FP&A teams deal with an ever-increasing amount of business data that requires consolidation before insights can be factored into forecasts, budgets, and business plans.
In many ways, finance and more importantly FP&A teams have taken on the role of data scientists. This strategic role is no longer limited to reporting financial indicators, involving predictions of what the future may hold.
For this, the crucial steps include:
- Data sourcing and wrangling,
- Building models and forecasts,
- Communicating actionable insights to leadership, and
- Enabling confident decisions.
When the casual relationship between data science and FP&A teams turns into a formal partnership, this collaboration becomes a data powerhouse. The intersection of finance and data science is an unbeatable competitive advantage for organizations.
Throughout this article, we’ll cover why now is the best time to start aligning the two teams and how to initiate the finance and data science collaboration.
Why is it important to apply data science in finance?
Per a recent FP&A trend report, finance professionals spend 49% of their time preparing and validating data.
The main reasons for this are:
- Inaccurate or error-prone data
- Lack of access to source data
- Multiple versions of the truth
- Underskilled team members
In addition, with Excel-driven manual processes for forecasting, it is difficult to efficiently meet the expectations of advanced methodologies such as scenario modeling.
In absence of a finance and data science collaboration, the limitations faced by FP&A teams include:
Gap 1 - Inconsistency with KPIs
Often, the financial data available to the financial planning and analysis team is not structured in the same way as other metrics presented to leadership.
What happens then?
Your FP&A team has to invest time and effort to manually rearrange this data into a structure that is consistent with other KPIs.
Gap 2 - Insufficient data to influence major decisions
The poor quality of data available and the inability to transform this data into critical insights is a common problem faced by finance and FP&A teams and CFOs.
In the absence of business planning and analytics software, teams share data over spreadsheets, and over time, different versions exist, which could differ from the actual version. Financial modeling becomes unreliable without version control.
Without a single source of truth, consolidation of data from multiple departments and business applications is inefficient. At a fast-scaling company, spreadsheets cannot support an infinite number of calculations and macros. This leaves the FP&A team without reliable models and projections required to produce accurate budgets and forecasts.
In absence of real-time data, senior management cannot drill down into business data for their decision-making.
Gap 3 - Unstructured internal and external data
Data comes from several sources and in multiple formats including:
- Structured data: spreadsheets, databases, financial statements
- Unstructured data: emails
For the FP&A to be at the forefront of the push toward deriving valuable insights from data, they must answer the question: how are they going to aggregate information from disparate sources?
Here, the advancement of technology, in the form of financial planning software can allow companies of all sizes to unlock value across multiple data applications. The capabilities of a business planning platform can analyze historical data to help create more accurate rolling forecasts and financial outlooks.
FP&A software can serve as a single source of truth, collect structured and unstructured data, and help drive the organization with actionable insights related to performance, profit, risk management, and beyond.
Gap 4 - Lack of data infrastructure to handle enormous amounts of data as the company scales
Approximately, 90% of the data in the world was generated in the last two years. For fast-scaling organizations, this avalanche of data raises the question: Are they capable of handling enormous amounts of data?
Functionalities such as multidimensional models, automated data flow, integrations, and access control are some of the critical features a business planning software offers. In absence of a platform, the employees may not know how to process data, its importance, or tracking the different sources. While data professionals know what’s happening, others in the FP&A team may not have a transparent picture.
Also, applying data science in finance with the help of business planning and analytics software is important owing to the sensitivity of the financial data which could get compromised if stored on spreadsheets.
What are the tasks of the FP&A manager as a data scientist?
With a finance and data science collaboration, the FP&A function can play a more strategic role to help stakeholders throughout the business make data-driven decisions.
The possibilities for collaboration include:
- Identification of key metrics for individual teams to track,
- Determining how each team should forecast components to grow, and
- Identifying strategies to make each department more cost-efficient.
Beyond data science, the FP&A department shares a single source of data and metrics throughout the organization. Isolated business systems and Excel spreadsheets lead to silos and prevent the flow of insights across applications and throughout the organization.
This centralized source of data empowers business functions and leaders to speak the same language as the finance organization, work on the same assumptions, and drive analysis to improve the overall performance.
The results of a finance and data science collaboration and applying data science in finance are:
- More effective collaboration and conversations between departments
- Stronger alignment around company objectives
- Less data wrangling and more time for the FP&A function to influence business decisions
FP&A partnerships with functions across the organization
Sales and FP&A partnership
Finance and sales alignment is critical since finance has access to operational data that can help sales sell the right products. Sharing this information with sales helps the sales team tailor efforts so that the company pursues smart growth opportunities.
Traditionally, sales teams were compensated upon hitting revenue targets while operational leaders see bonuses tied to profits. This made sense in normal times. But during an economic downturn, when factors such as increased costs of labor, materials, and shipping, and inflation affect profitability, shared metrics rooted in profits produce a healthier organization when built into sales revenue goals.
Well-defined sales planning, due to a finance and data science collaboration, helps sales teams meet and outperform sales targets and align cross-functional departments with the company’s mission.
Engineering, product, and FP&A partnership
When your engineering and product development teams have visibility into the costs of their actions and how each purchase contributes toward the financial goals, they will be better able to make efficient decisions.
By applying data science in finance, engineering and product teams can see how their design and infrastructure choices move the total spend closer to or away from the quarterly goals.
Furthermore, FP&A and tech teams have a reciprocal relationship that requires an open line of communication and shared data models for analysis.
Data science in FP&A empowers product teams to strategize and make decisions on their own, based on financial modeling. The result is more efficient discussions and decision-making during product reviews because the cost, revenue, margin, and pricing implications have already been factored into the analysis.
How to align finance and data science teams
Enable sourcing data with native integrations
A business planning and analytics platform facilitates a finance and data science collaboration by integrating all types of financial and operational data from all business applications and data sources.
All this data is curated on a single platform, to create a single source of truth that decision-makers can access to formulate enterprise-level decisions.
Establish a single collaborative platform
The modern FP&A business leader, as a strategic finance partner, collaborates with other departments to collect, aggregate, and validate data.
Business planning and analytics platform Pigment enables cross-functional collaboration by breaking down decision silos between different departments. The FP&A software analyzes data, allows the financial planning and analysis team to understand business drivers, and conduct meaningful discussions with stakeholders.
Allow for setting up data models
Robust data models can easily forecast and run multiple scenarios to spot risks and opportunities for growth. Automated workflows by business planning software Pigment collects all data in one place to ensure your predictions are accurate. Plan and predict the impact of your business decisions on your bottom line within minutes instead of days.
Build high levels of data accuracy and timeliness
A high level of data accuracy and timeliness can improve the FP&A team's results by:
- Integrating operational planning processes into timely financial planning processes.
- Incorporating operational and financial results into a narrative to make reports more actionable.
- Expanding financial modeling capabilities to include real-time forecasts.
Enforce top-grade security
Pigment’s fine-grained security gives your business the control needed for superior data safekeeping and protection.
Pigment’s security features include:
- Role-based access control
- Data encryption
- Audit trail
- Authentication via single sign on
The path forward with a Finance and Data Science collaboration
The newly minted finance and data science collaboration opens up countless opportunities for growth, high-quality work, and more satisfied employees.
With integrated business planning software Pigment at your side, the FP&A team can ramp up data-driven financial planning and analysis within weeks.