Analytics

The integration landscape is changing. According to Gartner, “Two-thirds of all business leaders believe that their companies must pick up the pace of digital transformation to remain competitive.” One of the byproducts of this increasing pace is the desire to get results quickly. In a cloud-first world, that means expectations are changing for how products are trialed, procured, and billed. People expect things to be simpler, faster, and more intuitive.

As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.

Data in organizations has become a significant risk issue. In recent years, new risks have emerged that have led many organizations to centralize their data and seek out new ways to mitigate their data risks through tighter data governance. Regulatory change, like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the US, have focused on data privacy.

Data lakes: smooth sailing or choppy waters? In May, Talend announced its support for Databricks’ open source Delta Lake, “a storage layer that sits on top of data lakes to ensure reliable data sources for machine learning and other data science-driven pursuits.” What does this mean for your company, and is Delta Lake right for you?

May 25, 2019 marked the one-year anniversary of the European Union’s (EU) General Data Protection Regulation (GDPR) coming into full effect. This milestone serves as a timely reminder for any business in the EU or doing business with EU residents on both the implications of failing to protect data and the procedures needed to prevent this from happening.