Chapter 12
What to do Now

Whether you have read the detail or skimmed the headings, at the point we hope you have a better understanding of the assertion in the Introduction that reliable data is not produced by accident. We have tried to show that well-managed data depends on planning, governance, and commitment to quality and security, as well as on disciplined execution of ongoing data management processes.

This chapter will discuss steps that are critical steps to initiate improvements to organizational maturity around data management. These include:

• Assessing current state
• Understanding options for improvement in order to develop a roadmap for data management
• Initiating an Organization Change Management program to support execution of the roadmap

Assess current state

The first step to solving a problem is understanding it. Before defining any new organization or attempting to improve an existing one, it is important to understand current state of component pieces, especially as these relate to culture, the existing operating model, and people. While the specifics of cultural change will differ from organization to organization, assessment of current state focused on improving data management will need to account for:

• 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? In what ways is the organization aware of the costs of poor quality data?
• Cultural norms about data: Are there potential cultural obstacles to implementing or improving data management and governance structures? Are upstream business process owners aware of the downstream uses of their data?
• 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: What is the relation between project-focused and operational execution? What committee structures are in place that can support the data management effort? What is the operating model for IT/Business interactions? How are projects funded?
• Reporting relationships: Is the organization centralized or decentralized, hierarchical or flat? How collaborative are teams?
• Skill levels: What is the level of data knowledge and data management knowledge of Subject Matter Experts (SMEs) and other stakeholders, from line staff to executives?

Assessment of the current state should also include the level of satisfaction with the current state. This will provide insight into the organization’s data management needs and priorities. For example:

• Decision-making: Does the organization have the information it needs to make sound, timely business decisions?
• Reporting: Does the organization have confidence in its revenue reports and other critical data?
• Key Performance Indicators (KPIs): How effectively does the organization track its KPIs?
• Compliance: Is the organization in compliance with all laws regarding management of data?

The most effective means to conduct such an assessment is by using a reliable data management maturity model that will provide insight into both how the organization compares with other organization and guidance on next steps.49

As described in Chapter 3, maturity models define five or six levels of maturity, each with its own characteristics, that span from non-existent or ad hoc to optimized or high performance.

The following generic summary of macro states of data management maturity illustrates the concept. A detailed assessment would include criteria for broad categories like people, processes and technology; and for sub-categories like strategy, policy, standards, role definition, technology / automation, etc. within each data management function or knowledge area.

• Level 0: No Capability: No organized data management practices or formal enterprise processes for managing data. Very few organizations exist at a Level 0. This level is acknowledged for purposes of definition.
• Level 1 Initial / Ad Hoc: General purpose data management using a limited tool set, with little or no governance. Data handling is highly reliant on a few experts. Roles and responsibilities are defined within silos. Each data owner receives, generates, and sends data autonomously. Controls, if they exist, are applied inconsistently. Solutions for managing data are limited. Data quality issues are pervasive and not addressed. Infrastructure supports are at the business unit level. Assessment criteria may include the presence of any process controls, such as logging of data quality issues.
• Level 2 Repeatable: Emergence of consistent tools and role definition to support process execution. In Level 2, the organization begins to use centralized tools and to provide more oversight for data management. Roles are defined and processes are not dependent solely on specific experts. There is organizational awareness of data quality issues and concepts. Concepts of Master and Reference Data Management begin to be recognized. Assessment criteria might include formal role definition in artifacts like job descriptions, the existence of process documentation, and the capacity to leverage tool sets.
• Level 3 Defined: Emerging data management capability. Level 3 sees the introduction and institutionalization of scalable data management processes and a view of data management as an organizational enabler. Characteristics include the replication of data across an organization with some controls in place and a general increase in overall data quality, along with coordinated policy definition and management. More formal process definition leads to a significant reduction in manual intervention. This, along with a centralized design process, means that process outcomes are more predictable. Assessment criteria might include the existence of data management policies, the use of scalable processes, and the consistency of data models and system controls.
• Level 4 Managed: Institutional knowledge gained from growth in Levels 1-3 enables the organization to predict results when approaching new projects and tasks and to begin to manage risks related to data. Data management includes performance metrics. Characteristics of Level 4 include standardized tools for data management from desktop to infrastructure, coupled with a well-formed centralized planning and governance function. Expressions of this level are a measurable increase in data quality and organization-wide capabilities such as end-to-end data audits. Assessment criteria might include metrics related to project success, operational metrics for systems, and data quality metrics.
• Level 5: Optimization: When data management practices are optimized, they are highly predictable, due to process automation and technology change management. Organizations at this level of maturity focus on continuous improvement. At Level 5 tools enable a view of data across processes. The proliferation of data is controlled to prevent needless duplication. Well-understood metrics are used to manage and measure data quality and processes. Assessment criteria might include change management artifacts and metrics on process improvement.

Figure 30 illustrates one way of presenting a visual summary of findings from a DMMA. For each of the capabilities (Governance, Architecture, etc.) the outer ring of the display shows the level of capability the organization has determined it needs to compete successfully. The inner ring displays the level of capability as determined via the assessment. Areas where the distance between the two rings is largest represent the greatest risks to the organization. Such a report can help set priorities. It can also be used to measure progress over time.

The primary goal of a current state assessment is to understand the organization’s starting point in order to plan for improvement. An accurate evaluation is more important than a high score. A formal data management maturity evaluation places the organization on the maturity scale by clarifying specific strengths and weaknesses of critical data management activities. It helps the organization identify, prioritize, and implement improvement opportunities.

In meeting its primary goal, a DMMA can have a positive impact on culture. It helps:

• Educate stakeholders about data management concepts, principles, and practices
• Clarify stakeholder roles and responsibilities in relation to organizational data
• Highlight the need to manage data as a critical asset
• Broaden recognition of data management activities across the organization
• Contribute to improving the collaboration necessary for effective data governance

Based on assessment results, an organization can enhance its data management program so it supports the organization’s operational and strategic direction. Typically, data management programs develop in organizational silos. They rarely begin with an enterprise view of the data. A DMMA can equip the organization to develop a cohesive vision that supports overall organizational strategy. A DMMA enables the organization to clarify priorities, crystalize objectives, and develop an integrated plan for improvement.

Use results to plan for improvement

A current state assessment will help determine what is working well, what is not working well, and where an organization has gaps. Findings provide the basis for road-mapping program goals because they help determined where to start and how quickly to move forward. Goals should focus on:

• High-value improvement opportunities related to processes, methods, resources, and automation
• Capabilities that align with business strategy
• Governance processes for periodic evaluation of organizational progress based on characteristics in the model

The specifics of the action plans will depend on the results of the current state assessment, but an example will show how the process works.

Table 4 presents a very simplified model that accounts only for the adoption of a standard methodology and the degree of automation for the process.

Let’s say an organization recognizes the need to improve the quality of its data. However, its current state assessment shows that it is at Level 1. It has not yet established repeatable practices around data quality measurement, but there are individuals who have tested the waters and figured some things out. Based on its overall strategy, it sets a goal to move from Level 1 to Level 3 within 18 months.

Achieving this goal requires an action plan that accounts for several streams of work:

• Researching approaches for measuring the quality of data and adopting an approach that aligns with the organization’s pain points, measurement goals, and industry
• Training staff on the methodology
• Identifying and adopting tools to support execution of the methodology

In addition to executing plans to meet these goals, leaders should also account for future development (i.e., in moving to Level 3, the organization should also ready itself to move to Level 4).

This simple example shows the thought process around planning for improvement of one component of data management. As noted in Chapter 3, Data Management Maturity Assessments can have different focus areas. If your organization comprehensively evaluates its data management practices, then the output will identify many opportunities for improvement. These will need to be prioritized to support business strategy.

Fortunately, a data management maturity model will include built-in guidance, by describing what progress looks like within and across data management functional areas. The stage-based path to improvement can be tailored to an organization’s needs and priorities.

Initiate organizational change management to support the roadmap

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 maturity scale). Which means almost all of them need to improve their practices.

For most organizations, improving data management practices requires changing how people work together and how they understand the role of data in their organizations, as well as the way they use data and deploy technology to support organizational processes. Successful data management practices require, among other factors:

• 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 of building quality into processes
• 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 realized through a careful and structured approach to the management of change in the organization. Change will be required at all levels. It is critical that it is managed and coordinated so as to avoid dead-end initiatives, loss of trust, and damage to the credibility of the information management function and its leadership.

Cultural change requires planning, training, and reinforcement. Awareness, ownership, and accountability are key to activating and engaging people in data management initiatives, policies, and processes.

Critical success factors for organizational change management are well-known. Ten factors have been consistently shown to play a key role in the success of effective data management organizations, regardless of their structure:

1. Executive sponsorship: The executive sponsor should understand and believe in the initiative. He or she must be able to effectively engage other leaders in support of the changes.
2. Clear vision: Organizational leaders must ensure that all stakeholders who are affected by data management – both internal and external – understand and internalize what data management is, why it is important, and how their work will affect and be affected by it.
3. Proactive change management: Applying organizational change management to the establishment of a data management practice addresses the people challenges and increases the likelihood that desired practices and organizational structures are sustainable over time.
4. Leadership alignment: Leadership alignment ensures that there is agreement on – and unified support for – the need for a data management program and that there is agreement on how success will be defined. Leadership alignment includes both the alignment between the leaders’ goals and the data management outcomes and value and alignment in purpose amongst the leaders.
5. Communication: The organization must ensure that stakeholders have a clear understanding of what data management is and why it is important to the company, what is changing, and what changes in behavior are required.
6. Stakeholder engagement: Individuals, as well as groups, affected by a data management initiative will react differently to the new program and their role within it. How the organization engages these stakeholders – how they communicate with, respond to, and involve them – will have a significant impact on the success of the initiative.
7. Orientation and training: Education is essential to making data management happen. Different groups of people (leaders, data stewards, data owners, technical teams) will require different types and levels of education so they can perform their roles effectively. Many people will require training on new policies, processes, techniques, procedures, and even tools.
8. Adoption measurement: Build metrics around the progress and adoption of the data management guidelines and plan to know that the data management roadmap is working and that it will continue working. The enabling aspect of data management could focus on the improvement of data-centric processes, such as month-end closing, identification of risk, and efficiency of project execution. The innovation aspect of data management could focus on improvement in decision-making and analytics through improved and trusted data.
9. Adherence to guiding principles: Guiding principles, such as DAMA’s principles of data management, serve as the reference points from which all decisions will be made. Establishing them is an important first step in creating a data management program that effectively drives changes in behavior.
10. Evolution not revolution: In all aspects of data management, the philosophy of ‘evolution not revolution’ helps to minimize big changes or large scale high risk projects. Establishing an organization that evolves and matures over time, incrementally improving the way that data is managed and prioritized by business objectives, will ensure that new policies and processes are adopted and behavioral change is sustained.

What you need to know

• Even though data management is complex, it can be executed effectively and efficiently.
• In your leadership role, you can make a significant contribution to your organization’s ability to get value from its data, if you show and share commitment to the process.
• Moving forward starts by being smart about current state: Do an assessment that allows you to understand where you are and plan from there.
• Recognize that changes in how you manage data will change how people work together. Do formal change management to achieve the cultural changes that will bring about success.
• Follow principles and best practices as you clear the way for your organization to get more value from its data.