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Why the FP&A team must demonstrate leadership in Business Analytics
Digital transformation has created new opportunities. It's possible to generate insight from more sources of data, faster than ever before. But technological advances have also increased competitive pressure.
How can FP&A teams adapt to these challenges and redefine their role in modern business? How can FP&A remain relevant in an organisation where everyone is an analyst?
In this article I will explore how FP&A teams can continue to create value by embracing new technology, acquiring new skills and providing leadership in Business Analytics.
Finance transformation is creating trusted business partners
Finance transformation programmes have moved beyond the outsourcing of transactional tasks to shared service centres. Finance teams now strive to become trusted business partners, using their expertise to help other functions make better decisions and create value.
New technology is driving further change
However, the business landscape continues to evolve rapidly as organisations embrace the opportunities offered by data exploitation. Traditional weekly or monthly reporting cycles no longer provide the speed to insight required to meet competitive pressure. Today’s business functions must be supported by on-demand reporting and predictive analytics.
Traditional business models are being challenged by initiatives such as data democratisation, which are removing the gatekeepers of corporate data. Instead of access to data being controlled by the IT function, creating a bottleneck, the goal of data democratisation is for anybody to use data at any time to make decisions.
An uncoordinated approach to analytics increases cost
Commercial pressures and a perceived inability for FP&A teams to provide the required insight in a timely manner have led to business functions developing their own analytic solutions. This uncoordinated approach leads to analytic capability developing in silos and increases costs due to duplication. Without adherence to a centralised policy on data management the risk of breaching regulation such as the GDPR increases.
FP&A teams are well positioned to lead Business Analytics
Business Analytics uses data and advanced analytics to develop new insights and understand business performance. This contrasts to Business Intelligence (BI) which is traditionally backward-looking, using fixed metrics to measure past performance. Business Analytics focuses on why an event happened, what might happen next (predictive analytics) and what is the best response (prescriptive analytics).
Finance teams are in the privileged position of having access to the General Ledger (GL). Any analysis of business performance that does not include input from the finance team won't contain information from the GL and will, therefore, be incomplete. A centralised Business Analytics capability led by FP&A can provide an organisation with a holistic view of business performance.
To become leaders in Business Analytics FP&A teams must:
- Embrace advanced technology to meet the emerging requirements of the business
- Develop new skills and technical knowledge to discover insight from data
- Design new processes that support modern ways of working
How FP&A teams can meet the needs of a data-driven business
Providing insight on demand
New technology can give business users access to the data they need to perform their role. Self-serve dashboards can deliver insights on demand, rather than users having to wait for weekly or monthly reports. Users are able to explore their data and answer questions, without needing support from IT.
Analytics aligned to strategy
A coordinated approach to analytics ensures that effort is not duplicated, is aligned with corporate strategy and contributes towards the business goals. A centralised function can ensure consistency and that best practice is shared, improving the quality of analysis throughout the business.
Insight from new sources of data
Gaining a competitive edge requires a broader picture, gained from analysing new data sources. Combining internal data with external data sources such as social media can provide new insights. Connected devices and tech logs are generating gigabytes of data that can reveal hidden patterns to those who can analyse and identify them.
Using Advanced Analytics to predict and respond to future events
Advanced Analytics uses sophisticated tools and techniques to go beyond traditional BI in order to deliver deeper insight and generate recommendations. Machine Learning (ML) techniques are used to predict future events based on historical data. These models can predict competitor behaviour and suggest the best response.
Finance teams that don't transform will become less relevant
In organisations where everyone is an analyst, FP&A will become less relevant if the analytic capability is distributed throughout the organisation in an uncoordinated way. Finance teams that do not transform, risk being left with regulatory reporting and compliance as their only areas of focus. Both of these areas are likely to see a reduction in headcount as Artificial Intelligence (AI) drives increased automation.