GDPR: What It Means for Analytics and Data Management
The deadline for complying with the European Union’s General Data Protection Regulation (GDPR) is fast approaching. The EU calls it “the most important change in data privacy regulation in 20 years” – and that’s no exaggeration. Beginning May 25, 2018, organizations that are in non-compliance may face heavy fines, not to mention damage to their reputations. How does this regulation affect the way your organization uses data for analytics and business intelligence? What do you need to do from a data management perspective to ensure compliance – not just by May 25, but into the future?
April 30, 2018
Analytics Everywhere: Building Analytics Applications for Driving Business Value
Analytics has become mainstream, and TDWI research indicates that the vast majority of organizations have adopted technologies such as dashboards and visual analytics. However, as organizations mature along their analytics journey, they are looking to embed their analytics into devices, applications, and systems. Embedding analytics layers analytics into another application or process and brings the results of analysis to the decision maker through applications that run the business. The result is opening up analytics to more users and making analytics relevant, actionable, and more valuable.
May 30, 2018
The Automation and Optimization of Advanced Analytics based on Machine Learning
However, embracing machine learning successfully is challenged by ML’s serious data requirements. In development, designing an analytic model depends on very large volumes of diverse data. In production, an analytic model created via machine learning again needs voluminous data, so it can learn and improve over time. In turn, managing big data for machine learning demands a substantial data management infrastructure and tool portfolio.
May 31, 2018
Modernizing Data Analytics: Moving Beyond Hadoop
As an open source platform that simplified the ability to develop distributed and parallel applications, Hadoop lowered the barrier to entry for many smaller organizations interested in big data analytics. Some people have gone as far to suggest that Hadoop be used to replace their existing data warehouse.
June 5, 2018