The big picture: CFOs have a vested interest in advanced analytics, given their role to bridge the gap between strategic and operational decision making through planning and forecasting activities.
What’s new: More FP&A groups are rebranding themselves as business planning and analysis (BP&A) functions to signify the beyond-finance manner in which planning, forecasting and analytical capabilities are increasingly interrelated with business operations.
BP&A also may serve as a translator between the business and the data scientists to effectively communicate analysis requirements and results.
The best approach: CFOs should focus on the following foundational components when developing an analytics strategy for their organization:
- Close the digital skills gap by deploying a variety of skill-sourcing mechanisms.
- Build solid partnerships with business partners and technology and analytics teams.
- Implement an effective enterprise data model and data governance.
Go deeper: Read our insights below.
What’s behind the best data and analytics strategies? Simply stated, it’s the capabilities that allow companies to generate forward-looking insights to proactively drive critical business decisions and propel the business forward to meet strategic goals.
Many organizations rely on data scientists who apply their algorithmic magic behind the scenes and ascertain insights from large volumes of unstructured information. But successful advanced analytics capabilities also require a level of translation and oversight from the finance organization. Finance leaders’ business knowledge, measurement savvy and risk mindset are ideally suited to foster the alignment, governance, accuracy and accountability that thriving advanced analytics capabilities require. Chief financial officers (CFOs) have a vested interest in the advanced analytics game, especially given their role to bridge the gap between strategic and operational decision making through planning and forecasting activities.
The trend toward BP&A
Successful financial planning and analysis (FP&A) teams are traditionally grounded in data-driven analytics to provide insightful information to help business partners strategically manage various aspects of the business. To that point, more FP&A groups are rebranding themselves as business planning and analysis (BP&A) functions to signify the beyond-finance manner in which planning, forecasting and analytical capabilities are increasingly interrelated with and interdependent on sales and marketing, supply chain, human resources (HR) and other business operations. However, in this new world of emerging technologies and big data, BP&A also may serve as a translator between the business and the data scientists to effectively communicate analysis requirements and results between the two parties.
For example, BP&A teams may leverage advanced analytics to identify opportunities to optimize spend, rather than rely on broad-brush cost cutting, through analyzing procurement spend, profitability gains generated via supply chain efficiency improvements, workforce-planning adjustments, and sourcing improvements, to name a few. On a more tactical level, BP&A teams may utilize analytics to drive collaboration with business partners to make more informed build-or-buy product decisions, set new price points, and identify which inventory products should be pulled forward in (or out of) the supply chain.
The need to access broader, more diverse data sets
Advanced analytics also may be utilized to enable more accurate financial projections with minimal human intervention. Machine learning, a process that utilizes algorithms to learn from historical data and help identify patterns and trends, may be used to enhance forecast timeliness and accuracy, as well as to improve effectiveness and efficiency of business decision making through scenario planning. As a result, CFOs can strengthen their ties throughout the business and expand influence outside of core finance functions.
A big-data capability equips BP&A teams with access to broader and more diverse data sets (including unstructured data and data sets that live outside the organization) that can be subjected to more extensive analyses. Leading organizations process unstructured data in a manner that provides insights to drive company performance while uncovering new potential risks and opportunities. As more companies access data that exists outside of their systems and organizations, data governance and accountability have become more critical, requiring CFOs and finance organizations to work closely with technology partners to ensure alignment with and adequacy of data governance strategies.
In developing an analytics strategy for their organizations, CFOs should focus on the following foundational components:
- Technical capabilities: Closing the digital skills gap has been an ongoing challenge throughout most areas of the business, including finance. The best way to address this challenge is by deploying a variety of skills-sourcing mechanisms, including upskilling, partnerships with external talent and consulting partners, and revamping hiring profiles. Advanced analytics capabilities require finance professionals to have digital fluency, knowledge of where data sits throughout the organization, and an understanding of the business to enable identification of trends and indications of risks and opportunities to be addressed. Core financial skill sets remain as important as ever, however they now need to be complemented by proficiency in machine learning, artificial intelligence (AI) and other advanced technologies.
- Business partnerships: Adept finance leaders cultivate a culture of collaboration and alignment through routine interactions and relationship building with their business partners. BP&A teams, more so than any other part of the organization, understand business drivers and key performance indicators (KPIs) used to track progress toward strategic goals. That knowledge is crucial when working with business partners to determine the performance measures and trends in supply chain, sales and marketing, HR, etc., necessary for making critical business decisions. Finance also should foster a solid partnership with its technology and analytics teams so that the appropriate business requirements may be communicated and the necessary algorithmic coding may be performed to address the analytical needs of the business. Through these analytics, BP&A teams may interpret the relevant insights that may be utilized to support better decision making in collaboration with business partners across the organization.
- A data model and data governance: An effective enterprise data model ensures that all the company’s metrics – from the strategic plan metrics and KPIs down to detailed operational measures – are aligned. It also is critical to consistently define terms to help ensure that data used for analysis produces accurate and reliable reporting and insights. Data governance describes a company-wide strategy and plan to establish control and oversight of the use of organizational data, both internal and external. While data governance is not the CFO’s responsibility, finance leaders for years have been taking on growing roles in shaping, executing and monitoring data governance programs. CFOs certainly have responsibility for determining whether data governance lapses that result in breaches rise to the level of materiality of reporting.
Finance leaders may not always be responsible for the algorithmic magic that equips the business with forward-looking analytical insights. However, they play an essential role in ensuring that those insights are based on accurate and relevant assumptions, fueled by secure data, and aligned with the company’s strategic plan.