AIIM recently released a new White Paper entitled, “The Big Data Balancing Act: Too Much Yin and Not Enough Yang” by David Jones. Instead of explaining what Big Data is, it looks at blind spots that occur when a purely technical approach is taken to a Big Data initiative and how to balance that with strategic business thinking.
This paper is not trying to define big data. Instead it focuses on the four areas of tectonic abrasion – the areas where the various aspects of big data grind against each other and require elements of balance to be introduced. It describes a new mode of strategic business thinking for big data, and delivers a practical framework for deploying big data within your organization – providing the missing balance in the big data story to allow yin and yang to exist in harmony.
The four areas that are looked at are:
1)Technology vs. Business – A big data project can’t be all about technology. This will create bottle-necks in your process as your data scientist is asked to do too much and has to perform the functionality of data entrepreneur.
2)Data Scientist vs. Data Entrepreneur – A data scientist is skilled in statistical analysis, operating technology such as Hadoop and NoSQL and identifying value when they find it. The data entrepreneur works at a more abstract level and is has the same information as the data scientist, but is not an expert in IT or scientific areas. “To quote Doug Miles, Director of Market Intelligence at AIIM ‘The data scientist has his ear to the business and his eyes full on the data – the data entrepreneur has exactly the opposite focus, eyes full on the business, ear to the data.’”
3) Big data experimentation vs. Experiment-driven big data
Hypothesis creation is critical and moves organizations from big data driven experimentation to experiment driven big data. No longer is the focus on using technology to identify and solve a problem – the focus is on identifying the problem and using technology to help solve it. The ability to locate the key issues that can help specific business areas – and the associated hypotheses that can provide insight into those issues – can only be achieved by those working in the business.
4)Analysis vs. Action
Analysis of data may yield interesting results – but results on their own do nothing. Actions are required to take results and put them to work… The data entrepreneurs create hypotheses that isolate what we are looking for, where we should start looking, how we know when we have found what we are looking for, and what should be done as a result. The data scientist operates the latest tools and techniques to test the hypotheses and reports the results back. Once both have found what they are looking for, the relevant business decision can be applied and the path from the hypothesis executed on.
I would highly recommend you check out this white paper. One of the best tools included in it is the appendix on “A Big Data Framework” that gives areas that need to be addressed for a Big Data project to succeed.