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

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In this special guest feature, Sanjay Vyas, CTO at Planful, discusses how tech departments can interpret data to steer company trajectory. A highly accomplished technologist, Vyas has served in engineering and software development leadership roles at SaaS firms for nearly 25 years. He holds multiple patents in payment authentication and analytics of unstructured data, 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, daily work life has always focused on numbers compiled within spreadsheets. Investigating and making inferences from these numbers, while not exactly being a simple process, was at least familiar in scope.

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

AI/ML (artificial intelligence and machine learning) technologies have also started to appear. Many processes, such as error and anomaly identification, can now be better accomplished by AI. Helping this process along has been new forms of AI that are tightly interwoven into FP&A applications. Financial data is unlike other forms of digital information; there are patterns in financial numbers that only specialized AI/ML, ingrained and deeply embedded in planning apps, can understand and interpret.

Between the shift in workflow 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 Shift in 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 glean more from the huge amount of data that enterprises collect today. Three aspects of data science help in this pursuit:

It helps analysts make more accurate predictions. Finance professionals need to be able to address the variances quickly and correctly between predicted versus actual numbers, at the point of use, to uncover trends and infer projections. Tools from the data science world support this kind of fast, often hidden interrogation.

It lets users quickly find aberrations. Utilizing AI/ML to detect anomalies eliminates the painstaking manual reviews that consume so much time at each monthly or quarterly close. Moreover, it remembers everything while learning and improving from every piece of feedback received. AI/ML revolutionizes the error-spotting process, increasing trust 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 over perhaps three years of operational and financial data, analysts can benefit from five, eight, ten years or more. It also allows users to cross-reference more data from more areas of the enterprise. Marketing, customer retention, HR and other data stores can be compared to inform business models and glean new insight.

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

It’s clear that enterprises must find faster, more efficient ways to benefit from all the data collected today. Higher levels of data literacy are needed in every corner of the organization. The sooner analysts can begin embracing the disciplines coming from the data science realm, the sooner they’ll be able to achieve these new levels of strategic insight. The horizons have changed—and the tools are here to make it all possible.

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