Strategic advice to leverage new technologies

In this article, we propose a maturity framework that addresses the data management challenges for organizations seeking to develop and deploy IoT platforms. We will highlight this framework through a data management lens, exposing the implications and requirements for data collection and storage, data integration, and data analysis. Finally, we will suggest a set of prescriptions for organizations wishing to improve their level of IoT maturity in order to capitalize on this ­phenomenon.

All that has been said and written about the challenges associated with the Internet of Things (IoT) does not quite prepare you for the practical difficulties that crop up as you start implementing and deploying IoT solutions. Most of the publicly available knowledge about IoT challenges relates to high-level issues that are typically addressed through architecture and design decisions. One of our recent successful implementations, an enterprise-wide Remote Energy Management System (REMS), brought us face-to-face with an entirely new set of ground-level challenges, from data ingestion to data storage, to data processing, to data analytics and visualization. In this article, we share our experiences related to data management in the course of implementing the system, both issues that we headed off at the pass and those that we discovered and addressed along the way.

The IoT helps embed technologies into everyday products/devices, such as audio/video receivers, wristwatches, smoke detectors, and home appliances, which not only enables them to communicate online, but also to receive and process data and information from other devices in a dynamic fashion, in real time. Thus, the real revolution of IoT goes beyond embedding a sensor and sending signals over the Internet to developing a 360-degree context awareness by analyzing data from multiple sensors or sources using complex advanced algorithms, in real time, for improved decision making.

In this edition of Cutter IT Journal, we focus your attention on an important issue facing us in leveraging the potential of the IoT: data management and analytics. To do so, we bring to you five excellent discussions around the IoT. We plucked these articles from an ­overwhelming response to our call for papers, identifying those with a fine balance of the rigors of theory and research together with examples and case studies that discuss direct practical applications of IoT.

As we explore in this Executive Update, understanding the driving forces behind digital transformation, its effects, and the role that certain enterprise architecture (EA) best practices can play in embracing digital transformation will help organizations benefit in this challenging time.

Last week, Microsoft moved to beef up its own IoT offerings by buying IoT platform and services provider Solair. I like this deal because it gives Microsoft — which has been heavy on technology but short on actual IoT user stories — a company that can point to a good customer base with actual deployed IoT applications across various industries. Acquiring Solair, Microsoft also gets IoT hardware and IoT industry expertise, along with focused applications.

Somewhat like the data warehouse architecture before it, data lake thinking has focused mainly on the information/data contained therein — its types and structures, its modeling and usage, and so on. However, as we showed in Part II of this Advisor series, the IDEAL conceptual architecture emphasizes that information is only one of three spaces that require consideration. As we saw in that Advisor, process and people demand equal consideration. In Part IV, we discussed the aspects of process that deal with getting data into the lake and ensuring its internal consistency where required. This Advisor examines the other aspects of process: particularly choreography, as well as its supporting function of organization — the means of creating and managing all the processes of the data lake. We also briefly touch on utilization, which represents the applications that make use of the information to provide business value.

Enterprise architects are well aware that their subject is multidimensional and complex. So how on earth do architects manage versions across architectures, building blocks, components, and artifacts? There is surprisingly little written about this topic, despite its obvious importance. In this Executive Update, I hope to rectify this by outlining current best practices on the subject of architecture versioning.