So does a data scientist have a career path in Financial Planning & Analysis ? Why does the FP&A function need someone who has specialized in data analysis , statistics and data modeling. Why can't the finance function just partner with IT or data analytics team in the company to get what they need ? There are many such questions that will cross your mind when we talk about modifying the organizational structure and actually introducing someone who does not have a background in finance or accounting in the field of finance.
Every function today is dabbling in big data and analytics whether it is sales and marketing looking at customer analytics or HR looking at employee performance analytics or supply chain optimizing transportation and production through supply chain analytics. Mary Driscoll in her article " CFOs want analysts trained in finance data science" refers to the study by APQC , her business research and benchmarking firm, that found that 95% of the financial professionals surveyed pegged data science as important to some degree. She aptly says" If the business is becoming data-driven, financial forecasting has to be driver-based and nuanced. And that means teasing apart probable economic consequences across the chain of value creation."
Today majority of the FP&A professionals develop forecasting models on excel and manage the models independently with each business unit/ area developing their own models . Their is minimal synchronization or standardization of the models leading to a lot of manual work of reviewing the methodologies so the outcomes can be compared the same way. Differences exist due to nuances in business unit, difference of skills sets, differences in data quality etc. These differences become greater through the years due to lack of standardization practices.
Even starting small such hiring an excel expert in FP&A teams who is responsible for maintaining and standardizing the model on excel, adding VB capabilities etc can go a long way in helping the analyst actually deliver on the " analytics" . It would free up the time of the analysts to actually do more value creation work. these excel experts can become the standard bearer for standardization of the models and data quality. But as data becomes bigger , excel based forecasting models cannot keep up with the predictive and prescriptive modeling needs. Finance functions will need data science experts to work along FP&A analysts to develop such models. These experts would know the source of the data and how to apply various techniques to develop models but will work in conjunction with FP&A analysts who " understand" the data as well as the business needs and will guide the data scientists to develop the right models and explore the correct relationships.
One way is to create a center of excellence for FP&A which will develop and standardize the models, interact with IT for data needs and develop information assets and explore the latest technologies and techniques in the area of financial forecasting.
The FP&A professionals today need to become the agent of change and advocate for the need of interaction with data experts. Finance function also needs to develop a career path for finance data experts/ scientists and work with universities to develop the training programs needed.
So where is your company in this area - do you have a data expert in your team. Looking forward to hearing your feedback and comments.
Every function today is dabbling in big data and analytics whether it is sales and marketing looking at customer analytics or HR looking at employee performance analytics or supply chain optimizing transportation and production through supply chain analytics. Mary Driscoll in her article " CFOs want analysts trained in finance data science" refers to the study by APQC , her business research and benchmarking firm, that found that 95% of the financial professionals surveyed pegged data science as important to some degree. She aptly says" If the business is becoming data-driven, financial forecasting has to be driver-based and nuanced. And that means teasing apart probable economic consequences across the chain of value creation."
Today majority of the FP&A professionals develop forecasting models on excel and manage the models independently with each business unit/ area developing their own models . Their is minimal synchronization or standardization of the models leading to a lot of manual work of reviewing the methodologies so the outcomes can be compared the same way. Differences exist due to nuances in business unit, difference of skills sets, differences in data quality etc. These differences become greater through the years due to lack of standardization practices.
Even starting small such hiring an excel expert in FP&A teams who is responsible for maintaining and standardizing the model on excel, adding VB capabilities etc can go a long way in helping the analyst actually deliver on the " analytics" . It would free up the time of the analysts to actually do more value creation work. these excel experts can become the standard bearer for standardization of the models and data quality. But as data becomes bigger , excel based forecasting models cannot keep up with the predictive and prescriptive modeling needs. Finance functions will need data science experts to work along FP&A analysts to develop such models. These experts would know the source of the data and how to apply various techniques to develop models but will work in conjunction with FP&A analysts who " understand" the data as well as the business needs and will guide the data scientists to develop the right models and explore the correct relationships.
One way is to create a center of excellence for FP&A which will develop and standardize the models, interact with IT for data needs and develop information assets and explore the latest technologies and techniques in the area of financial forecasting.
The FP&A professionals today need to become the agent of change and advocate for the need of interaction with data experts. Finance function also needs to develop a career path for finance data experts/ scientists and work with universities to develop the training programs needed.
So where is your company in this area - do you have a data expert in your team. Looking forward to hearing your feedback and comments.
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