Demystifying Data Science for Decision Makers

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One of our core missions at Dataiku is to arm organizations with the ability to make better day-to-day decisions with data. But how can those who hold the responsibility to make those decisions (i.e., business leaders and executives) do so without data science? The short answer is: They can’t and, particularly in light of the global health crisis, shouldn’t — per McKinsey, “most high-performing companies” have increased their investment in data science and AI amid the COVID-19 crisis.

At its core, data science — which is frequently lumped together with machine learning — is a field that uses processes, scientific methodologies, algorithms, and systems to gain knowledge and insights across structured data (data which is highly organized, i.e., names, dates, addresses, credit card numbers) and unstructured data (data that is not structured in a predefined way, i.e., images, video, audio, social media data). The definition can vary widely based on business function and role but, in this article, we’ll break down why business stakeholders need to embrace data science to bolster their decision making. They can:

Decision makers can make more informed choices by using their historical data to make predictions. Predictive analytics aim to predict what is going to happen and aren’t valuable unless they are actionable. Examples of popular predictive analytics use cases include churn prevention, demand forecasting, fraud detection, and predictive maintenance. With the example of churn prevention, the goal would be to figure out what the customer is ultimately going to do and when so that the organization can intervene and hopefully avoid the churn (or at least mitigate the risks associated with it).

When collecting and using the right data, the prep work to get to the decisions (i.e., data wrangling, processing, and blending) can be automated without much heavy lifting (i.e., financial reporting that involves a myriad of data sources). For example, many banks that grant loans use credit scoring systems to predict their clients’ credit worthiness. Data science and machine learning can increase predictive power by analyzing more data from more sources, faster, to make credit decisions (often better than a human analyst). 

It is important to note, though, that not all decisions should be automated and, in all instances, there should be a human involved in the machine learning workflow to identify risks and make any necessary changes. Further, by doing so in a centralized platform, models determining credit risks and loss become more transparent and interpretable for professional staff — even those without a technical background. 

By applying data science to operational procedures, decision makers can more efficiently implement changes and monitor if they are successful. With the example of supply chain optimization — which impacts every industry, from retail to manufacturing — data science and machine learning can enable companies to improve logistics and determine factors that affect performance, thus increasing productivity. It helps especially built-to-order producers, as the technology helps harmonize constraints automatically. 

While all of the above information is well and good, it will serve no purpose to decision makers if they don’t loop in their counterparts and staff to play a pivotal role. Data science and self-service analytics act as the catalyst to help decision makers generate business value and enable everyone below them to do so as well, because the more data is used in day-to-day work, the more comfortable people (across roles and departments) become using data. By making the use of data an everyday occurrence, decision makers undoubtedly benefit — not only because data projects can make tangible business impacts, but because they, in turn, empower their workforce to make more informed decisions in their jobs as well.