People, processes, and technology have long been the cornerstone of organizational success. As such, it should come as no surprise that business leaders have often focused on these three areas when changing their organization. There is, however, a fourth cornerstone of success: data. And it is a common thread through people, processes, and technology when undergoing a transformation.
Data is often one of the most valuable assets an organization can create. It is malleable and can be shaped, added to, and transformed through the systems and processes implemented during a transformation effort. When data is available and accessible, it informs decision-making, enables insights, drives process improvements, and supports innovation. These changes have downstream and upstream impacts that can affect what data is collected and how it is stored, accessed, and shared. In the context of today’s business transformations, data deserves a prominent seat at the table.
Be Intentional About Data Landscape Design
Scaling a large transformation requires defining the end goal of a data landscape from a new angle. Most companies use the same strategies and project planning techniques for nearly every change, even when planning a large transformation. However, the mindset and skills that enable one to successfully climb a hill are not the same needed to scale a mountain. For one, the challenges are different. The mountains are more treacherous. One may need specialized gear, more and better information, and appropriate training. So why is it popular to approach transformation planning in the same way one would an everyday project?
A single person creates 1.7MB of data every second.
You can curate a data landscape by flipping the way it’s been done. Instead of basing your desired reports or insights on what is understood and available, consider your ideal master data management solution with reporting outcomes and other user goals in mind. That may mean you will need to reconsider the entire existing pipeline of historic data to facilitate the vision of the future data landscape. And considering a single person creates 1.7MB of data every second, revisiting that pipeline may seem like an extensive task. It’s worth it. Doing so ensures your organization’s future of more governable and clean data is achieved sooner rather than later. System configurations and pipeline processes should all be considered with a mindset toward how they will facilitate a clean, well-governed data ecosystem from the outset of any transformation project.
Data Quality Goals Should Drive the Current Stage of Assessment
When planning a large-scale transformation, it is critical to evaluate the processes and technology that are driving the business to, and inhibiting it from, success. The goal of any transformation is to address the components of the business that are impeding it from achieving success. However, the targeted transformation area is often identified through qualitative assumptions, not data-driven insights. Your organization should strive to make data driven decisions. And while this may not be possible in the organization’s current state, a well-executed transformation should better position the business to make data-driven decisions.
The degree of value that can be derived from data is limited by the total data inputs, the quality of the data, and the way data is governed. Organizations cannot make data-driven decisions or implement data-driven strategies if the underlying data foundation is not well grounded.
Strong data quality differentiates successful implementations. And data architecture is often the first measure for ensuring data quality, as it provides the framework for organizing and managing data assets, as well as enabling efficient data integration and governance practices. It facilitates effective decision-making, supports your organization’s overall data strategy, and enhances data accessibility and usability.
Unregulated data is often unusable. Data governance provides the framework for the availability, integrity, usability, and security of your organization’s data. A robust data governance model enhances data quality and ensures that data acquisition, storage, and accessibility processes are compliant from a regulatory standpoint. While this is a critical component of any organization, pay special attention when collecting the personal data of internal employees or external customers. Also, by aligning the business functions and your IT department during the initial setup, you will ensure that long-term maintenance, which is required for any data governance model, goes more smoothly.
Consider Future State Access Roles and Rights in Planning
When your organization undergoes a change, such as a merger, acquisition, or adoption of new technology, there may be changes in the way data is collected, processed, and shared. This is likely to create new circumstances, including privacy risks for individuals and businesses. Data access rights help ensure that only authorized individuals or groups access sensitive or confidential data, and they are a key consideration when planning and undergoing transformation.
With the growing presence of new data protection and privacy laws such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Brazilian General Data Protection Law (LGPD), it is important to recognize that what may be compliant today may not be compliant in the future. Proper planning will better ensure that the transformation’s result is compliant and provides the scalability required to accommodate future data protection and privacy laws.
However, if your organization does undergo a change that will affect how data is collected, processed, or shared, it is almost certain that existing access roles and rights will be insufficient. Therefore, it is more efficient and cost-effective to structure access roles and rights at the beginning of a change rather than addressing the topic as an afterthought. To start, business users should document user requirements by identifying the needs and responsibilities of different user groups within your organization who will interact with the tool. The end goal would be to define specific tasks they need to perform and the data or functionalities they require access to. From there, business users should define and document role responsibilities and partner with IT to allocate access based on the least privilege access principle, which entails users having no more access than what is needed to fulfill their job responsibilities.
People, processes, and technology are frequently deemed the driving component of any transformation. However, it’s critical to ensure that a great data landscape is not just the end goal, but the guiding light of a successful transformation roadmap. Data should no longer be an afterthought but prioritized from the outset. By reframing the approach, being intentional about data landscape design, and considering data quality goals, your organization can create a strong foundation for data-driven decision-making and successful transformations. Data governance and proper planning for future state access roles and rights are also crucial, particularly in light of evolving data protection and privacy laws. By integrating data considerations into every stage of the transformation roadmap, your organization can achieve compliance, scalability, and long-term success.