Strategic advice to leverage new technologies

The commercialization of cognitive systems is impacting the consumer and enterprise worlds by changing the way people interact with computers along with the methods for data analysis. Cognitive computing holds the promise of transforming information-intensive industries with its ability to ingest, analyze, and summarize massive data sets and facilitate self-service analytics, intelligent decision support, and smart advisory systems via the application of natural language processing, machine learning, and intelligent reasoning capabilities. This, the first of a two-part Executive Report series, begins to examine the rise of cognitive computing — including the technologies enabling cognitive platforms, cognitive applications, available commercial cognitive products, and development trends.

I no longer think of technical debt as a problem. It is a symptom — a symptom of deeper system problems in our organizations. Trying to fix technical debt by simply fixing the code is like bailing a boat that is taking on water. It is likely necessary, but it won’t stop the water coming in. We need to find and fix the root causes of the technical debt.

Most enterprise architects have heard of the Zachman Framework. Indeed, many know that John Zachman first developed his eponymous framework in the 1980s and are familiar with its iconic graphic. Still, it truly astonishes me that some architects know very little about Zachman, or his framework. Upon doing my research for this Executive Update, I was amazed to learn that there really isn’t a good summary of the contributions that Zachman’s work has made to enterprise architecture (EA).

So … here is my attempt to record the importance of the Zachman Framework.

The biggest problem facing organizations in their data protection efforts is finding and classifying sensitive data because they are unsure of where it actually resides. This can be attributed to various reasons.

Capabilities are the main link between business architecture and IT architecture in general, and digital transformation in particular. Not all capabilities can be automated, but as a rule, the greater the level of capability automation, the greater the degree of efficiency, effectiveness, and digitization.

Over the past three to four years, Health-USA has been trying to expand its operations with a new set of services and solutions for patient-centric care delivery, care coordination, and clinical decision support. Recently, the company decided to implement Internet of Things (IoT) devices and sensors as part of its strategic IT plan. In this Advisor, I present a case study of Health-USA’s experience leveraging the combination of enterprise architecture (EA) and IoT in its digital transformation effort and share a set of effective practices gleaned from that effort.

Business design looks at the enterprise from an outside-in perspective. Business design starts with examining the value customers receive through their interactions with the enterprise and how the products and services meet tangible and emotional needs. A key benefit that business design brings to an organi­zation over traditional EA is the focus that it places on value, and more importantly, on the value proposition that the products and services of the enterprise have for customers and clients.

Cognitive systems are well suited for use in customer engagement scenarios in general because of their ability to process questions similar to the way people think and to generate tailored recommendations pertinent to customer wants and needs. The Q&A format in which they interface with customers — allowing users to ask questions using human natural language — is particularly useful for establishing a more engaging and personalized relationship between a company and its customers. These capabilities can go a long way toward making customers feel like they are being treated as individuals when dealing with your company.