Why Hadoop BI Projects Fail

Remy Rosenbaum lays out several reasons why he’s seen business intelligence projects on Hadoop fail:

In order to set up and run an effective Big Data Hadoop project that provides reliable BI, your organization will need to adopt a new mindset that addresses not only the technology, but also the organizational EIM. You will need to conduct a comprehensive analysis of your business with the help of analysts, internal domain experts, and strategists to come up with robust and relevant business use cases. You will also need buy-in from management, and take company politics into consideration.

Your Big Data project needs to work with your existing BI tools, along with your security and monitoring systems. Data security needs to be addressed because standard Hadoop implementations have relatively poor security, and many organizations are wary of keeping all their data in one location.

I do agree with these reasons, though I’m a bit surprised that I didn’t see much about “classic” BI problems like the inability of the company to standardize on terminology or definitions (e.g., what the Kimball method describes as conformed dimensions), the desire to tackle too much of the problem at once, rapidly-changing source systems (and how BI team members tend to be the last to know that something has changed), etc.

Related Posts

Tips For Using PolyBase With Cloudera QuickStart VM

I have a post on using Cloudera’s QuickStart VM with PolyBase: Here’s something which tripped me up a little bit while connecting to Cloudera using SQL Server. The data node name, instead of being quickstart.cloudera like the host name, is actually localhost. You can change this in /etc/cloudera-scm-agent/config.ini. Because PolyBase needs to have direct access to the data nodes, […]

Read More

Bayesian Modeling Of Hardware Failure Rates

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates: This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930