Whether companies refer to results, outcomes, ROI, or case studies, Big Data and data science are finally moving beyond the hype and proving to deliver dividends over time. Several new Big Data technologies and predictive tools have been launched to meet the growing demand within business and technology groups to harness the constant growth of both structured and unstructured data within and outside of the enterprise. But such technologies and tools won’t be effective unless you define the problem to be addressed.
Most data science initiatives start with a proof of concept (PoC) or in some cases with a proof of value (PoV) if the foundational concept is clearly established. Developing a pipeline of PoC’s can be extremely helpful through working sessions with data scientists, business subject matter experts (SME’s), data experts, and leaders. Following this, prioritize PoCs by stack-ranking each of them based on business value and ease of implementation which factors in availability of data, granularity, and quality.