You may also be interested in:


Focus on Data to Leverage the FP&A Capability Maturity Model

  • By Bryan Lapidus, FP&A
  • Published: 10/25/2017

fpa-bannerWhat is the vision for your FP&A department? Are you already a high-performing team? Do you have a road map for how to improve?

AFP’s FP&A Maturity Model was developed by the FP&A Advisory Council. The Maturity Model was designed to help organizations determine the level of maturity of their current FP&A capabilities, identify what best-in-class looks like, and what specific actions can improve your team. The Maturity Model is organized in four domains:

  • Business Process
  • Technology
  • Organization/people/skills
  • Data.

Each of these domains is then subdivided into five elements of FP&A practices. An FP&A team can self-evaluate on these practices on a one-to-10 scale into foundational, emerging or optimized groups. This is the second in a series of occasional articles highlighting a section of this impressive resource.

This month we are examining the data aspect of the Maturity Model—a fully integrated and robust modeling and purpose-built planning platform seamlessly integrated with self-service analytics and visualization capabilities and reliant on a central repository.

In order to prevent repetition in this article, the following are the high-level actions that are applicable to each of the five sub-topics that follow:

  • Baseline: Establish a baseline by understanding and documenting the current environment, including
  • Catalog the issues data
  • Assess and prioritize the severity, business impact and relative costs of the issues
  • Determine alternatives for addressing the issue, including stop-gap fixes, near-term, mid-term, and longer-term solutions
  • Establish a roadmap, scope out the first project for a quick win.

Data Quality

Finance aspires to a comprehensive and fully integrated enterprise data governance model; it uses robust tools and processes for managing the lineage and flow of data from source systems to staging / repositories to planning and reporting analytical applications. Data quality focuses on source system owners, data integration and management, data owners, governance, and data usage. Drilling down one level deeper requires an examination of data quality issues, including data elements, data source, core issues(s), and known remediation methods.

Data Access

The aspirational FP&A has complete and timely access to the right information from a trusted source with a high level of data quality and surety. This topic focuses on individual and team access and data availability, data elements, data sources and models, specific issues, and necessary workarounds / remediation. Assess the severity, business impact, and relative cost of the data access issues.

Data Usage

FP&A is moving towards big data principles to support advanced analytics; data sources incorporate a full array of financial, operational, customer, competitive benchmark, etc., from internal and external sources. FP&A should focus its activities on understanding the type of data, frequency of usage, and importance / value. For example, what is useful in for planning forecasting, reporting, and analysis, and what is not part of this process but still relevant to FP&A. What is the opportunity and cost associated with incorporating additional data elements into the FP&A environment? Adding data may be evaluated like the investment that it is.

Master Data Management

The aspirational Master data is managed centrally and promulgated consistently throughout all analytic applications and reports; FP&A relies on enterprise tools and technology which provide automation and support streamlined processes. Specific actions to improve this process include taking inventory of all hierarchies, data movement, data transformation, data mapping, and data loading at a fairly granular level.

Data Governance

The organization strives to have a comprehensive formalized governance program,  processes, and procedures around managing the collection, identification, storage, and usage of data across the enterprise. Specific actions to improve this process include defining and documenting the key data governance needs across the organization, including the relationship requirements among different components such as data, business process, how you will use the data and ownership; for example who has the authority to add a new account and how?

Bryan Lapidus, FP&A, is a contributing consultant and author to the Association for Financial Professionals. Reach him at

For additional FP&A insights, download the FP&A Maturity Model.
CFO Playbook by SERRALA:

Strengthen Your Finance Departments’ Offense and Learn About Best-In-Class Cash Visibility and Finance Process Efficiency Now

Click To Find Out How the CFO Playbook Can Help You

Copyright © 2020 Association for Financial Professionals, Inc.
All rights reserved.