Many CDOs tend to be part business strategist, adviser, data quality steward and all around data management ambassador. Common mandates may includes the following:

• Establishing an organizational data strategy
• Aligning data-centric requirements with available IT and business resources
• Establishing data governance standards, policies and procedures
• Providing advice (and perhaps services) to the business for data dependent initiatives, such as business analytics, Big Data, data quality, and data technologies
• Evangelizing the importance of good information management principles to internal and external business stakeholders
• Oversight of data usage in analytics and Business Intelligence

Regardless of industry, it is common for a Data Management Organization to report up through the CDO. In a more decentralized operating model, the CDO is responsible for the data strategy, but resources that are in IT, operations, or other lines of business execute that strategy. Some DMOs are established initially with the CDO just determining the strategy, and over time other aspects of data management, governance, and analytics are folded under the CDO umbrella as efficiencies and economies of scale are identified.

You might be Interested in Reading of:

• What is IoT – Internet of “Things” ?
• NoSQL Graph Database
• Data Governance Program – Success Criteria
• Big Data Characterized in terms of V’s
• OLAP – ROLAP, MOLAP and HOLAP
• Data Governance Program – Scope
• Data Science – Process and Iterative Phases
• Data Quality – Policy and Metrics
• Reference and Master Data – A Glance

The post CDO – Chief Data Officer appeared first on MDM Team.

Some organizations don’t have the luxury of going through this maturity process. They are forced to mature quickly based on a market shock or new government regulations. In such a case, it is important to proactively address the discomfort associated with the organizational change if it is to be successful and sustainable.

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• What is IoT – Internet of “Things” ?
• NoSQL Graph Database
• Data Governance Program – Success Criteria
• Reference and Master Data Management – Guiding Principles
• Data Handling Ethics – Personal Data PIPEDA Principle and Description
• Data Governance Program – Scope
• RDM – Reference Data Management
• Linux Partitioning – Parted Throwing Warning

The post DMO Alternatives and Design Considerations appeared first on MDM Team.

A stakeholder is any person or group who can influence or be affected by the Data Management program. Stakeholders can be internal to or external to the organization. They include individual SMEs, senior leaders, teams of employees, committees, customers, government or regulatory agencies, brokers, agents, vendors, etc. Internal stakeholders can come from IT, operations, compliance, legal, HR, finance or other lines of business. External stakeholders can be influential, and it is important that their needs be accounted for by the Data Management Organization.

A stakeholder analysis can help the organization determine the best approach to engaging participants in the data management process and leveraging their roles within the operating model. Insight gained from the analysis is also helpful in determining how to best allocate time and other limited resources. The earlier this analysis is conducted, the better, since the more the organization is able to anticipate reactions to change, the more it can plan for them. A stakeholder analysis will help answer questions like:

• Who will be affected by data management?
• How will roles and responsibilities shift?
• How might those affected respond to the changes?
• What issues and concerns will people have?

The analysis will result in a list of stakeholders, their goals and priorities, and why those goals are important to them. Figure out what actions are needed for stakeholders based on the analysis. Pay particular attention to what needs to be done to bring along critical stakeholders, those that can
make or break an organization’s data management success, especially its initial priorities. Consider:

• Who controls critical resources?
• Who could block data management initiatives, either directly or indirectly?
• Who could influence other critical constituents?
• How supportive stakeholders are of the upcoming changes?

Involve the Stakeholders

After identifying the stakeholders and a good Executive Sponsor, or a short list from which to choose, it is important to clearly articulate why each of the stakeholders should be involved. They may not jump at the chance. The person or team driving the data management effort should articulate the reasons each stakeholder is necessary to the success of the program. This means understanding their personal and professional goals, and being able to link the output from data management processes to their goals, so they can see a direct connection. Without an understanding of this direct connection, they may be willing to help out in the short term, but they will not provide long-term support or assistance.

Click Here for the Post: Build the Data Management Organization – Part 01

You might be Interested in Reading of:

• Data Centric Organization
• Data Quality – Improvement
• Data Governance Committees / Bodies
• Data Governance – How to Set a Strong Foundation?
• Data Governance Readiness Assessment
• How to Measure the Value of Data?
• Data Asset Accounting Principles

The post Build the Data Management Organization – Part 02 appeared first on MDM Team.

When implementing the operating model, start with teams already engaged in data management activities. This will minimize the effect on the organization and will help to ensure that the focus of the team is data, not HR or politics.

Start by reviewing existing data management activities, such as who creates and manages data, who measures data quality, or even who has ‘data’ in their job title. Survey the organization to find out who may already be fulfilling needed roles and responsibilities. Such individuals may hold different titles. They are likely part of a distributed organization and not necessarily recognized by the enterprise. After compiling a list of ‘data people,’ identify gaps. What additional roles and skill sets are required to execute the data strategy? In many cases, people in other parts of the organization have analogous, transferrable skill sets. Remember, people already in the organization bring valuable knowledge and experience to a data management effort.

Once an inventory is complete, and people are assigned to the roles, review their compensation and align it with the expectations of data management. Likely, the Human Resources department will get involved to validate the titles, roles, compensation, and performance objectives. Ensure that the roles are assigned to the right people at the right level within the organization, so that when they are involved in decision making, they have the credibility to make decisions that stick.

Identify Committee Participants

No matter which operating model an organization chooses, some governance work will need to be done by a Data Governance Steering Committee and by working groups. It is important to get the right people on the Steering Committee and to use their time well. Keep them well informed and focused on the ways that improved data management will help them reach business objectives, including strategic goals.

Many organizations are reluctant to start yet another committee since there are so many already existing. It is often easier to take advantage of existing committees to advance data management topics than it is to start a new one. But take this route cautiously. The main risk in using an existing committee is that data management may not get the attention it requires, especially in the early stages. The process to staff either a senior steering committee or a more tactical working group requires conducting stakeholder analysis and, through that, identifying executive sponsors.

Client Here to Continue to: Build the Data Management Organization – Part 02

You might be Interested in Reading of:

• What is Content and Content Management?
• Data Quality – Improvement
• Data Governance Committees / Bodies
• Reference and Master Data Management – Guiding Principles
• Data Governance Readiness Assessment
• Data Steward – Stewardship Activities
• RDM – Reference Data Management

The post Build the Data Management Organization – Part 01 appeared first on MDM Team.

You might be Interested in Reading of:

• Data Management – What is Master Data?
• Data Quality – Improvement
• Data Quality Rules
• Reference and Master Data Management – Guiding Principles
• Data Governance Readiness Assessment
• Data Asset Accounting Principles
• RDM – Reference Data Management

The post Critical Success Factors – Part 02 appeared first on MDM Team.

• Learning to manage on the horizontal by aligning accountabilities along the Information Value chain
• Changing focus from vertical (silo) accountability to shared stewardship of information
• Evolving information quality from a niche business concern or the job of the IT department into a core value of the organization
• Shifting thinking about information quality from ‘data cleansing and scorecards’ to a more fundamental organizational capability
• Implementing processes to measure the cost of poor data management and the value of disciplined data management

This level of change is not achieved through technology, even though appropriate use of software tools can support delivery. It is instead achieved through a careful and structured approach to the management of change in the organization. Change will be required at all levels. It is critical to manage and coordinate change to avoid dead-end initiatives, loss of trust, and damage to the credibility of the information management function and its leadership.

Data management professionals who understand formal change management will be more successful in bringing about changes that will help their organizations get more value from their data. To do so, it is important to understand:

• Why change fails
• The triggers for effective change
• The barriers to change
• How people experience change

You might be Interested in Reading of:

• Data Warehouse, Data Lake & Data Vault
• NoSQL Column-Oriented Database – Columnar Database
• Data Modeling – Metrics and Checklist
• What is Content and Content Management?
• Data Governance Readiness Assessment
• Data Asset Accounting Principles
• DMMA – Data Management Maturity Assessment

The post Data Management and Organizational Change Management – Part 01 appeared first on MDM Team.

In a decentralized model, data management responsibilities are distributed across different lines of business and IT. Collaboration is committee-based; there is no single owner. Many Data Management programs start as grass root efforts to unify the data management practices across an organization and therefore have a decentralized structure.

The benefits of this model include its relatively flat structure and its alignment of data management to lines of business or IT. This alignment generally means there is a clear understanding of data requirements. It is also relatively easy to implement or improve.

The drawbacks include the challenge of having many participants involved with governance bodies and in decision-making. It is generally harder to implement collaborative decisions than centralized edicts.

Decentralized models are generally less formal and because of this, they can be harder to sustain over time. To be successful, they need to have ways to enforce consistency of practices. This can be difficult to coordinate. It is also often difficult to define data ownership with a decentralized model.

• Network Operating Model

Decentralized informality can be made more formal through a documented series of connections and accountabilities via a RACI (Responsible, Accountable, Consulted, and Informed) matrix. This is called a networked model because it operates as a series of known connections between people and roles and can be diagrammed as a ‘Network’.

A network model’s benefits are similar to those of a decentralized model (flat structure, alignment, quick set up). The addition of a RACI helps create accountability without impacting the organizational charts. The additional drawback is the need to maintain and enforce expectations related to the RACI.

• Centralized Operating Model

The most formal and mature data management operating model is a centralized one. Here everything is owned by the Data Management Organization. Those involved in governing and managing data report directly to a data management leader who is responsible for Governance, Stewardship, Metadata Management, Data Quality Management, Master and Reference Data Management, Data Architecture, Business Analysis, etc.

The benefit of a centralized model is that it establishes a formal executive position for data management or data governance. There is one person at the top. Decision-making is easier because accountability is clear. Within the organization, data can be managed by type or subject area. The drawback is that implementation of a centralized model generally requires significant organizational change. There is also a risk that formal separation of the data management role moves it away for core business processes and can result in knowledge being lost over time.

A centralized model generally requires a new organization. The question arises: Where does the Data Management Organization fit within the overall enterprise? Who leads it and to whom does the leader report? It is becoming more common for a Data Management Organization not to report to the CIO because of the desire to maintain a business, rather than IT, perspective on data. These organizations are also commonly part of a shared services or operations team or part of the Chief Data Officer’s organization.

• Hybrid Operating Model

As its name implies, the hybrid operating model encompasses benefits of both the decentralized and centralized models. In a hybrid model, a centralized data management Center of Excellence works with decentralized business unit groups, usually through both an executive steering committee representing key lines of business and a set of tactical working groups addressing specific problems.

In this model, some roles remain decentralized. For example, Data Architects may stay within an Enterprise Architecture group; lines of business may have their own Data Quality teams. Which roles are centralized and which stay decentralized can vary widely, depending largely on organizational culture.

The primary benefit of a hybrid model is that it establishes appropriate direction from the top of the organization. There is an executive accountable for data management and/or governance. Business Unit teams have broad accountability and can align to business priorities to provide greater focus. They benefit from the support of a dedicated data management Center of Excellence that can help bring focus to specific challenges.

The challenges include getting the organization set up, since doing so generally requires additional headcount to staff a Center of Excellence. Business Unit teams may have different priorities, and these will need to be managed from an enterprise perspective. In addition, there are sometimes conflicts between the priorities of the central organization and those of the decentralized organizations.

• Federated Operating Model

A variation on the hybrid operating model, the federated model provides additional layers of centralization / decentralization, which are often required in large global enterprises. Imagine an enterprise Data Management Organization with multiple hybrid data management models delineated based on division or region.

A federated model provides a centralized strategy with decentralized execution. Therefore, for large enterprises it may be the only model that can work. A data management executive who is accountable across the organization runs the enterprise Center of Excellence. Of course, different lines of business are empowered to meet requirements based on their needs and priorities. Federation enables the organization to prioritize based on specific data entities, divisional challenges, or regional priorities.

The main drawback is complexity. There are a lot of layers, and there needs to be a balance between autonomy for lines of business and the needs of the enterprise. This balance can impact enterprise priorities.

For Your Further Reading:

• Organization / Enterprise Ontology and Taxonomy
• Data Management Organizational Constructs and Operating Models – Part 01
• Data Security Standards
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?
• Data Governance Readiness Assessment

The post Data Management Organizational Constructs and Operating Models – Part 02  appeared first on MDM Team.

A reliable operating model helps create accountability by ensuring the right functions within the organization are represented. It facilitates communication and provides a process to resolve issues. While it forms the basis for the organizational structure, the operating model is not an Organization-Chart – it is not about putting names in boxes, but about describing the relationship between the component pieces of the organization.

There are basically, five following Operating Models and each one has it’s own Pros-n-Cons:

• Decentralized Operating Model
• Network Operating Model
• Centralized Operating Model
• Hybrid Operating Model
• Federated Operating Model

Identifying the Best Model for an Organization

The operating model is a starting point for improving data management and data governance practices. Introducing it requires an understanding of how it may impact the current organization and how it will likely need to evolve over time. Since the operating model will serve as the structure through which policies and processes will be defined, approved, and executed, it is critical to identify the best fit for an organization.

Assess whether the current organizational structure is centralized, decentralized, or a combination, hierarchical or relatively flat. Characterize how independent divisions or regions are. Do they operate almost self-sufficiently? Are their requirements and goals very different from each other? Most importantly, try to determine how decisions are made (e.g., democratically or by fiat), as well as how they are implemented.

The answers should give a starting point to understand the organization’s location on the spectrum between decentralized and centralized.

Whichever model is chosen, remember that simplicity and usability are essential for acceptance and sustainability. If the operating model fits the culture of a company, then data management and proper governance can be embedded in operations and aligned with strategy. Keep these tips in mind when constructing an Operating Model:

• Determine the starting point by assessing current state Tie the operating model to organization structure
• Take into account:
  • Organization Complexity + Maturity
  • Domain Complexity + Maturity
  • Scalability
• Get executive sponsorship – a MUST for a sustainable model
• Ensure that any leadership forum (steering committee, advisory council, board) is a decision-making body
• Consider pilot programs and waves of implementation
• Focus on high-value, high-impact data domains
• Use what already exists
• Never take a One-Size-Fits-All approach

For Your Further Reading:

• Organization / Enterprise Ontology and Taxonomy
• Data Management – Key Components and Knowledge Areas
• Data Security Standards
• Data Steward – Stewardship Activities
• Data Modeling – Metrics and Checklist
• How to Measure the Value of Data
• What is Content and Content Management?

The post Data Management Organizational Constructs and Operating Models appeared first on MDM Team.

• Does the organization have the information it needs to make sound, timely business decisions?
• Does the organization have confidence in its revenue reports?
• Can it track the organizational key performance indicators?
• Is the organization in compliance with all laws regarding management of data?

Most organizations that seek to improve their data management or governance practices are in the middle of the capability maturity scale (i.e., they are neither 0’s nor 5’s on the CMM scale). To craft a relevant Data Management Organization, it is important to understand and accommodate the existing company culture and organizational norms. If the Data Management Organization is not aligned to the existing decision-making and committee constructs, it will be challenging to sustain it over time. Therefore, it makes sense to evolve these organizations, rather than imposing radical changes.

A Data Management Organization should align with a company’s organizational hierarchy and resources. Finding the right people requires an understanding of both the functional and the political role of data management within an organization. The aim should be cross-functional participation from the various business stakeholders. To accomplish this:

• Identify Employees currently performing data management functions; recognize and involve them first. Hire additional resources only as data management and governance needs grow.
• Examine the Methods the organization is using to manage data and determine how processes can be improved. Determine how much change is likely to be required to improve data management practices.
• Roadmap the Kinds of Changes that need to take place from an organizational perspective to better meet requirements.

Previous …

For Your Further Reading:

• Organization / Enterprise Ontology and Taxonomy
• What is Master Data?
• Data Security Standards
• Data Warehouse, Data Lake & Data Vault
• Data Centric Organization
• NoSQL Column-Oriented Database – Columnar Database
• Data Modeling – Metrics and Checklist

The post Understand Organization and Cultural Norms – 02 appeared first on MDM Team.

Before defining any new organization or attempting to improve an existing one, it is important to understand current state of component pieces, related to culture, the existing operating model, and people.

• The role of data in the organization: What key processes are data-driven? How are data requirements defined and understood? How well-recognized is the role that data plays in organizational strategy?
• Cultural norms about data: Are there potential cultural obstacles to implementing or improving management and governance structures?
• Data Management and Data Governance practices: How and by whom is data-related work executed? How and by whom are decisions about data made?
• How work is organized and executed: For example, what is the relation between project-focused and operational execution? What committee structures are in place that can support the data management effort?
• How reporting relationships are organized: For example, is the organization centralized or decentralized, hierarchical or flat?
• Skill levels: What is the level of data knowledge and data management knowledge of SMEs and other stakeholders, from line staff to executives?

Continue …

For Your Further Reading:

• Data Governance Readiness Assessment
• What is Data Governance?
• Data Governance – Framework & Pillars
• Data Governance – How to Set a Strong Foundation?
• Understand Organization and Cultural Norms
• Data Centric Organization

The post Understand Organization and Cultural Norms appeared first on MDM Team.