Data management professionals today face sea change upon sea change in the way data is done. They are gathering ever-larger and more varied amounts of data, trying out machine learning and navigating a new big data ecosystem of data management tools — all at once.
Still, essential tenets hold true, despite the rolling seas. Healthcare analytics is a case in point. Analyzing fast-arriving data can lead to useful insights, but handling that data is the first step, according to a practitioner in healthcare analytics.
“The important thing is to get the data in a format in which the machine can work on it,” said Manuel Salgado, senior data and analytics manager at healthcare giant McKesson Corp.
Salgado holds that an important first step in working with today’s data is to simplify data management. That can be hard, given the surfeit of new tools available in a big data ecosystem, including frameworks such as Hadoop, HBase, Spark and many, many more.
Eliminating data silos
To reduce complexity while building a data pipeline for analytics, Salgado and McKesson opted for a hybrid database from Splice Machine for some projects. Splice Machine is called a hybrid database because it supports both transactional and advanced analytics jobs. It is ready-built with connections to different big data ecosystem elements.
“We realized the ecosystem for big data is not as mature as traditional data management,” Salgado said. “We were dealing with a lot of components, and we looked for a way to make it easier.”
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