A 3-Phase Approach to Creating a Single Source of Truth Data Environment for FP&A and Enterprise Analytics

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Garrett Rizk

Managing Director, FP&A
Finance Solutions

Garrett Rizk

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Managing Director, FP&A
Finance Solutions
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Making informed, data-driven decisions that connect operational choices to financial outcomes is more crucial than ever. A single source of truth data environment is pivotal in enabling financial planning and analysis (FP&A), financial modeling, analytics, and visualization. However, private equity-backed middle-market companies often face common pain points such as disparate systems, poor data governance, and lack of data capture. In a survey conducted by Intertrust and Everest Group, 75% of PE respondents said the inability to consolidate siloed business systems is a major challenge. This article introduces a phased approach to creating a data environment that not only addresses these business needs but also brings significant benefits, providing an in-depth analysis of the steps and components required for success.

Phase 1: Establishing Requirements & Laying the Foundation for Data Centralization

The first phase involves identifying data sources and overcoming data fragmentation challenges. Assessing an organization’s various systems and databases is the first step in addressing fragmentation. By focusing on the most critical systems and business areas, organizations can prioritize their efforts and, importantly, empower themselves to maximize the early impact of data centralization.

Selecting the appropriate tools and platforms for your data centralization journey is also crucial. Starting with Excel and Power Query or evolving to Power BI Data Models, Azure, and AWS for scalable data environments can help streamline the data ecosystem to suit the organization’s operating model. Integrating these systems and tools ensures data is collected, organized, and structured efficiently to enable actionable, data-driven decisions.

Furthermore, actionable visualization is crucial in encouraging the adoption and buy-in for the data ecosystem. It allows stakeholders to see the benefits of centralized data in their day-to-day roles and, importantly, promotes company-wide alignment on data and analytics priorities, making everyone feel included.

Phase 2: Refining the Data Environment and Developing BI Dashboards

Building on initial data centralization efforts, the focus moves toward data transformation and integration. This involves identifying crucial business questions, KPIs, and views for data transformation and understanding the data requirements for effective analysis and decision-making.

Transforming diverse data types for analytics requires effective data modeling and an emphasis on data governance. A robust data governance framework ensures the accuracy, consistency, and quality of data collected and utilized in the organization’s analytics initiatives.

Companies can then implement BI dashboards and visualization by identifying priority areas for development, refining dashboards based on requirements, and showcasing quick wins to drive adoption and accelerate decision-making. This process is instrumental in elevating the organization’s analytical capabilities and unlocking the insights buried within the wealth of data in a centralized environment.

Phase 3: Expanding the Data Environment to Cover the Entire Enterprise

The final phase involves incorporating the remaining systems and data sources into the data environment and refining data governance strategies and maintenance procedures. By expanding the coverage of their data ecosystem, companies can enable data-driven decision-making across all business areas and levels.

Iteratively developing financial and operational dashboards is essential in this phase, ensuring that the single source of truth becomes accessible and actionable for all organizational stakeholders. This process involves identifying new priority areas for dashboard development and refining existing dashboards to adapt to changing business needs and new data requirements.

Single Source of Truth: Benefits and Long-Term Impact

A cohesive data environment offers numerous benefits. These include better access to data for various stakeholders such as finance, accounting, data analysts, and business leaders; streamlined, organized, and definitionally aligned data; faster and more informed decision-making; and alignment of resources, systems, processes, policies, and people for long-term success.

In addition, a single source of truth data environment fosters a culture of data-driven and evidence-based decision-making, which can lead to improved operational efficiency, cost reductions, enhanced competitive advantage, and overall long-term growth. A single source of truth data environment is invaluable in driving decisive decision-making and connecting operational choices to financial outcomes. A phased approach ensures maximum success and value while addressing key challenges private equity-backed middle-market companies face. E78 Partners invites readers to explore further and optimize their financial performance and capabilities by creating a single source of truth data environment. By embracing this approach, organizations can transform their analytics landscape and propel themselves to new heights of success. Contact us to learn how our FP&A experts can help.

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Garrett Rizk
Managing Director, FP&A
Finance Solutions
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