You're developing a product for your data science business. What's the most important factor to consider?

In the same way that a book cannot be written without the author having a message to convey, a Data Science solution cannot be developed without seeking to meet a business or specific audience need. Identifying the problem you want to solve or the purpose you are trying to meet should NEVER be carried out after work has begun, but rather be initiated to solve a problem. This reversal of flow ends up being the reason for most, not to say most, failed projects in Data Science.

1 ?????????????? ???? ???????????????? ??????????: By prioritizing market needs, you ensure you're building a product that solves a real problem for a specific target audience. This increases the likelihood of product adoption and commercial success. 2 ?????????????? ?????????????????????? ????????: Validating market needs helps you avoid investing significant time and resources into building a product that nobody wants or needs. 3 ?????????????? ?????????????? ??????????????????????: Understanding market needs helps shape your product definition, ensuring the features and functionalities directly address customer pain points and provide them with significant value

Coming from a Product and data science background , i can clearly say that we need to do a deep research about the problem we are trying to solve and need to analyze the market and see what are the other solutions in the market, how mature are they ? Is this a green field development ? need to speak to potential target group and need to spend time analyzing the problem we are trying to solve .We also need to analyze how our product can solve the problem ? what are our USPs ? what will play in advantage for us and so on.We should analyze the time and other resources we have to build this product? is this the best solution for the problem? We need to answer all these before developing a product

In my experience as a data scientist, defining the solution is paramount when developing a product for our business. It's akin to mapping out the blueprint before constructing a building: specifying features, functionalities, and performance metrics ensures we're on track to meet our clients' needs effectively. Moreover, considering technical nuances like data sources and algorithmic fairness adds a layer of responsibility to our innovation, ensuring our solutions not only work well but also uphold ethical standards in the ever-evolving landscape of data science.

1 ???????????????? ?????? ??????????????: Once you've validated a market need, you can define the specific problem your product will solve. 2 ???????????? ?????? ????????????????: Based on the validated need, you can design a data science product that offers a compelling solution with a clear value proposition. 3 ???????????????? ?????? ???????????? (??????????????): Even after initial validation, continue to gather market feedback throughout the product development process to ensure your product remains aligned with evolving customer needs.

After Identifying your problem statement and defining the market , next step is to define the solution. It need to be as detailed as possible , it need to cover all the scenarios, it need to also analyze and define the technical feasibility , cost benefit analysis , product roadmap, infrastructure, data source , availability , Explainability of solution and also need to take care of Responsible AI as well as data security security, ethical aspects and model biasness. All these need to be studied in detail and need to be documented.Should also communicate all these to all relevant stakeholders and can also perform tests and PoCs to prove the workability of solution

Market research is key in product management. You need to learn market and analyze it before you finalize on your product . You also need to go a step deeper after you develop solutions and need to check whether any other players in market are providing similar solutions, what are USPs of other products in the market, what are they lacking ? where are you going to place your product? what need to be the messaging of the product ? who are your target audience ? their buying pattern , behavior , capacity .All these need to be researched in detail .Also you need to analyze using market frameworks like Porter's five force model or SWOT analysis and see what will play in your favor , what are your strengths and weakness and so on.

1 ?????????? ????????: Focus on ???????????????????? ?? ?????????????? ???????????? ?????????????? (??????) that offers core functionalities to address the core user problem. This allows you to gather early user feedback and iterate quickly. 2 ?????????? ??????????????????????: Consider adopting an agile development methodology that emphasizes rapid prototyping, iterative development, and continuous improvement based on user feedback.

Testing the product before launching it in market is very much needed.Need to test various aspects and scenarios.Need to ensure its solving all the business problems it supposed to solve .In addition to the performance evaluation with model related metrics, its also needed to test the biasness of the model , test the security aspects of model , load balancing or load managing , access and other related tests before launching the product in market

1 ???????????? ????????????????: Develop a targeted marketing strategy that reaches your ideal customers. Utilize relevant channels and messaging to communicate the value proposition of your data science product. 2 ?????????????? ??????????????????: Create valuable content (e.g., blog posts, case studies, webinars) that showcases your expertise and educates potential customers about the problem your product solves.

An obvious problem that does not get stated enough is that when marketing your data science product you have to be able to highlight the value of your product, and often describe some characteristics of it, without giving away the 'secret sauce,' so to speak. Unless you are in a small subsection of the market, odds are you will be using data that people can scrape or gather for themselves and using tools that anyone with access to GPT could theoretically replicate with enough trial and error. What this means is that you need to be able to identify what your product is and how it is unique without sharing how someone else could replicate it, which can sometimes be a challenge.

O mais importante é que o seu negócio com ciência de dados resolva um problema e desenvolva um produto que gere valor no amplo sentido da palavra. Pergunte-se: pra quem estou criando esse produto e o que ele vai resolver pra esse público? Daí pra frente centre no usuário. Fa?a testes de usabilidade (espero que tenha feito um estudo de viabilidade). Seu produto n?o precisa ser feio, mas antes de beleza ele deve ser funcional. O usuário n?o deve se sentir perdido ao utilizá-lo. Utilize seu conhecimento em dados para facilitar e n?o pra complicar.

1 ??????????????????????: Design your product with scalability in mind, considering future growth and potential increases in user base and data volume. 2 ?????????????? ????????????????: Develop a pricing strategy that aligns with your target market, product value, and long-term business goals. 3 ???????? ???????????????? ?????? ??????????????: Ensure your product adheres to all relevant data security and privacy regulations to protect user data and build trust with your customers.