Strategic advice to leverage new technologies

Data science is essential in extracting value from big data but it is not sufficient. It makes use of technical, management, and business analysis skills. Data science also deals with the enterprise architecture (EA) of the organization. Astute leadership is essential and an imperative for value extraction from big data.

Giti Javidi, Ehsan Sheybani, and Lila Rajabion present a convincing argument for redistributing the cloud. They suggest that the cost and time associated with transmitting data to the cloud, processing it there, and returning the results back to the IoT devices are critical. Fog computing both enhances and complements the cloud by bringing the processing closer to a cluster of IoT devices, resulting in faster analytics.

Matthew Ganis and Frank Coloccia eloquently explain that analytics is not a perfect science. Indeed, the authors judiciously argue that analytics is more an art than a science. They demonstrate their analytical approach with a direct and practical example of using Twitter data in real time to understand the sentiments (positive or negative) of trade show participants and to gain insights from them.

Clustering large amounts of unstructured data into relatively smaller chunks based on some similarity is at the crux of unstructured data analytics. Here’s where “swarm intelligence” can play a role — an application that drives neural networks for clustering unstructured data. The authors offer an excellent example of swarm intelligence via a model that learns to classify Web documents. We can easily apply this same algorithm to business, medicine, defense, and supply chains, to name a few other areas.

The authors focus on the challenges of security in the Agile deployment of big data applications in the cloud. Security issues can make or break the deployment of otherwise complete solutions in the cloud. The authors begin by introduc­ing the topic of “micro­segmentation” — which allows “public cloud-based infrastructure as a service (IaaS) providers to offer software-centric or software-only solutions.” The value of this discussion to business lies in the opportunity to quickly deploy secure models for end-user consumption. 

This article touches upon a crucial challenge in the big data space: identifying the right questions for it. As data continues to explode, not only are businesses struggling to find answers to business questions, they often cannot even determine what questions to ask of their data. The authors discuss a practical experiment on classifying genetic data using a colored de Bruijn graph and show the application of this technique in the business world.

It is now possible to search on the Web for “IoT use cases in [industrial sector __ ]” and find links to hundreds of case studies and white papers. Of course, these should sometimes be taken with a grain of salt. In general, however, published case studies (especially when an end-user company is named) present some good evidence of an area in which the IIoT shows solid potential.

Andrew Guitarte describes the paradigm of big data analysis and how it “shifts from manual to auto­mated, dependent to autonomous, isolated to context-aware, product-driven to needs-based, batch to real-time, and static to streaming.” This paradigm shift is important especially in the context of automated wealth advisory services where the organization of otherwise unstructured data is vital. His article takes us into the realms of business capability architecture (BCA) and presents a cognitive and heuristics-based emergent financial management (aka CHEF) tool that can be used effectively in emergent decision-making processes of bank employees, shareholders, and their customers.