Strategic advice to leverage new technologies

Businesses are implementing analytics and trying to use data to uncover new insights about their operations, customers, suppliers, employees, and so on. Even though the idea of using analytics is exciting, these types of projects are not for the faint-hearted — at least if you’re trying to implement analytics across the entire enterprise.

The need for business architecture in organizations has never been greater than it is today, as we must continually sense and respond to opportunity and change, both of which abound. Though the business architecture discipline continues to gain traction at an ever-increasing pace, how we practice it is critical for its adoption and effectiveness. This Executive Update provides an overview of the importance of using visual techniques as part of a business architecture practice and highlights three aspects: visual design, graphic recording and facilitation, and storytelling.

For decades, the commercial relationships between companies that provided software development services and their clients have been shaped by either fixed-price/fixed-scope or time-and-materials types of contracts. The drawbacks of both approaches have long been evident, but, nevertheless, both sides have learned to use them to protect their own interests. As we explore in this Advisor, an Agile ecosystem requires the creation of a systemic setup that works with the market, not just selected vendors.

Large, non-software companies introducing Agile to their organizations tend to suffer from a cognitive dissonance of sorts: we would like to have the same look and feel across the entire company, delivering stellar-quality products, yet we want to enable high-performing, self-organizing, self-managed, and self-empowered teams to deliver (or demo) at the end of each sprint. This Executive Update summarizes five key scenarios in which this cognitive dissonance becomes especially evident for large companies, particularly with non-software teams.

Identifying and developing new drugs and conducting clinical trials involve complex and lengthy (i.e., costly) processes that require researchers and drug manufacturers to integrate, manage, and analyze incredible amounts of data while at the same time collaborate with other medical research and pharma companies in their efforts. Pharmaceutical and biotechnology companies are using artificial intelligence (AI) to optimize the discovery and evaluation of new drug compounds, to explore patient and efficacy data, and to develop and bring new therapies to market.

In statistical project management (SPM), we simplify the project management approach by eliminating many concepts that the dominant project management methodologies consider central. While I caution you to err to the side of adopting a lighter methodology rather than a thicker one, that choice is a local one and yours to make. The SPM ontology provides you with options. Here in Part IV, we examine how projects grow.

Information gathering and recording are plagued by fragmentation, context switching, and volatility. These problems seem to be inherent to working with data and constitute the data beast. The contradiction between, on the one hand, the search for consensus and, on the other, the fragmentation, context switching, and volatility of information that dilute this effort is a never-ending rodeo ride. The reasons behind fragmentation, context switching, and volatility are misunderstood. This triple plague is often seen as either an imperfection that requires fixing or a roadblock to digitization.

Decision automation means that software — not people — makes decisions. The concept of decision auto­mation is both deceptively simple and intriguingly complex. On the surface, the idea is to write a computer program that uses data, rules, and criteria to make decisions. Decision automation is programmed decision making. A decision automation system replaces and eliminates the need for a human decision maker in a specific decision situation. Through such a system, inputs and events trigger business rules and programmed instructions and then the program “makes” a choice and initiates action. The greatly expanded and evolving computing infrastructure makes it increasingly cost-effective to apply decision automation in situations that previously had been prohibitively costly. Increasingly, decision automation deploys as a distributed, cloud-based application that uses integrated networks and sensors to make decisions in a specific domain.