3 Ways Thinking Like a Data Scientist Helps Make Better Business Decisions

In this special feature, Sanjay Vyas, CTO at Planful, discusses how technical departments can interpret data to guide the trajectory of the business. A highly accomplished technologist, Vyas has held senior software engineering and development positions in SaaS companies for nearly 25 years. He holds several patents in payment authentication and unstructured data analysis, and is the co-author of “The Cloud Security Rules: Technology Is Your Friend”. And enemy.

The language of business is changing. Among corporate FP&A (financial planning and analysis) professionals, day-to-day work life has always focused on numbers compiled into spreadsheets. Investigating and making inferences from these numbers, while not exactly a simple process, was at least familiar in scope.

In recent years, however, tools for individuals and teams in FP&A roles have advanced. COVID-19 was a big instigator; it forced organizations away, which changed the fundamental nature of analytics. With teams suddenly distributed, collaboration and automation solutions were imperative. Financial planning has gone (or continued in) the cloud, and teams have learned to share their thoughts not between cubicles or conference tables, but within apps.

AI/ML (artificial intelligence and machine learning) technologies have also started to appear. Many processes, such as identifying errors and anomalies, can now be better done by AI. New forms of AI tightly woven into FP&A applications have helped this process. Financial data is different from other forms of digital information; there are patterns in the financial numbers that only specialized AI/ML, rooted and deeply embedded in planning applications, can understand and interpret.

Between workflow change and the proliferation of new FP&A solutions, today’s business analysts need to think and work like data scientists in order to maximize their performance.

A change of approach

Data science is about streamlining the process of identifying trends and insights from data. It’s about applying the latest tools, not as technicians but as skilled digital explorers. A data science mindset allows business analysts to get more out of the massive amount of data businesses collect today. Three aspects of data science help in this pursuit:

It helps analysts make more accurate forecasts. Finance professionals need to be able to quickly and correctly address discrepancies between forecast and actual numbers, at the point of use, to uncover trends and infer projections. Tools from the data science world support this type of quick and often hidden querying.

It allows users to quickly find outliers. Using AI/ML to detect anomalies eliminates time-consuming manual scrutiny at each monthly or quarterly close. Plus, it remembers everything while learning and improving from every feedback it receives. AI/ML is revolutionizing the error detection process, increasing confidence in FP&A numbers and reducing business risk.

It allows teams to analyze larger amounts of data. Scalability has taken off; now, instead of looking back at perhaps three years of operational and financial data, analysts can benefit from five, eight, ten or more years. It also allows users to cross-reference more data from more areas of the business. Marketing, customer retention, HR, and other data stores can be compared to illuminate business patterns and glean new insights.

Fortunately, thinking like a data scientist doesn’t mean becoming a “data mechanic.” The tools and technologies of the data science world are easier to understand and use than ever. As a result, companies are gearing up for democratized solutions that can meet business needs, without requiring a graduate degree in data science. Teams can automate mundane tasks and move on to higher value work.

It’s clear that businesses need to find faster and more efficient ways to take advantage of all the data collected today. Higher levels of data literacy are needed in all corners of the organization. The sooner analysts can begin to embrace the disciplines emerging from the field of data science, the sooner they will be able to achieve these new levels of strategic insight. The horizons have changed and the tools are there to make it all possible.

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Sean N. Ayres