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Four Big Data Challenges

We are getting ready to launch the TDWI Big Data Maturity Model and assessment tool in the next few weeks. We’re very excited about it, as it has taken a number of months and a lot of work to develop. 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 50 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. 

At the same time we’ve been building the Big Data Maturity Model, I’ve also been working on the predictive analytics Best Practices Report. Since no survey about analytics would be complete without some questions about big data, I did include a section about it in the survey. Interestingly, over 70% of the respondents actively using predictive analytics or planning to use it (242 respondents) from the survey claim to have some sort of big data push going on in their organization.   

The group also reported a series a challenges (seen in the figure, below).

  • Data integration. The top challenge cited by this group of respondents was big data integration (35%). Interestingly, if you look only at those respondents who are current users of predictive analytics and users of big data, 44% report this as the top challenge. Clearly, integrating disparate kinds of data from different sources is difficult.  We saw this in our interviews for the big data maturity model. For instance, despite having some sort of data warehouse (especially in the case of the enterprise), a nascent organization in terms of big data maturity will often have also collected data as files with different formats, but without naming standards, and with storage structures that are minimally defined. This data is not integrated.
  • Getting started with the right project. This is a key challenge that companies face when getting started with big data. Thirty percent of the respondents in this group cited it as a top challenge. We note this in the maturity model as well as a pre-adoption issue. Typically, there might be a team charged with exploring big data that is trying to determine the top business problems to solve. Identifying the right business problem is critical for success and business needs to get involved as quickly as possible.
  • Architecting a big data system. Big data can mean disparate kinds of high-volume and high-frequency data. Architecting a system is important for success and it can be difficult. Twenty-nine percent of the respondents cited this as a challenge. The end goal for an architecture is frequently some sort of big data ecosystem that contains a unified information architecture which underpins analytics. Getting there can involve surgical precision in terms of rolling out infrastructure.
  • Lack of skills or staff. Twenty-eight percent of respondents cited this as a top challenge. This is a key challenge, whether it is the skills for Hadoop or other big data infrastructure or the analytics skills required to make sense of big data. Different organizations approach the skills issue in different ways. Some hire externally or look for university hires. Others try to re-train from within. Still others look to cross-pollinate skills from one part of the organization to a team that utilizes new technologies. Some build centers of excellence that help with the training from within. Others form SWOT-type teams to address big data analytics.

Look out for e-mails and other materials over the next few weeks letting you know that the Big Data Maturity Model and assessment tool are live!  


Posted by Fern Halper, Ph.D. on October 29, 2013


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