What’s in the ebook?

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... such as 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.  

 

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21 pages (25 min. read)

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The 5 Key Challenges to Building a Successful Data Lab

This 21-page ebook will teach you how to address, avoid, and fix the main challenges that come up in Data Science Laboratory Environments 

Dataiku   is the software developer behind Data Science Studio. DSS is the all-in-one data science platform where teams collaboratively build end-to-end highly specific services that turn raw data into business impacting predictions. At Dataiku, we strongly believe that the business value that lays in predictive analytics is under-utilized because predictive services and products are too hard to build & run with most companies’ existing tool stacks. Indeed, companies have the skills sets, they have the data, they even have access to free and open source predictive technologies, but they are missing the platform that links data to people to technology. Dataiku's vision is a world where all companies, whatever their expertise, industry or size, can create their own data-driven strategic advantages by transforming their raw data into value, quickly.

ABOUT US

What you'll find in this ebook

I WANT TO READ!

This ebook is organised by type of challenge. In each part, you'll find Possible Solutions, some Tips, Decision Points, and a Solution Summary.

Introduction

1-3

1° Time-based Challenges

4-8

2° Collaboration - or Lack Thereof

9-13

4° Platform Incompatibilities

16-17

5° Growth

18-20

Conclusion

20-21

Copyright 2015 Dataiku www.dataiku.com

3° Skill-set Disconnect

14-15

Or why it's easy to work with old data, but inoperative.

Or why attempting to collaborate without a common ground is nearly impossible.

Or how the skills of analysts, developers, and statisticians are mutating. Quickly.

Or how early miscommunications lead to long-term data incompatibilities.

Or what to do when that unavoidable build or buy decision point comes around.

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