Presented by Pragyansmita Nayak
Enterprises globally recognize the emerging trend in data and analytics-focused applications and predict their investments will continually increase over the coming years. Corporations are depending on their Chief Data X (Scientist/Officer) to tactfully identify the right opportunities and grow the team strategically. The characteristics of the data are as diverse as the different domains with unexplored problems – broadly categorized into the three categories of structured, unstructured, and semi-structured data. Data Fusion for DataOps (not a typo for DevOps!) is critical for added advantage and effective Self-Service Analytics (and so many more – Descriptive, Predictive, Prescriptive, Cognitive, Domain Informatics, Infographics).
If you are just starting to consider data application, it is important to first set goals and define expected outcomes. Scientific databases following the mantra of defining "five questions" to focus the effort is a good start. This will additionally help foresee the need for Algorithmic Orchestration to accomplish the desired goals of Machine Learning and Deep Learning-based projects. "Everything is a GRAPH" – or a relational database – think about the different possibilities and define the problem scope early on.
As Data Scientists are working towards defining and solving a problem, specifically as the field is becoming more accessible with increased access to data, storage, and computing resources, they should not lose sight of the associated management needs – data ethics, model explainability, and Data Governance and lineage. We do need Data Analytics to solve our own problems as well – a recursive "Graph Analytics application for Analytics of Analytics"!! As complex as everything appears, we should not forget the power of KISS (Keep it Simple) – Occam's Razor should be prime.