If you are in the process of building a data science lab or if you are already working on one, this ebook is for you. This 21-page ebook will teach you how to address, avoid, and fix the main challenges that come up in data science laboratory environments.
Why you should read "The 5 Key Challenges to Building a Successful Data Lab"
Companies that create their own data labs face a number of obstacles as they ramp up their operations. Many of these challenges are centered on the need for collaboration between IT and business profiles. Common mistakes, such as using static data or not thoroughly planning a solution’s implementation, can trip up a young data lab before it completes its first proof-of-concept. As data labs mature, the challenges do not go away but instead take different forms. For instance, deciding whether to stick with older technologies (SAS, SPSS) or opt for newer approaches (R, Python, Spark). This ebook aims to address these challenges and offer solutions that are applicable to all data labs whether they are just starting out or are already established.
This ebook is organized by type of challenge. In each part, you'll find possible solutions, tips, decision points, and a solution summary:
1. Time-Based Challenges - Or why it's easy, but inoperative, to work with old data.
2. Collaboration or Lack Thereof - Or why attempting to collaborate without a common ground is nearly impossible.
3. Skillset Disconnect - Or how the skills of analysts, developers, and statisticians are mutating. Quickly.
4. Platform Incompatibilities - Or how early miscommunications lead to long-term data incompatibilities.
5. Growth - Or what to do when that unavoidable build or buy decision point comes around.
To download your free copy and get started scaling your data team now, just click here! Or, for another take on data team collaboration, check out our interview with Mona Vernon, Vice President of Thomson Reuters Labs.