Data analytics and data science are frequent terms across all industries these days. For those looking for long-term career potential, big data and data science jobs have long been a safe bet. This trend is likely to continue as artificial intelligence (AI) and machine learning become highly integrated into our daily lives and economy. The Tech Re public brought this topic to our attention in their article, “Top 5 ways to distinguish data science from data analysis.”
Today, data is the asset that businesses are gathering critical insights on to improve business performance and protect the asset. But who will glean insights? Who will process all the collated raw data? Everything is done either by a data analyst or a data scientist.
Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. Data science is a multi-disciplinary blend that involves algorithm development, data inference and predictive modeling to solve analytically complex business problems.
Making data accessible is something we know a little about. Whatever you are searching for, it is important to have a comprehensive search feature and quality indexing against a standards-based taxonomy.
Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
