There has always been a major discussion and debate about the prerequisites that an individual must have or possess to take the route to be a decision scientist or how to leverage the modern techniques including Machine Learning, Deep learning & AI, advance analytics and use a data driven decision for actionable business outcomes.
The common perception being the person must be a programming nerd or a statistician or a scientist at least for leveraging the subject. To help us understand it better and optimize the right track that should maximize our learning, the following are the four approaches that might be the pivotal for applying it real life.
- A technology expert more inclined towards tools and hence can implement a model or an algorithm suggested. He takes it more from learning multiple technologies including open source (R, Python, Rattle, Orange, etc.) as well as COTS (SAS, IBM products, etc) and take this as an integration of diverse technologies under a portfolio and the learning outcome is this portfolio of technologies in the data science space.
- A data scientist who can design as well as implement algorithms/ machine learning approach and methodology based on business requirements and pain points of the clients/ business. This individual should equally stress on the design part as well as the implementation part and should be agnostic of technology and should be more focussed on best practices.
- A consultant who mostly focusses on problem solving and data driven decision science using these techniques and technology is also a way to do that. This individual should have a strong sectorial knowledge and the knowledge of machine learning, AI, data science and advance analytics should focus more towards a sector rather than a normal process or a vanilla approach. He should be translator where the focus should not stop merely from data to insights but to convert those insights into actions in line with the business objective and key performance indicators.
- An individual in a role who liaise between business and data scientists. The focus is not to be a data scientist but how to work with a data scientist. He may be more from a Project Manager, Analytics leader, recruiting the right talent, team building, risk mitigatory, pre-sales, analytics product manager, marketing or operational role.
We in AbsoluteInAnalytics through our innovative methodology and approach created through multiple iterations and embedded experience of seasoned professionals, data scientists, analytics leaders, consultants from the entire analytics landscape help in you asking the right questions and guide you taking the right approach. This helps in suggesting the optimized process for better value creation. Our focus of embedding technique, technology, process and people with strong sectorial flavour and hands on case studies through the best practices provides an experiential learning platform for a collective and collaborative learning.
Our focus and continuous research in AbsoluteInAnalytics is concentrating on how to scale machine learning, data science and AI to create the right and optimized learning trajectory for users / learners from diverse field and help them develop their own solutions in line with the customized business problem for a client in a sector in their real life.