Informatica is acquiring GreenBay Technologies, a Wisconsin based startup that it funded to fill in a gap with its machine learning capabilities when it comes to matching data entities and the schema that represent them. Informatica's latest acquisition extends machine learning capabilities into matching of data entities and schemas. And the acquisition came out of Informatica's first formal partnership effort with a university. The new capabilities will find their ways into Informatica's existing master data management (MDM), enterprise data catalog, privacy, governance, and data integration offerings. The company, GreenBay Technologies, was co-founded by a University of Wisconsin at Madison computer science professor and began operation with ties to the university and its alumni research foundation.

A new crop of data preparation tools, powered by machine learning, are changing the way that businesses prepare their data for optimal use. Here's what you need to know. At analytics-capable companies, just about every business department is asking for more data to help them make better decisions. But many of these same companies are also struggling with one common problem: It simply takes too long to transform raw data into meaningful sets that analysts, data scientists, and business users can actually use to their advantage In fact, according to a report from Forrester, 59 percent of these decision-makers say that data prep is the key bottleneck they must over to achieve better business intelligence through analytics. One of the primary problems is that data preparation is a time-consuming, often-manual process that relies heavily on human labor.

Data is the new language today. Data leads to insights, and insights help organizations to make actionable business decisions. However, sourcing the data and preparing it for the analysis is one of the tedious tasks organizations face these days. Analysts devote a lot of time in searching and gathering the right data. According to a research firm, analysts spend around 60 to 80 percent of their time on data preparation instead of analysis.