They orchestrate the interplay between high profile professions for go-to-market deployments.
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This post will be the first in a series of posts regarding product science and the duties of product scientists in organizations. In general, I will present methods and concepts in this post before dissecting them in subsequent articles.
What basically is product science?
It applies science and engineering to products or industrial processes to tackle problems. It facilitates product design, development, testing, and improvement while ensuring that they meet user requirements. There are various disciplines or subdisciplines. These are a few examples:
— Restructuring science is a means of considering how to manufacture, package, and sell items in a manner that optimizes resource utilization and satisfies quality criteria.
— The objective of the science of resource management is to enhance the economics of manufacturing products by reducing processing costs and ensuring consistent performance throughout the product’s life cycle.
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— Consumer behavior science assists in determining what consumers want and how to achieve their needs. In addition, this strategy can determine how individuals utilize media, brands, and other things.
— Product safety science is the study of how to use products to keep people safe (product safety). This strategy entails conducting tests and developing methods for testing even the finer details. This approach is utilized by numerous organizations across diverse industries, including pharmaceutical, food, and automobile manufacturers.
Product scientists work with diverse engineering teams to build, test, and enhance products. Researchers in this discipline frequently work with other professionals, such as chemists and biologists, to develop and improve existing products and invent new ones. They should be knowledgeable about the complete product development process and devise strategies to improve its dependability. Product scientists are in charge of creating AI solutions that use machine learning techniques. However, this is far from the end of the story.
Product scientists must be knowledgeable in various topics, including product development, artificial intelligence (AI), user experience (UX), and operations.
Before delving into the best practices and general techniques of product science, it is critical to examine the link between AI and product science.
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Product scientists with AI expertise?
Product scientists will eventually have to add digital or technical components to a product during its development or maintenance life cycle. Knowing how much technology-focused techniques function would provide them with even more relevant data and insights, especially if they have a background (ideally a deep one) in AI. I would go a step further and say that people should advance to product science after gaining expertise in artificial intelligence, which I believe is crucial in product management.
Product scientists should understand how AI may be utilized to improve and expand the use of their products. AI technologies are always evolving; therefore, product scientists must stay current to make informed decisions about incorporating them into their products. In addition, many aspects of life, such as economics, health care, and transportation, rely increasingly on artificial intelligence. This means that organizations searching for methods to improve their products will look for product scientists with this experience.
Many recent machine learning approaches necessitate a large amount of data expertise, normally best handled by someone familiar with computer science fundamentals such as programming languages and data structures. An AI background would also be quite useful in this scenario. Finally, because today’s marketplaces are so competitive, any advantage gained through innovation (in your background), such as deploying cutting-edge technology like AI, will undoubtedly pay off in the long term.
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The interplay between product science and UX
Some product scientists may be adept at UX since they frequently collaborate with users to determine their requirements, build features to match them, and test products. They may also determine whether a design solution is feasible and satisfies the consumer’s needs. Finally, effective product science abilities can assist designers in creating better user experiences by demonstrating how people use products and how to best set up their interfaces. Nevertheless, it is not as straightforward as this generalization implies.
For various reasons, a product scientist should be familiar with UX. Here are a few examples:
— UX is one of the most important aspects of any software or tech product, and ensuring that customers appreciate interacting with products is crucial to their success. Product scientists who understand user experience can assist in preventing design errors from making something difficult to use. Through flow analysis and prototyping, they may also enhance user involvement, and they can design for various use cases.
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— An exceptional user interface can alter how people perceive a whole product, be it a website, an app, or something else. Conversely, poorly designed interfaces can cause users to lose interest in a product and result in virtually every negative outcome.
— To design effective software products, developers must consider various aspects, such as performance requirements, response time, and user-friendliness. Effective UX planning facilitates comprehension of which elements are included first and where they belong within the overall architecture scheme.
Here are the methods and procedures that, in my opinion, are applicable today for entering and flourishing as a product scientist (agnostic of industry or tradecraft expertise).
1. Exploratory research entails performing analytical and investigative tasks to unearth unique, previously undiscovered ideas or insights. Questionnaires, interviews, and focus groups are examples of such procedures.
2. Insights generation: This strategy aims to develop practical answers by drawing on current facts in novel ways and applying cutting-edge thinking from various fields.
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3. Product analysis and user interface design: Various aspects come into play when developing consumer products, including far deeper user needs at the emotional level, physical capabilities, and usage environment. These are only a few components that may be required for analysis. Things must be simple enough for large-scale assembly (for example) without sacrificing quality when designing for manufacturability.
4. Multi-criteria decision making: When faced with questions about a technique for exploration, it can be difficult to objectively evaluate various possibilities without allowing personal biases to enter the picture. Through tactics such as loss forecasting and value chain mapping, this strategy aids in the resolution of such challenges.
5. Sizing and forecasting: This includes determining how many units of a product will be sold or whether a new product line will be profitable. It may also entail determining the necessary resources, such as raw materials or a user base.
6. Mathematical modeling and simulation: Engineers use technologies to simulate physical processes within items to understand how they work.
7. Product development process improvement: Organizations can save time and money while delivering high-quality products by implementing best practices across the product development cycle.
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8. Metrics management: If returns on investment (ROI) are expected, it is critical to maintain track of all intangibles associated with a business; metrics offer managers objective methods for measuring success. While an ROI are generally monetary based in the context of #4, it is not the only area of categorical focus under the auspices of metrics management — this one specifically will receive its own post (as noted at the top as a part of this multi-part series around product scientists).
9. Becoming lean and agile: To reduce time-to-market, improve error rates, and increase agility, rethink how products are conceived, developed, and executed.
10. Collaborative research (at least based on UX research and design thinking approaches): Researchers from many fields pool their expertise and skills to create novel solutions in this undertaking.
11. Predictive analytics: Having a background in machine learning (ML) algorithms that can find patterns or trends that are not immediately apparent is, in my opinion, a big differentiator across categories. Big data has democratized the availability of machine learning (ML) capabilities, allowing organizations with little technical resources to utilize them.
12. Regulatory compliance: Countless items must adhere to government regulations about safety, health, privacy, and the environment. Therefore, product scientists must be conversant with these requirements to develop products that satisfy them. Therefore, product scientists must be conversant with these requirements to develop products that satisfy them.
13. Business and program design: Product scientists may be involved in various business functions, including market research, program and strategy design, product planning, and human resources management (direct operations).
14. Global sourcing: Increasingly, products are manufactured not just within specific nations but also across various international borders. Knowledge of global supply networks and their interconnected components allows organizations to make better-informed product development decisions.
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Parting Thoughts
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Anil Tilbe
