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The Top 7 Skills Needed to Build a Data Product
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The Top 7 Skills Needed to Build a Data Product

Today, anywhere you look; data is at the crux of everything: the trends you track, the social media interactions you have, the equipment you monitor, and more. but this data is only good if you have the capability to analyze and process it in a way that it solves a particular need. No wonder, every product or process today is trying to evolve as a data product.

Companies like Google and Amazon have long been relying on data products to increase engagement and experience; but the opportunity extends far beyond the tech giants: today, every company, irrespective of its size or type, can indeed must, build its own data-powered products to improve processes and decisions.

Whether you want to build a data product that allows users to compare and buy products or curate an enterprise analytics engine that interprets data to derive insights that drive operational workflows, data products are everywhere to aid the decision-making process. So, what does it take to build a data product today?

While the lifecycle of a data product is more or less very similar to standard software product development, the data component adds an extra layer of complexity that changes quite a bit.

Here are the top 7 skills you need to build a data product:

Product engineering: One of the first skills to build a good data product is product engineering. Using a combination of statistical methods and tools and strong domain knowledge, this skill can help provide answers to many questions while building the product, including what the current market environment is, what product concept will work, what is the expected budget, feasibility, and ROI, what features to build, how to create the prototypes, how to test the product and against what parameters, etc.

Product design: Just building a data product that delivers modern features or that collects and analyzes information is not enough to ensure its long-term success. You also have to ensure the product is designed in a way that is easy to use. Right from creating a good visual and UX design to ensuring good communication and integration with other products in the ecosystem: having the right features included in your product and ensuring they can be easily used by business users is the only way to have your product solve the purpose it intends to solve.

Programming: A good data product is not just a result of incorporating modern data analytics tools and processes; it also requires organizations to have top-notch programming skills to be able to build a product that is modern and intuitive. Proficiency in the latest or most appropriate programming languages not only helps in building a product that is innovative and easy-to-use; it also ensures your data product is effective, reliable, secure.

Machine learning and AI: For a data product to be really effective, having skills in machine learning and AI is extremely vital. Since machine learning sits at the intersection of data science and software engineering, it is a core skill needed to build a working product that performs data analysis autonomously and with minimal human supervision. In addition, you also need resources to be trained in associated technologies such as deep learning, neural networks, natural language processing as well as the optimization of machine learning algorithms. This is essential to build a future-ready product.

Data modeling: Data modelling lies at the foundation of any data product, as it enables the collection of clean, interpretable data that businesses can use to make decisions. So, if you want your data to tell the perfect story, you need to have the latest data modeling knowledge and skillset. This includes managing how data flows in and out of your system, how it is treated, how the different points connect and interact with each other, and more.

Reporting: Since data products must be able to furnish appropriate and accurate dashboards and visualization, another skill that is required is the ability to design and build powerful reporting and visualization. Resources with statistical competence in dealing with numbers are important to ensure all the heavy-lifting is done by the product while only the most relevant information is presented to users in an easy-to-understand format. Presenting only the right information in the right form ensures interpretation and decision-making are left in the hands (minds) of users.

Security: Given the fact that data products capture and process a humongous amount of critical data, these products must possess the highest level of security while ensuring compliance with necessary guidelines and regulations. This makes having security skills a critical requirement for building a highly secure and compliant data product. Resources that have knowledge and experience in using modern security tools can help in securing all types of data that goes in and out of the data product. They need to be able to map out security vulnerabilities and constantly update existing security mechanisms to reduce the risk of data loss, prevent the occurrence of a data breach or theft as well as ensure governance and compliance.

Achieving business goals and objectives gets extremely easy when organizations can make informed decisions relying on insights from data products. Given the constant surge in the volume, velocity, and variety of data that is being generated, data products are a necessary evolution. Building such a data product needs skills across product engineering, product design, programming, machine learning, data modeling, reporting, and security. That’s how to deliver true value to users through highly functional, brilliantly designed, and uber-usable date products.