In this special technology white paper, The 5 Key Challenges to Building a Successful Data Science Lab & Data Team, you’ll learn how a Data Lab establishes an effort to answer business needs by making sense of raw information. Data labs are intended to create critical mass within the organization that enables them to reach the level of innovation required for new data-driven products.
The age of data is here. Sensors, cameras, security monitoring systems, software, hardware, the Internet, and even humans themselves all have one thing in common: data. Countless bits & bytes of binary information that represent the beating heart of our modern technological world. As technology has increased, so has our interest in tracking its progress and trying to learn what it all means. Enter Big Data: a holistic term that aims to encapsulate the sheer massiveness of this concept of “information.” As data storage capabilities have grown, the world of IT has made a significant effort to collect data… although, up until recently, most people and organizations really didn’t know what to do with it. We’re collecting the Big Data – now what?
As the 21st century starts to pick up its pace, we are discovering an arena dedicated to making sense of Big Data: the data lab. In short, a data lab works to answer business needs by yielding insights found in raw data from a variety of structured and unstructured sources. A deeper look, however, reveals a much more nuanced purpose. Data labs, which are basically the equivalent of data research and development departments launched internally by organizations, are intended to help businesses develop solutions from their raw data. Business units can use their data lab as a sandbox to explore possibilities that, due to cost restrictions, they would ordinarily never be able to pursue. Terabyte upon terabyte of raw data can be cleansed, formatted, and modeled to reveal business opportunities in new markets… often in ways that were never before imagined.
The white paper includes the following sections:
- Time-based Challenges
- Collaboration – or Lack Thereof
- Skill-set Disconnect
- Platform Incompatibilities