- Executive Sponsorship
Having the right executive sponsor ensures that stakeholders affected by a Data Management program receive the necessary guidance to transition efficiently and effectively through the changes needed to put the new data focused organization together and sustain it for the long term. 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. - Clear Vision
A clear vision for the Data Management Organization, along with a plan to drive it, is critical to success. 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. - Proactive Change Management
Managing the change associated with creating a Data Management Organization requires planning for, managing, and sustaining change. Applying organizational change management to the establishment of a Data Management Organization addresses the people challenges and increases the likelihood that desired Data Management Organization is sustainable over time. - 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. If leaders are not aligned with each other, they will end up sending mixed messages that can lead to resistance and eventually derail the change. Therefore, it is critical to assess – and regularly re-assess – leaders at all levels to identify disconnects and take steps to quickly address them. - Communication
Communication should start early and continue openly and often. 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. People can’t improve the way they manage data if they don’t know what they are supposed to do differently. Creating a story around the data management initiative and building key messages around it helps these processes.
Messages must be consistent, underscoring the importance of data management. In addition, they should be customized according to stakeholder group. For example, the level of education or amount of training needed by different groups concerning data management will vary. Messages should be repeated as needed and continually tested over time to ensure they are effectively getting out there and that awareness and understanding are building. - 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. A stakeholder analysis helps the organization better understand those
affected by data management changes. By taking that information and mapping stakeholders according to level of influence within the organization and level of interest in (or affect due to) the data management implementation, the organization can determine the best approach to
engaging different stakeholders in the change process. - Orientation and Training
Education is essential to making data management happen, although different groups will require different types and levels of education. Leaders will need orientation to the broader aspects of data management and the value to the company. Data stewards, owners, and custodians (i.e., those on the frontlines of change) will require in-depth understanding of the data management initiative. Focused training will allow them to perform their roles effectively. This means training on new policies, processes, techniques, procedures, and even tools. - Adoption Measurement
It is important to 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. Plan to measure: Adoption, Amount of improvement, or the delta from a previous state, The enabling aspects of data management – how well does data management influence solutions with measurable results?, Improved processes, projects, Improved identification and reaction to risk, The innovation aspect of data management – how well does data management fundamentally change how business is conducted?, and Trusted analytics.
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. - Adherence to Guiding Principles
A guiding principle is a statement that articulates shared organizational values, underlies strategic vision and mission, and serves as a basis for integrated decision-making. Guiding principles constitute the rules, constraints, overriding criteria, and behaviors by which an organization
abides in its daily activities in the long term. Regardless of whether there is a decentralized or centralized operating model, or anything in between, it is critical to establish and agree upon guiding principles so that all participants behave in synchronistic ways. The guiding principles 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. - 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. It is important to establish 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. Incremental change is also much easier to justify so it is easier to gain stakeholder
support and buy-in, and get those critical participants involved.
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