Demand for advanced analytics is increasing, and so is the expectation that management accountants will perform the analytics. Finance leaders want a data-driven culture to maximize the value of data through analytics. Specifically, they aim to make business decisions faster and more accurately through automation and predictive modeling. Trust in data analytics is the foundation of a data-driven culture. Fortunately, progression through the stages of analytics maturity also builds a foundation of trust, and eventually business decision makers gain enough confidence in predictive models to let the models themselves make the decisions
A world market leader of power tools and accessories manufacturing launches hundreds of new products each year across various brands. Until recently, the company relied on a traditional manufacturing process. Product teams worked with end users to identify needs and then build features around these needs to roll out around the world. However, they lacked insight into their user’s needs and how to leverage their data to become completely customer-focused. Once they gained the ability to drill-down into their data for important business insights, they were then able to create meaningful and fulfilling experiences for their customers.
Most ERP vendors offer tools that can accelerate analytics, such as bolt-on applications that speed up the extraction of data and analytics. These tools might shave a few minutes from reporting or help speed up the financial close, but they do little to reduce or simplify your ERP data models. The problem is that most ERP solutions require separate data models for analytics and transactions: separate tables and datasets, which are often multiplied for different business units, functions, and industries, all requiring updating and verification. A simpler data model is what allows business processes to be more efficient while also simplifying the IT landscape.
A shift in the finance function has occurred due to more and more businesses adopting dynamic finance analytics technologies. For finance professionals, the new model will focus less on accounting skills and more on data analysis, financial modeling, and communications expertise. That will result in a much more dynamic workforce, with analysis, interpretive skills, and data-driven insights providing it with a much deeper sense of its value. This shift, which will enable finance to play a bigger role in guiding performance and strategy across the enterprise, will project well into the future of the finance role.