Unleashing the Power of Data: Building a Foundation

Introduction

As private market firms continue to generate increasingly vast amounts of data, the need for a comprehensive data strategy becomes more critical. In today’s business world, data is the lifeblood of any organization, and having a well-defined data strategy is essential for long-term success. In this second part of our four-part series on Enterprise Data Strategy, we will explore the foundational elements of a best-in-class operating model for an enterprise data strategy. Specifically, we will review the importance of performing a data strategy assessment and how it can help private market firms achieve their data management goals. By understanding the benefits of a data strategy assessment and the elements involved in assessing data strategy, firms can design a roadmap for success and unlock the full potential of their data.

Benefits of Data Strategy Assessment

Performing a data strategy assessment is a critical first step for any private market firm looking to develop a comprehensive data strategy. This assessment serves as a baseline for identifying areas of improvement and creating a roadmap for achieving data management goals. By evaluating the current state of a firm’s data strategy, leaders can uncover gaps in their approach and develop a plan to address those areas. Here are some key benefits of performing a data strategy assessment:

• Identify Gaps: One of the primary benefits of a data strategy assessment is that it helps identify gaps in a firm’s current data management approach. By evaluating each element of the data management framework, firms can identify areas where they may be falling short and develop a plan to address those gaps.
• Prioritize Investments: A data strategy assessment can help firms prioritize their investments in data management. By identifying quick wins and areas where investments will have the greatest impact, firms can develop a roadmap for achieving their data management goals in a way that aligns with their business priorities and budget.
• Develop a Data Roadmap: A data strategy assessment provides a framework for developing a data roadmap that aligns with the firm’s overall business strategy. By evaluating each element of the data management framework, firms can develop a comprehensive plan that addresses their specific data management challenges and aligns with their broader business goals.
• Improve Data Quality: A data strategy assessment can help firms improve the quality of their data. By evaluating the current state of data quality and identifying areas for improvement, firms can develop a plan to improve data quality, ensuring that they are working with accurate and complete data.
• Increase Efficiency: A data strategy assessment can help firms increase efficiency in their data management processes. By evaluating the current state of data management workflows and identifying areas for improvement, firms can streamline their processes, reducing the time and effort required to manage data.

Elements to Assessing Data Strategy

A successful data strategy assessment involves a comprehensive evaluation of each element of an organization’s data management framework. Here are the key elements that private market firms should consider when assessing their data strategy:

• Data Estate Definition: Defining the data estate is the first step in assessing an organization’s data strategy. It involves mapping out all the data points and sources that an organization uses, creating a comprehensive view of the accumulated data assets for all business teams. This mapping is critical to ensure a shared understanding of all data items and to facilitate discussions between business lines. With a clear understanding of the data estate, firms can develop a comprehensive data management plan that aligns with their broader business goals.
• Data Operation Assessment: A data operation assessment reviews the data quality, including its accuracy and completeness, and assesses the current framework to recognize and address issues. This step determines whether data is trustworthy and can be used to drive insights and support broader business objectives. By assessing data operations, firms can identify areas where data quality may be lacking and develop a plan to improve data quality, ensuring that they are working with accurate and complete data.
• Data Architecture Assessment: A data architecture assessment reviews the current technology tools and system architecture supporting data operations for all data domains. This analysis identifies gaps with accessibility, usage, adoption, and integration of data and informs any target state implementation guidelines. By assessing data architecture, firms can identify areas where data may be siloed or difficult to access, enabling them to develop a plan to improve data accessibility and integration.

The results of the data operation and architecture assessment are benchmarked against peers and then summarized in a heatmap, which enables the organization to identify the pain points and gaps to be addressed in the target state design. The outcomes of this process include:

• Target Operating Model: The assessment will result in a defined target operating model that aligns operating capabilities and capacities to long-term strategic objectives. The target operating model will identify, leverage, and enhance data assets to proactively improve performance, reduce risk, create efficiencies, identify new opportunities, enable predictive analytics, support AI and machine learning, and reduce organizational information asymmetries. The TOM will also define the controls and governance needed to manage workstreams, roles, responsibilities, and best practices to be implemented for effective data management.
• System Selection: If a new technology is required to address any shortcomings highlighted in the TOM, a system selection process will occur. This involves prioritizing a pilot dataset, building the architecture and business processes to understand how to integrate the new technology.
• Technology Roadmap: The assessment will help define a technology roadmap that sets the requirements for implementing the TOM. The roadmap highlights key milestones and structures the high-level business use case for the preferred solution.
• Change Management and Transition Plan: Implementing a new data strategy framework is a significant change and requires thoughtful change management to be successful. The assessment will result in a change management and transition plan that ensures the success and sustainability of the TOM after go-live. This plan includes communicating the impact to all stakeholder groups, deployment planning, relevant training, and ongoing monitoring to create the optimal circumstances for adoption.

Common Complexities

Formulating a data strategy is complex and fraught with challenges. Each organization has its own idiosyncrasies that require careful analysis and thoughtful evaluation of its data capabilities to provide the right guidance. While performing a data strategy assessment, there are several complexities that can arise. These include:

• Complexity of Data Landscape: Enterprises often have complex data landscapes with multiple data sources and various data types. It can be difficult to gain a comprehensive view of all data assets and their relationships across the organization.
• Incomplete or Inaccurate Data: Incomplete or inaccurate data can lead to incorrect insights, poor decision-making, and negatively impact the overall success of the data strategy assessment.
• Resistance to Change: Implementing a new data strategy framework can be met with resistance from stakeholders who are used to working with existing processes and systems. Change management becomes critical in such scenarios to manage the transition and ensure successful adoption.
• Technology Integration: Enterprises often have existing technology infrastructure in place, and integrating new technologies into the existing framework can be challenging. Careful planning and implementation are required to ensure seamless integration.
• Resource Constraints: A data strategy assessment can require significant resources, including time, budget, and skilled personnel. Lack of these resources can hinder the success of the assessment.
• Governance and Compliance: Data governance and compliance requirements can vary based on industry, region, and organizational policies. Ensuring compliance with these requirements while also achieving business objectives can be challenging.
• Scalability and Sustainability: A successful data strategy assessment should be scalable and sustainable, which requires planning for future growth and ensuring the framework is flexible enough to adapt to changing business needs.

Conclusion

Data strategy assessment is a critical first step to effectively manage and leverage an organization’s data assets. By defining the data estate, assessing data operations and architecture, and designing a target operating model, organizations can improve performance, reduce risk, and identify new opportunities. It is important to understand that performing a data strategy assessment can be a complex endeavor, as it often involves navigating various data quality issues and addressing resistance to change within the organization.

At Lionpoint, we understand these challenges and offer an all-encompassing data strategy assessment to help your organization navigate them. Our experienced consultants can help you design a best-in-class operating model for your enterprise data strategy, enabling you to unlock the full potential of your data assets. Contact us today at mcastaneda@lionpointgroup.com to learn more or to schedule a consultation.