From IBM and Google, to McKinsey and Booz, and from corporate and sports circles, to Human Resources and William Shakespeare, Big Data and Analytics seem quite the rage. The more expansive media and technology have become, the greater our platform for connecting and the more intricate our conduits for communication. In brief, not only is there a super-wide array of means for collecting data, but also we ourselves have multiple, vast forums in which to generate data: posts, comments, discussions, images, videos, music, GIFs, PDFs and slides.
You get the picture.
I have a keen and vested interest in Big Data and Analytics, because these figure significantly in my conceptual framework:
Theory of Algorithms is a framework for solving problems and completing tasks, and it aims to do so in a better way than we’ve done before. It is also a way of knowing things in our world more sharply and deeply. So in this regard, it aims to be a complete epistemology, too.Big Data and Analytics are about (a) understanding our world more deeply, (b) gaining the insights which I call extracting the algorithms, and (c) taking action in order to be more effective at meeting our objectives and serving our purpose. But before it can truly work for us, and fulfill its promise of insights and results, we must step back and put Big Data and Analytics under the spotlight.
This is my introduction to a series of articles on this hot topic. My friend, Patrick Carmichael, and I have had numerous conversations and e-mails, and he has a good sense for, and appreciation of, Theory of Algorithms and in particular Big Data and Analytics. So I plan to interview him, among others, and invite him to weigh in on what is truly expansive and exciting as well as intricate and convoluted.
I have also framed a working point of view on Big Data and Analytics, and below are key arguments. Along with others, I hope to better advance our thinking, insights and results.
- Big Data is not big enough at all, nor is it close to a complete gathering of information or rendering of insight.
- Analytics must heed the principles and cautions of statistics, so engaging in analytics requires a grasp of statistics.
- Big Data and Analytics rely on the scientific method, but interestingly enough it also challenges the scientific method in ways that are, as I will argue, antithetical to traditional science.
- Decision-making on small and simple, to large and complex matters is a lightning rod for the best and the worst of human thinking.
- If our decision-making lacks rigor or accuracy, then Big Data and Analytics are likely to make radical improvements for us.
- If our decision-making is fundamentally sound for our purpose, then these incremental improvements are likely to be smaller.
- Some may advance the notion that Big Data and Analytics are the saving grace of all saving grace. It is simply not. It is a good tool, to be sure, but the experienced carpenter has a collection of many tools in order to build a house.
- Just as technologists have struggled for decades to mimic the sophistication of the human mind, that is, via artificial intelligence, so-called data scientists will struggle to grasp the full human complexities of business, markets and organization.
- The applicability of Big Data and Analytics is far and wide, indeed. But for it to fulfill its promise, users must know when, and how, and where to apply it.
- Tao of Statistics
- Tao of Science
- Science vis-à-vis Technology
- The Tripartite Model
- Implications for Top Leaders
- Imperatives for Organizations
- Business Analytics
- People Analytics
- Sports Analytics
Thank you for reading, and let me know what you think!
Ron Villejo, PhD