play a critical role in helping organizations use their to their fullest potential. They provide direction and structure for how an organization can collect, store, and utilize data while ensuring compliance with laws and regulations.
Here are the key takeaways from this article:
- Strategic plans and controls are necessary to provide fruitful governance efforts.
- The exploration phase involves creating a data governance council to implement governance in critical areas.
- The expansion phase is focused on scaling up the efforts to management.
- The transformation phase the governance program per the needs.
In this article, we will uncover severalavailable. By understanding these frameworks and the offerings they bring, organizations can make informed decisions on how to create a that works for them.
Table of Contents
- DGI Data Governance Framework
- McKinsey–Designing data governance that delivers value
- BCG Data Governance Framework
- Eckerson Group–The Path to Modern Data Governance
- PwC Enterprise Data Governance Framework
- SAS Data Governance Framework: A Blueprint for Success
- Data Governance With Oracle
- Which Data Governance Framework Should I Follow?
- Data Governance with Integrate.io
Indeed, around 75% of US adults believe that there should be more regulation around , while 28% face identity theft issues.
It is no surprise that these sentiments are the driving force for increasing data regulations such as General Data Protection Regulation (). Such privacy laws may distort an and storage processes, reducing the number of available.
Further, Gartner reports that organizations incur a cost of USD 12.9 million annually due to low-quality data.
The Data Governance Institute (DGI) defines data governance as exercising and authority over data-related issues. As such, the goals of data governance include improving , reducing operational inefficiencies, protecting , reducing costs, and training staff while building transparent processes and standards.
Value Statement: the framework should have a clear value statement highlighting the framework's mission, vision, and.
Goals and: Organizations should create goals that support the value statement and the relevant to measure them.
Data Rules: Organizations should develop appropriate policies,, , and compliance requirements.
Decision Rights: Organizations should also determine who makes the decisions regarding data rules. Should it be more autocratic, or should it involve different teams in the company? All of this will form theof decision rights.
Accountability: Next, theshould clearly define who is accountable for what activities to data . The framework should then address issues regarding risk management.
Controls: Proper standards should govern, storage, , and flow.
People: Then comes the people involved in the program. They may include data, stewards, and a data governance office that enforces and facilitates the governance efforts.
Processes: Lastly, the program should have a clear set of standardized, documented, and repeatable processes to support, , and .
The DGI framework is quite comprehensive in its recommendations and takes a pragmatic approach to build a governance program from the ground up. This is beneficial for organizations developing a framework from scratch.
McKinsey–Designing data governance that delivers value
McKinsey's focuses more on organizing people correctly rather than processes or technology. Essentially, McKinsey suggests that an effective program requires buy-in from the senior executive team.
The governance structure consists of aoffice (DMO), a data council, and data leadership by domain.
Office: The Chief Data Officer (CDO) heads the DMO to develop the overall and direction of the . In addition, The DMO is responsible for devising and standards, providing with training and , coordinating efforts along the , and facilitating the resolution of issues.
Data Leadership By Domain: The data leadership by domain consists ofand subject-matter experts who execute the policies and standards developed by the DMO. They are responsible for managing , developing , and meeting the needs of the data consumers.
Data Council: Finally, the data council acts as a liaison between the DMO and domain leaders, tying the strategies withand defining the structure of the DMO. It also enforces the standards and monitors the progress of governance efforts while approving funding requests.
Best Practices: McKinsey suggests some best. These include integrating governance efforts with significant business projects, prioritizing certain , and developing an iterative program that balances and regulation.
Mckinsey’s framework is more suited to larger enterprises that may already have the people with the relevant expertise. They can then use the framework to organize the people correctly.
The Boston Consulting Group (BCG) governance framework consists of four core components. Data Structures, Policies, Tools, and Data Organization Participants and Target Operating Model (TOM).
Data Structures: Like the frameworks above, data structures involve developing, models, and flows to create a standard set of for ensuring data consistency across . More specifically, data structures involve a comprehensive data glossary containing the definition of each data term, data domain, and for maintaining and , along with for tracking .
Data Policies: Data Policies include developing standards for ensuringand security.
Data Tools: Also, organizations must figure out the right data tools and applications todata governance procedures. They may initially use simple tools like spreadsheets. But as the company expands, more sophisticated tools become necessary.
Data Organization Participants: Finally, like McKinsey's model, data organization participants include a DMO headed by the CDO and, owners, and custodians.
TOM: The TOM can either be a fully centralized model or a completely decentralized one. However, BCG suggests organizations start with a federated model where the CDO acts as the "doer," directly managing data operations. However, as data governance matures, the CDO can take a more facilitating role where the domain teams have full data ownership.
BCG’s framework is quite similar to DGIs, but not as comprehensive. However, the TOM component makes the BCG framework stand out. Organizations already at some stage of building the governance process can take guidance from the framework to move forward in the right direction.
The DAMA defines a comprehensive governance framework consisting of 10 functions. DAMA- DMBOK defines a Book of Knowledge (DAMA-DMBOK) framework within which data governance forms one of the functions.
Data Governance: In principle, data governance is central in the framework around nine other functions. Data governance, in turn, is defined as the control and planning of.
Control deals with supervising staff,, coordinating data governance activities, etc. At the same time, planning involves creating data policies, establishing , and value.
Data Architecture Management: The second function covers the development and maintenance of, architecture, architecture, and business taxonomies.
Data Development: the data development function deals with creating, data design in databases, , etc., quality management in the form of standards, and data implementation related to deployment issues.
Database Operations Management: Next in line is database operations management, which involves designing the entirefor both structured and data, from the phase to the data archival or disposal phases.
: management component addresses the issue of planning and developing security standards, managing users, passwords, procedures, etc.
Reference and(MDM): Reference and MDM consist of understanding, identifying, developing, and implementing the architecture of master to establish a clean version of the master data for accurate referencing.
and (BI): Next comes and BI management, which may involve creating a data that supports easy analysis and querying of .
Documentation,, and Management: The eighth is managing documentation and content, whereas the ninth deals with managing . Finally, the tenth function includes developing to measure and improve the .
The DAMA-DMBOK focuses more on overallthan just data governance. Organizations that want to combine their efforts with data governance organically can refer to this framework for guidance.
Eckerson Group–The Path to Modern Data Governance
The Eckerson Group consists of six layers with a total of 36 components. The layers relate to goals, methods, people, processes, technology, and culture.by the
Goals: The goals layer addresses issues like developing the goals of the governance program, establishing theto measure them, and building measurement processes.
Methods: The methods layer includes reviewing policies, implementing guides, coaching sessions, and designing guardrails.
People: The people's layer concerns identifying and engaging with sponsors, including the executive team,, , data consumers, and other data .
Processes: The processes layer involves efforts tomanagement processes. Also, it consists in developing protocols and defining to measure the success of data governance.
Technology: The technology layer covers the tools used for data governance. These tools can be third-party applications that help implement aapproach to data governance.
Culture: Finally, the culture layer involves identifying and implementing the cultural attributes that would facilitate making data governance a success.
The benefit of Eckerson’s framework lies in its simplicity and versatility. This is because organizations can choose which components to address within each layer and combine components from different layers to build their programs to suit their culture.
The PwCs consists of five broad components. These are , , management, , core functions, and data governance enablers.
: Like the frameworks above, the framework deals with defining the and establishing corresponding to measure progress.
: The component emphasizes the need for developing systems for managing , , , , etc.
: management is the as it flows through collection till archival or disposal.
: The and core functions cover managing and supervising day-to-day activities for maintaining , lineage, and security.
Data Governance Enablers: Data governance enablers identify the gaps in the current dataand assess the current level of maturity. Organizations can then work on creating a tailored solution that brings value to the business.
PWC’s framework does not go into the granularity of data governance. As such, this framework is suited to organizations where data governance already exists in some form. They can mold their existing programs according to PWC’s principles for more effectiveness.
SAS: A Blueprint for Success
The SAS takes a holistic and pragmatic approach, covering almost every aspect of while considering the challenges organizations encounter when implementing a governance program.
Corporate Drivers: For starters, the Corporate Drivers part is the top component of the framework and says that the governance framework should address significant business problems.
Data Governance: The data governance component consists of four sub-components: Program Objectives, Guiding Principles,Bodies, and Decision Rights.
Program Objectives: Program Objectives deal with identifying the framework's goals that align with corporate objectives and establishingfor measurement.
Guiding Principles: Guiding Principles drive the framework's direction to ensure the program supports the company's culture and.
Bodies: The bodies include the relevant who actively participate in deciding the principles and standards for data.
Decision Rights: Finally, the Decision Rights sub-component defines who is responsible and accountable for data governance activities.
and Management: The other significant components are and . They involve managing day-to-day activities around , architecture, security, , and the overall .
People, Processes, and Technology: Then come the components of People, Processes, and Technology, which deal with finding the right people to define measurable policies and implement them through appropriate technological solutions. The solutions, in turn, can be applications that help in data preparation, data monitoring, data, , etc.
The SAS framework is, again, pretty comprehensive. The advantage of using this framework is that it helps tie data governance efforts to corporate goals rigorously. Organizations can use this to get executive buy-in for.
Data Governance With Oracle
Oracle's considers the current maturity and takes a phased approach to governance.
Goals: The starting point is the development of governance goals that include defining policies, strategies, andand tracking compliance with the defined standards.
Deliverables: Data governance deliverables include policies, standards, resolved issues,data, and a sense of value around the .
Focus Areas: Also, the framework suggests four focus areas around which organizations need to design their policies.
Policies and Standards: First is a core focus on policies and standards. In this case, the aim is to implement anarchitecture and break departmental to create a shared environment. However, the focus can be on . In this case, the aim is to the of among teams.
Privacy and Compliance: Other areas include a focus on privacy and compliance. Here, the governance framework comes out as a need to address data regulations.
Data Architecture: In contrast, focusing on data architecture emphasizes increasing operational efficiency through data.
(BI): Lastly, the framework can focus on BI systems to ensure that the new project provides value.
Strategic Plan and Controls: Once an organization sets a focus area, it should develop a strategic plan while implementing control mechanisms to ensure fruitful governance efforts. This step should include creatingto measure data value, achievement of targets, costs, etc.
Exploration Phase: After this, the organization should start with an exploration phase by establishing a data governance council to implement governance in critical areas.
Expansion Phase: Next comes the expansion phase, where the focus is to scale up the efforts tomanagement.
Transformation Phase: Last comes the transformation phase, where the focus is tothe governance program per the needs.
Oracle’s phased approach to data governance makes the framework suitable for organizations with some already existing frameworks. Once they determine their maturity, organizations can define a focus area and then explore, expand, and transform accordingly.
WhichShould I Follow?
Data governance will become a core competency soon, and the above frameworks offer different ways of developing effective data governance. However, there is no one ideal framework. The question of which framework an organization should choose comes down to what it requires and where it currently stands.
Suppose the organization is concerned about data regulations. In that case, it should choose a framework with a strong focus on processes that provides clear guidance on developing policies and standards. However, a framework that offers clearmanagement guidelines might be helpful for organizations that want to use its for projects.
So identifying the right platform is a matter of which problem a business is trying to solve. Of course, the nature of the business's industry and the type of product or service can have a significant influence.
Data Governance with Integrate.io
No matter the governance framework, it will always include the dimensions of people, processes, and technology. A productive framework would then involve the right technology to ensure process compliance.
One significant step in Extract, Transform and Load (ETL) activities. Integrate.io lets organizations simplify , offering low code transformations while giving them the flexibility to write custom ETL scripts. It also helps in combining different into one and ensures consistency.involves
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