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Marry Financial and Operational Data to Improve Forecasting


Data analytics tools better enable teams to bring both financial and operational data together.

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Though most finance teams are digitally automated, wide variations in experience can be found in their progress (or lack thereof) beyond implementation. The most adept have made significant progress with newer cloud-based technologies to give themselves more leverage, more efficient process, better collaboration with people and broader integration of data.

The payoff for those who leverage data analytics technology is evident; reduced IT resources and time no longer spent managing processes that were highly inefficient. Falling behind is becoming riskier and riskier.

According to Rob Hull, a former CFO and founder and chairman of cloud-based FP&A provider Adaptive Insights, “Finance teams have got to be in a place where they’re bringing financial and operational data together in a very collaborative framework that works with the rest of the management team in a nimble, agile and very dynamic way, so that things like scenario analysis and reforecasting can be done quickly while still being collaborative.”

An example is the mobile accessories company, ZAGG, which had a significant sales forecasting problem during one quarter when it missed sales projections by over 30%. By improving their sales forecasting, they were able to change the shipping method of their product from China from overnight to longer term shipping, reducing their costs by about $8 million in one quarter. The intersection between financial impact and operations drive results and brings power to the finance team to engage with the operational managers to drive the business in a more strategic way.

If companies are stuck in the early phases of digitization with Excel-based analytics and planning, they’ll either have to change and improve their processes or they’ll quickly fall behind competitively.

Having the proper tools helps finance organizations to be more collaborative and allows them to redirect their time into analyzing the data, not just managing it. “We see in the research that organizations that are operating at a higher level of technology proficiency and overall FP&A efficiency and effectiveness are organizations that end up having a better bottom line margin for the company.”

As far as what Hull sees on the technology transformation horizon, he recognizes that predictive analytics is still very data-scientist oriented. “It can be a technically challenging concept. It’s not really a business solution for the masses. What needs to happen in the predictive space is that it needs to become more intuitive and as that happens I think you’ll see broader adoption of it.”