties; the same holds for data assets and domains. Each neighbourhood has its own characteristics, such as demographics and property values, and similarly, each data domain has its own attributes and requirements. An organisation, such as a homeowners' association (equivalent to data domain owners or stewards), can be chartered to oversee the implementation of these requirements.
Data Policies & Standards and Regulatory Compliance: These policies align with the regulations that govern the use and development of properties, such as zoning laws, environmental regulations, and building and fire codes. Similarly, data policies and standards define the rules for managing data within an organisation and are derived from applicable regulations, such as those on data privacy and protection.
Metadata Management: Metadata is data about data. It describes a data asset by its attributes, ownership, access permissions, access history, location, record count, and total size. It can be compared to detailed information about a property and its features, such as the total square footage and cubic footage, the owner, the number of rooms, its location, and who has keys to the building.
Data Quality: Data quality refers to how well data serves its intended purpose, as measured along dimensions such as accuracy, completeness, and consistency. In real estate management, data quality is comparable to the condition and upkeep of a property, such as whether it has any defects or safety hazards.
Data Remediation: Data remediation refers to the process of identifying and correcting data quality issues. In real estate management, data remediation is comparable to the process of identifying and correcting property defects, such as a leaky roof or a faulty foundation, to maintain a property's value and safety.
Data Usage: This is comparable to measuring property usage, which helps determine its potential value. This includes occupancy rates but perhaps even more detailed logs of who entered the building, when, and for how long. Similarly, data usage measurement involves tracking and measuring how and by whom data is used within an organisation, as well as the extent to which data assets are adopted.
Interoperability: The term interoperability refers to the compatibility of a property with other properties and (upstream or downstream) systems, as well as its ability to share common infrastructure or resources. For example, a building is connected to the electrical grid, water network, and sewage system, where each connection comes with precisely defined standards for voltage, water pressure, pipeline sizes, and sewage. In a similar sense, data interoperability refers to the ability of the asset to exchange data and work together seamlessly with various other systems and applications, subject to common standards.
Data Storage: Data storage can be compared to the physical size and foundational structure of a property. A property might have to be of a certain minimum size, for example, to accommodate industrial machines or to house families of a certain size. Similarly, data storage refers to the physical or virtual storage capacity in databases, data warehouses or data lakes.
Data Lifecycle: The data lifecycle is comparable to the life cycle of a property, which involves stages such as construction, maintenance, renovation, and demolition. Similarly, data lifecycle management involves managing data through various stages, such as creation, storage, use, archiving, and disposal.
Data Integration: Roads and transportation systems connect different properties and neighbourhoods. A particular building may make it easy to access public transport and a nearby highway. Data integration involves connecting data from different domains and sources, including tasks such as data cleansing, data mapping, and data transformation to ensure that data from different systems can be used together. Without integration, you can’t access or use the data; similarly, you wouldn't be able to enter or use a building.
