Architecture blocks that are crafted for solving data science problems need an open ended approach to accommodate huge volume of data flowing into the data warehouses in unsteady state, ability to convert structured to unstructured data on real-time, setup mathematical modeling pipelines that are capable of processing humongous quantities of data with optimal solution convergence, delivery of varied (predictive, prescriptive & descriptive) insights with minimal latency etc.
Talent Mix Challenges
The next major frontier of challenge lies the composition of talent mix for the productization. The inherent heterogeneous nature of artificial intelligence products necessitates that the product team shall comprise of experts from various fields that include software architects, mathematicians, statisticians, functional & business analysts, business managers, devop engineers, programmers and finally the product leader who brings in the data leadership for the project execution.
The solution execution journey for big-data products shall be an ongoing, iteratives process in which the business and product team stakeholders take measured and manageable risks. Various constructs shall evolve while managing the objectives, risks and the value chain which shall exhibit unpredictability and volatility and are paralleled as Complex Adaptive Systems. A strong foundation on customer-centric practices is necessary to co-evolve with chaos and drive towards clarity in execution.
Seamr’s multi-disciplinary specialists with strong intrinsic problem solving skills along with the deep business insights can build bespoke AI-powered products for any organization.