Data Management

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Data Management is an umbrella term that covers everything put in place by an organization to manage information and utilize it to make decisions.¹

A Data Management refers to the Practices, Architectural Techniques, Strategies, and Tools that manage, store, and analyze data throughout its lifecycle. Effective data management ensures that data is accurate, available, and accessible when needed while maintaining security and compliance with relevant regulations.

For the trend of a Data Management that we should follow the sharing knowledge and research from the Gartner Hype Cycle for Data Management. You should to know that a data management strategy can change and shift everytime because it depend on a data service or technology that was implemented by your organize.

Gartner Hype Cycle for Data Management 2023

Note

A Modern Data Management involves a set of practices, techniques, and technologies used to handle data as a valuable resource. Its aim is to ensure the availability, integrity, security, and usability of data within an organization.²

Getting Started

The buzz word that has taken over pretty much every industry and large organization in the last decade is "Data-Driven". You can find millions of articles, studies or blogs out there on why you should adopt a data-driven culture in your organization. You can identify problems and opportunities faster than you could without data. You can analyze past and sometimes even real-time data to make informed decisions about the future. Ultimately when you're making an important decision, you want as much information and context as you can possibly get, so you can make the decision confidently.

Being able to make data-driven decisions gives you an incredibly strong competitive advantage in the marketplace, because very few organizations are. Building a data-driven business is difficult but if you pull it off, it’s way easier to become a leader in your market because you can leverage information your competitors just don’t have.

And on the flip side of that, if you lack any type of data management in your company - good luck staying competitive, it’s probably not going to happen.

Note

In this New Vantage Partners report from 2022 on The State of Corporate Data Initiatives, it states that only 26.5% of organizations have achieved their data-driven goals.

So if you're looking to get ahead and stay ahead, becoming data-driven should 100% be part of your plan. But building an effective data-driven culture only works when it’s built on strong data management foundations.

3 Pillars of a Strong Data Management Strategy:

Data Governance
Data Architecture
Data Quality

Strategies

Data Governance

Data Governance refers to all the organizational rules that are put in place around data, including roles and responsibilities of team members, access control, policies, procedures and standards.

Everything that will enforce a high quality of data securely flowing through the organization. Governance is typically documented but looks different for every company depending on company size, what your systems are, and what types of data you're dealing with.

Includes policies, procedures, and standards that ensure the appropriate use, management, and protection of data throughout its lifecycle. It also involves establishing roles and responsibilities for data management, as well as ensuring compliance with legal and regulatory requirements.

Policies and Standards: Establishing clear policies for data usage, privacy, and security.
Data Stewardship: Assigning responsibilities to ensure data quality and compliance.

Data Quality and Consistency

Having accurate and up-to-date data is an essential part of being data-driven. All systems need to be monitored and data needs to be cleaned very carefully. The data you receive in reports needs to reflect what reality is, which is the whole point of being data driven, so the quality of the data is super important.

Ensuring that data is accurate, complete, and consistent. It includes defining data quality metrics, establishing data quality rules, and implementing data profiling and cleansing tools and techniques.

Data Cleansing: Regularly cleaning data to remove inaccuracies and inconsistencies.
Data Validation: Implementing processes to ensure data accuracy and reliability.

Data Integration & Transformation

Consolidating data from multiple sources into a single, unified view of the data. It includes selecting appropriate data integration tools, defining data mapping and transformation rules, and establishing data synchronization and replication protocols.

ETL/ELT Processes
Batch and Stream Processing
Real-Time Data Integration
Data Transformation Techniques

Read More about Data Integration & Data Transformation

Data Lifecycle Management

Archiving and Retention: Implementing policies for data archiving and retention based on data usage and legal requirements.
Disposal: Securely disposing of data that is no longer needed.

Data Security and Privacy

This involves ensuring the confidentiality, integrity, and availability of data. It includes establishing data security policies and procedures, implementing access controls and encryption, and complying with legal and regulatory requirements.

Advanced Encryption: Using cutting-edge encryption techniques to protect data.
Regulatory Compliance: Ensuring adherence to global data protection regulations like GDPR, CCPA, and others.
Zero Trust Architecture: Implementing security models that verify every access request as if it originated from an open network.
Data Encryption and Masking
Access Control and Authentication
Compliance and Regulatory Requirements
Privacy-Preserving Data Processing

Data Architecture

The next important pillar is Data Architecture. This is the actual design of the systems involved in how data flows through your company and gets where it needs to be, when it needs to be there. Broadly speaking this includes your infrastructure which could be in the cloud or big server rooms at your company and all the applications running on those servers which are responsible for making the data flow from place to place. It’s the hardware and software.

Data Democratization

Self-service Analytics: Providing tools and platforms that enable non-technical users to access and analyze data.
Data Literacy Programs: Promoting data literacy across the organization to empower employees to make data-driven decisions.

Data Storage

Data Storage on this part we will focus on below list:

	EDW	ODS	DM
Purpose	Serves the entire organization	Supports operational reporting	Serves a specific business unit/department
Data Integration	Integrates data from multiple sources	Integrates real-time data from transactional systems	Integrates data from a specific subject area
Data Model	Top-down approach to design	Bottom-up approach to design	Designed based on specific business requirements
Complexity	More complex and time-consuming to design and implement	Less complex and quicker to implement	Less complex and quicker to implement
Query and Analysis	Supports complex queries and analytics	Supports operational reporting and simple analysis	Optimized for querying and reporting on a specific subject area
Data Volume	Large volume of historical data	Real-time or near-real-time data	Smaller volume of data
Users	Business analysts, executives, data scientists	Operational staff, business analysts	Business analysts, departmental staff
Cost	Higher cost due to complexity and scale	Lower cost due to simpler design and implementation	Lower cost due to simpler design and implementation

Criteria	EDW	ODS	DM
Scope	Enterprise-wide	Operational	Departmental or functional
Data sources	Multiple internal and external sources	Multiple operational sources	EDW, ODS, or other sources
Data integration	High degree of integration and standardization	Moderate degree of integration and standardization	Low degree of integration and standardization
Data granularity	Mixed levels of granularity	Low level of granularity (detailed)	High level of granularity (aggregated or summarized)
Data currency	Historical and current data	Near real-time or real-time data	Historical and current data
Data quality	High quality (cleansed and validated)	Moderate quality (some cleansing and validation)	High quality (cleansed and validated)
Data structure	Relational or dimensional models	Relational models	Dimensional models
Data volume	Very large (terabytes or petabytes)	Large (gigabytes or terabytes)	Small or medium (megabytes or gigabytes)
Query performance	Moderate to high (depends on indexing and partitioning)	Low to moderate (depends on updates and concurrency)	High (optimized for analysis)
Query complexity	High (supports complex and ad-hoc queries)	Low to moderate (supports simple and predefined queries)	Moderate to high (supports complex and ad-hoc queries)
Query frequency	Low to moderate (periodic or on-demand)	High (continuous or near-continuous)	Moderate to high (periodic or on-demand)
User types	Analysts, managers, executives, data scientists, etc.	Operational staff, managers, etc.	Analysts, managers, etc.

Conclusion

Overall, a Data Management strategy is a comprehensive approach to managing data that includes a range of components designed to ensure the effective use, management, and protection of data throughout its lifecycle.