Our appetite to discuss external data is boundless. Below is just a sample of recent thinking and resources to achieve this goal. If you have feedback or are searching for something specific, please reach out.
|Data Hub Maturity Model||This document seeks to summarize the stages of enterprises in terms of Data Hub maturity. Hopefully this can help open a conversation with industry about where enterprises are in their journey.||Download PDF|
|External Data Ethics||This document discusses the ethics of using external data in AI and ML. It advocates for an attribute-centric approach, with each attribute evaluated based on provenance, meaning, and biases. The degree of data usage is context-dependent and should be decided by team discussion and executive oversight. Special considerations are needed for external data including source verification and regular reassessment.||Download PDF|
|External Data Management - Applying the CDMC Framework||This document is a response to the exceptional CDMC framework and provides suggestions on key aspects that are relevant to external data use cases. Broadly, the key takeaway is that data leaders need to consider the risks, responsibilities and opportunities associated with harnessing external data and, across all aspects of the framework, how external data is necessarily a centrally managed function. Leading enterprises are, with the right investments and processes, establishing external data hubs and teams that finally start to deliver on the promise of value from external data.||Download PDF|
|Extracting Business Value from External Data|
|Every data practitioner understands the potential of external (or third party) data. The ability to enhance, on demand, one’s first party data with a rich set of data points is critical in building a differentiated value propositioning today’s data-savvy world. So why is adoption so slow? Demyst’s product team set out to get to the bottom of this question by conducting in-depth interviews with data experts across industries and regions. In this white-paper, we summarize our findings, share best practices in accelerating the adoption of external data and tips to optimize ROI for data initiatives.||Download PDF|
|The External Data Imperative||Commercial impact from analytics, i.e. by improvement over the status quo, is driven fundamentally by data and algorithms. Within data, one can simplistically think of rows and columns. More rows (observations internally), more columns (features - typically through external data), and a better analytical algorithm multiplicatively combine to drive value.||Download PDF|
Updated 2 months ago