Machine Vs. Operational Learning

Today’s economy, led by advances in changing economic cycles and communication, relies more on data science. Big data techniques, a significant component of data science and business intelligence, are utilized to harness vast amounts of data quickly for analysis. Increasingly, the volume and availability of granular data, coupled with highly specific and powerful analytical tools such as R and SAS drive organizations toward making more accurate predictions with the prospect of increasing sales and generating organizational efficiencies. These predictions help enable efficient supply chains, driving down costs for producers and leading to more expedient delivery of products and services for consumers.

The widely acclaimed global phenomenon known as machine learning helps push boundaries of analysis and decision making perspective. It is only in the past decade where machine learning has become part of the daily conversation. Machine learning in its essence is the ability of machines and computer based systems to learn without requiring human guidance. Deep learning is one of the features of machine learning where algorithms use artificial neural networks to form complex models. Deep learning algorithms learn beyond levels of human insight, and help organizations forecast pitfalls in their product demand curves and show prospective areas of customer demand yet to be realized by both, consumers and producers. Companies such as Netflix utilize machine learning and deep learning to gauge demand, set price levels effectively, and even create in-house productions based on latent (hidden) viewer demands. It is not a coincidence that Netflix produced shows relating to current events and culture trends experienced in society. Netflix’s viewing suggestions are generated according to the user’s preferences and are quite accurate. Many of these recommendations would never have been directly communicated by consumers to Netflix.

There needs to be a medley of human intuition and analytical insight in order to attain relevant outcomes.

Machine learning and deep learning help present the larger picture. However, the efficacy of the analysis depends on the ability of the teams involved to derive actionable insights that align with the organization’s goals. Machine learning needs to be paired with operational learning in order to form the right analysis. Operational learning represents an innate understanding of the industry on two levels. On the macro level, it refers to the knowledge of the product/service offering and that of the competitors’ offerings, and the awareness of consumer’s needs and that of the industry trends and standards. On the micro level, OL means having a familiarity with the organization’s goals, its implementation structure, its work culture, and the individuals involved.

“The intuitive recognition of the instant, thus reality… is the highest act of wisdom.” –  D. T. Suzuki

Often, textbook applications of techniques fail to achieve the desired results, giving data analytics a bad reputation.  There is no substitute for human insight and experience, which can be loosely termed as wisdom. There needs to be a medley of human intuition and analytical insight in order to attain relevant outcomes. The discussions between individuals and across teams surrounding a data science project are probably as valuable as the analysis itself. The data presents everything as it is, and provides latitude for the interpretations of everyone involved. These discussions help unearth truly latent variables, not directly included in models, because they don’t exist directly in the data.

Discovery of latent, yet important variables, provides an understanding that transcends the initial project scope and leads to insights beyond the team’s initial goals. For example, we undertook a data science initiative for understanding user search behavior for one of our clients. The analysis aimed at providing users with promotional links relevant to their search query. We analyzed user-behavior patterns using k-means clustering, a data exploration and dimension reduction technique, and further analyzed individual interactions using random forest decision trees. The project kept expanding in scope, and multiple business units (product development, marketing and finance) became increasingly involved. The project began with a single aim of categorizing search queries, but resulted in a process increasing paid clicks, enhancing user engagement and optimizing marketing spend, based on consumer’s relative value. The project fostered communication between teams, and increased their understanding of team goals.

Conducting a complete well rounded analysis is paramount for relevant and successful outcomes. However, machine learning needs to be paired with operational learning in order to generate value. A balanced implementation enables the mathematical models to be interpreted and implemented in the appropriate context and leads to actionable results, which should be the goal of any data science/analytics project. Machine learning projects with a clearly defined business objective from an operations perspective tend to have a high success rate since they are designed, from the very beginning, with the most relevant needs of the organization.