Data Science & Analytics: A Catalyst for Operational Effectiveness
As a seasoned data science and analytics professional (6-ish years), I have fully bought into the hype of data science and analytics and the tremendous value the discipline can have on an organization. This is not because I have drunk the buzzword “Kool-Aid” on this subject as widely portrayed on social media, at conferences, or in interviews with “experts”, but my fascination stems from my years of experience in and being a part of organizations that have prioritized data-drivenness as a core value that has led to business success.
However, based on largely anecdotal evidence, it appears that data science and analytics success stories tend to be few and far between. It seems like a lot of organizations look at the discipline as a magic bullet formula that will automatically give them a competitive advantage over their competitors. This got me thinking, “should data science & analytics be viewed as part of an organization’s strategy or simply a tool for operational effectiveness?”. The answer is it can be both! In this article, I will explore how data science and analytics can be used as a catalyst for operational effectiveness, and in another, I will explain how the same can be leveraged to create a strategic or positional advantage.
Before we dive into the details, let’s first understand the difference between strategy and operational effectiveness. Michael Porter, a leading voice in the field of strategy, defines it as “the creation of a unique and valuable position, involving a different set of activities”¹ whereas he describes operational effectiveness as the methods that a company employs to optimize how they create, produce, sell, and deliver their products. According to Michael Porter, organizations can gain tremendous advantages from operational effectiveness, as it enables them to produce things faster, with fewer inputs and defects than their rivals. For example, many Japanese firms in the 1970s and 1980s achieved great levels of operational effectiveness previously not seen in the industry. These organizations did so by implementing practices such as total quality management and continuous improvement². Today, we are once again seeing organizations achieve great levels of operational effectiveness by leveraging the practice or discipline of data science and analytics.
Let’s explore three examples of how data science and analytics can play a role in improving operational efficiency in key areas and actions of a business.
Creation of products
From understanding direct and indirect customer feedback to having a strong experimentation framework, data science & analytics can play a major role in new product development. For example, organizations can leverage advanced analytics to identify features or products that customers consistently request in product reviews or satisfaction surveys. Product Managers/ Owners can (and probably should) develop data-driven product strategies and experimentation frameworks that enable them to validate minimum viable products. These frameworks will also enable them to pivot early if the data suggests. Experimentation creates efficiencies in the new product development process by providing insights that enable organizations to build, measure, learn and iterate quickly and cost-effectively.
Production & Delivery
Nowhere is the impact of data science & analytics more promising than in the field of operations and supply chain. Organizations like Amazon and Uber lean on advanced analytics and data-driven methods to build out sophisticated but efficient operations. From improving demand forecasting through predictive and prescriptive modeling, employing real-time data for optimizing and redirecting shipments that are affected by disruptions, leveraging the internet of things (IoT) to proactively diagnose and fix faulty machinery, and using cognitive sourcing to improve supplier selection, the use cases of data science & analytics in the improvement of operational effectiveness are endless.
Sales
According to a survey by Mckinsey of over 1000 sales organizations, 53% of those organizations that were rated as high performers, ranked themselves as effective users of analytics. Use cases in these organizations include better prediction of sales to help with optimal inventory and logistics management, smart lead generation strategies, data-driven cross-selling and upselling techniques, better customer segmentation and prediction of customer lifetime value, and much more. With the exponential growth in customer and transaction data available to most organizations, sales leaders can leverage data science and analytics to stay competitive by understanding how and when to sell to their customers to maximize revenue, while maintaining a strong customer experience.
As promised, I have highlighted various examples of how data science and analytics can be used to improve operational effectiveness! I plan to dive into each of these use cases in further detail in future articles, to provide not simply a technical understanding of the applications, but how organizations can leverage these techniques to improve their competitive advantage in the marketplace.
See you soon!
¹ What is Strategy — Micheal Porter
