Data Strategy Development Service
We Develop a Modern and Actionable Data Strategy to enhance business value
A sound Data Strategy is an indispensable tool in a modern company’s business and technology portfolio. We help you to develop a comprehensive and practical Data Strategy that can provide structured approaches to harnessing data for business value, guiding organizations in how they collect, manage, utilize, and protect data.
Our approach involves a detailed assessment of your current data management practices, strategic business goals, technological infrastructure, and digital experience strategy. We work closely with you to identify the most suitable framework that not only addresses your immediate challenges but also sets you up for long-term success. This tailored approach ensures that your Data Strategy becomes a cornerstone of your business success, driving innovation, efficiency, and competitive advantage.
Data Frameworks
Each of these frameworks offers valuable insights and methodologies for organizations looking to leverage their data effectively. The choice of framework often depends on the specific needs, maturity, and strategic goals of the organization. Depending on our customer’s situation we use one of several popular frameworks each with its unique focus and methodology. Here’s a brief description of some of them:
DAMA-DMBOK (Data Management Body of Knowledge)
- Focus: Comprehensive data management
- Description: DAMA-DMBOK is a framework developed by the Data Management Association International (DAMA). It serves as a guide for data management professionals, covering all aspects of data management, including data governance, data architecture, data modeling, data warehousing, data security, data quality, and metadata management. It aims to ensure that data acts as a valuable resource for organizations.
Gartner’s Data & Analytics Framework
- Focus: Integrating data and analytics into business processes
- Description: Gartner’s framework emphasizes the importance of data and analytics as core business functions. It guides organizations in creating a strategy that aligns data analytics with business outcomes, focusing on building a data-driven culture, establishing strong governance, and investing in technology that supports analytics at scale.
TDWI’s Data Strategy Framework
- Focus: Data management and analytics for business insights
- Description: The Data Warehousing Institute (TDWI) offers a framework that focuses on leveraging data as a strategic asset for competitive advantage. It includes guidance on governance, quality, integration, and analytics, aiming to help organizations become more agile, effective, and data-savvy.
The Data Governance Institute’s Framework
- Focus: Data governance
- Description: This framework focuses specifically on data governance, offering a structure for establishing policies, procedures, roles, and responsibilities. It aims to ensure that data is accurate, available, secure, and compliant with regulations, thereby supporting organizational decision-making and performance.
EDM Council’s Data Management Capability Assessment Model (DCAM)
- Focus: Comprehensive assessment of data management capabilities
- Description: DCAM provides a set of standards and best practices for evaluating and improving data management functions. It covers areas such as data governance, data quality, data operations, and data architecture. The framework is designed to help organizations measure their maturity in various aspects of data management and identify areas for improvement.
FAIR Data Principles
- Focus: Making data Findable, Accessible, Interoperable, and Reusable
- Description: Though not a framework in the traditional sense, the FAIR Data Principles guide organizations in managing data in a way that enhances its value. They emphasize the importance of making data easily discoverable, accessible under clear conditions, interoperable for seamless integration with other data, and reusable for future research and applications.
Data Frameworks
Each of these frameworks offers valuable insights and methodologies for organizations looking to leverage their data effectively. The choice of framework often depends on the specific needs, maturity, and strategic goals of the organization. Depending on our customer’s situation we use one of several popular frameworks each with its unique focus and methodology. Here’s a brief description of some of them:
DAMA-DMBOK (Data Management Body of Knowledge)
- Focus: Comprehensive data management
- Description: DAMA-DMBOK is a framework developed by the Data Management Association International (DAMA). It serves as a guide for data management professionals, covering all aspects of data management, including data governance, data architecture, data modeling, data warehousing, data security, data quality, and metadata management. It aims to ensure that data acts as a valuable resource for organizations.
Gartner’s Data & Analytics Framework
- Focus: Integrating data and analytics into business processes
- Description: Gartner’s framework emphasizes the importance of data and analytics as core business functions. It guides organizations in creating a strategy that aligns data analytics with business outcomes, focusing on building a data-driven culture, establishing strong governance, and investing in technology that supports analytics at scale.
TDWI’s Data Strategy Framework
- Focus: Data management and analytics for business insights
- Description: The Data Warehousing Institute (TDWI) offers a framework that focuses on leveraging data as a strategic asset for competitive advantage. It includes guidance on governance, quality, integration, and analytics, aiming to help organizations become more agile, effective, and data-savvy.
The Data Governance Institute’s Framework
- Focus: Data governance
- Description: This framework focuses specifically on data governance, offering a structure for establishing policies, procedures, roles, and responsibilities. It aims to ensure that data is accurate, available, secure, and compliant with regulations, thereby supporting organizational decision-making and performance.
EDM Council’s Data Management Capability Assessment Model (DCAM)
- Focus: Comprehensive assessment of data management capabilities
- Description: DCAM provides a set of standards and best practices for evaluating and improving data management functions. It covers areas such as data governance, data quality, data operations, and data architecture. The framework is designed to help organizations measure their maturity in various aspects of data management and identify areas for improvement.
FAIR Data Principles
- Focus: Making data Findable, Accessible, Interoperable, and Reusable
- Description: Though not a framework in the traditional sense, the FAIR Data Principles guide organizations in managing data in a way that enhances its value. They emphasize the importance of making data easily discoverable, accessible under clear conditions, interoperable for seamless integration with other data, and reusable for future research and applications.
