The TDWI Big Data Maturity Model and Assessment is set to launch November 20th.  Krish Krishnan and I have been working on this for a while, and we’re very excited about it.  There are two parts to the Big Data Maturity Model and Assessment tool. The first is the actual TDWI Big Data Maturity Model Guide. This is a guide that walks you through the actual stages of maturity for big data initiatives and provides examples and characteristics of companies at different stages of maturity. In each of these stages, we look across various dimensions that are necessary for maturity. These include organizational issues, infrastructure, data management, analytics, and governance.

The second piece is the assessment tool. The tool allows respondents to answer a series of about 75 questions in the organization, infrastructure, data management, analytics, and governance dimensions. Once complete, the respondent receives a score in each dimension as well as some expectations and best practices for moving forward. A unique feature of the assessment is that respondents can actually look to see how their scores compare against their peers, by both industry and company size.

We urge you to take the assessment and see where you land relative to your peers regarding your big data efforts.  Additionally, it’s important to note that we view this assessment as evolutionary.  We know that many companies are in the early stages of their big data journey. Therefore, this assessment is meant to be evolutionary. You can come back and take it more than once. In addition, we will be adding best practices as we learn more about what companies are doing to succeed in their big data efforts.

In the course of our research for the model, Krish and I spoke to numerous companies embarking on big data.  There were a number of patterns that emerged regarding how companies get started in their big data efforts.   Here are a few of them:

  1. Large volumes of structured data are already being analyzed in the company.  Some companies have amassed large volumes (i.e., terabytes) of structured data that they are storing in their data warehouse or in some sort of appliance, often on-premises.  They feel that their BI infrastructure is pretty solid.  Typically, the BI effort is departmental in scope.  Some of these companies are already performing more advanced kinds of analysis; such as predictive analytics on the data.  Often, they are doing this to understand their customers.  The vision for big data is about augmenting the data they have with other forms of data (often text or geospatial data) to gain more insight.
  2. A specific need for big data. Some companies start a big data effort, almost from scratch, because of a specific business need.  For instance, a wireless provider might be interested in monitoring the network and then predicting where failures will occur.   An insurance company might be interested in telemetric information in order to determine pricing for certain kinds of drivers.  A marketing department might be interested in analyzing  social media data to determine brand reputation or as part of a marketing campaign. Typically these efforts are departmental in scope and are not part of a wider enterprise big data ecosystem.
  3. Building the business on big data.  We spoke to many e-businesses that were building the business model on big data.  While these companies might be somewhat advanced in terms of infrastructure to support big data often they were still working on the analytics related to the service and typically did not have any form of governance in place.
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