How to Achieve Self-Service Data Transformation for AI and Analytics

Data transformation is the critical step that bridges the gap between raw data and actionable insights. It lays the foundation for strong decision-making and innovation, and helps organizations gain a competitive edge. Traditionally, data transformation was relegated to specialized engineering teams employing complex extract, transform, and load (ETL) processes using highly complex tooling and code. […]

The FAAR Framework for Consuming Insights from Data and Analytics 

Faced with overwhelming amounts of data, organizations across the world are looking at leveraging data and analytics (D&A) to derive insights to increase revenue, reduce costs, and mitigate risks. McKinsey found that insight-driven companies report EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to Forrester, organizations that use data and insights […]

Four Perspectives on the Art of Data Analytics

As data science professionals, we are often viewed as people who draw conclusions based only on data and minimize other factors. This perception usually becomes contentious when the insights and evidence from the data are inconsistent with somebody else’s “hypothesis.” Or we are confused and maybe frustrated when “qualitative” analysis trumps quantitative analysis. The next time […]

Data Catalog, Semantic Layer, and Data Warehouse: The Three Key Pillars of Enterprise Analytics

Analytics at the core is using data to derive insights for measuring and improving business performance [1]. To enable effective management, governance, and utilization of data and analytics, an increasing number of enterprises today are looking at deploying the data catalog, semantic layer, and data warehouse. But what exactly are these data and analytics tools […]