Change is risky. The decision to change or not change needs to be based on a risk-versus-reward analysis supported by executive sponsors. Some change will occur based on necessity. Many IT organizations are adopting DevOps practices bottom-up because the burden of delivering and the overhead for maintaining current systems is affecting their physiological and psychological well-being. However, businesses should view this as a leading indicator of the need for further change.

The long-time hiring vs. training debate has been energized by an influx of new technologies requiring new skills. Today's IT departments constantly struggle to meet the skill and experience requirements of new projects, new development platforms, new languages, new environments, and new infrastructure. The advantages of hiring are that it makes it possible to immediately bring in new skills and new perspectives -- often from people who have some familiarity with the industry and may have worked with competitors. This is offset by a range of serious disadvantages.

In this Update, we explore how to collect and communicate contextual information, increasing the odds that customers will see value in software and willingly adopt it. The same principle applies to contextual information that applies to any kind of requirements insights: the type of information needed, and the medium for communicating it, varies according to the question asked. Therefore, software professionals need to develop a toolkit of techniques that will vary across teams, projects/products, organizations, and time, since the same questions do not arise for everyone.

Shadow IT is symptomatic of how enterprises really operate. There are processes, systems, and structures that are visible on the surface, and these work well for the most part. However, there are activities, investments, people, and decisions that are outside of the well-delineated boundaries of an organizational function.

Psychology has provided interesting insights into human behavior and how to mitigate the challenges of running an enterprise. This Executive Report discusses how CIOs can successfully leverage these psychological concepts to foster a healthy business-IT relationship.

The relationship between business and IT is similar to a married couple setting up and running a home. The "home" in this case is an "enterprise."

There is plenty of evidence that organizations can achieve cost savings through outsourcing. But there are limits to providers working smarter to achieve the holy trinity of (for the client) dramatic savings and higher KPIs and (for the provider) a decent profit margin.

We can think of an "enterprise" as any human undertaking, endeavor, or project. Any enterprise comprises three areas: (1) an organization, (2) the businesses operated by the organization, and (3) any technologies or equipment needed to support the organization’s businesses. Enterprise architecture (EA) needs to support all three. It is striking that as EA has evolved over the last 40 years, there has been a parallel development: that of organization design (OD). In this Executive Report, we look at what EA can learn from OD models.

We can think of an "enterprise" as any human undertaking, endeavor, or project. Any enterprise comprises three areas: (1) an organization, (2) the businesses operated by the organization, and (3) any technologies or equipment needed to support the organization’s businesses. Enterprise architecture (EA) needs to support all three. It is striking that as EA has evolved over the last 40 years, there has been a parallel development: that of organization design (OD). In this Executive Report, we look at what EA can learn from OD models.

There is a simple principle that would keep us from making grievous errors due to incomplete (or worse, inaccurate) data. Make "data-informed decisions" instead of the more common catchphrase of "data-driven decisions." The difference is an extremely important nuance.

There is a simple principle that would keep us from making grievous errors due to incomplete (or worse, inaccurate) data. Make "data-informed decisions" instead of the more common catchphrase of "data-driven decisions." The difference is an extremely important nuance.

In some companies, leaders may have a hard time participating in conversations because of how they feel they are influencing the conversation. When they speak, people will tend to go along with them, so they find themselves speaking last to allow everyone else to speak; ironically, this also gives them the last word.

The barriers between business and IT are long-standing. Historically, the business would ask IT to deliver on their requests. IT felt the requests were misguided or wrong. So IT built what they wanted to build. Of course, the response from the business was, "That's not what we wanted! We can't sell that!" However, it doesn't stop there. IT began gold-plating some features, adding features they thought customers would want (or things that IT really wanted to build) and not building other things.

The challenge in measuring EA value is not a lack of metrics; it is knowing which ones make sense for an organization and provide the most "value" for the effort. The key to a successful value measurement program is to identify metrics that correlate to business key performance indicators (KPIs).

Social business analytics enables organizations to apply real-time sentiment and insights derived from social media and enterprise sources to identify and target key influencers (on social media sites), generate new leads and opportunities, and improve customer loyalty.

Social business analytics enables organizations to apply real-time sentiment and insights derived from social media and enterprise sources to identify and target key influencers (on social media sites), generate new leads and opportunities, and improve customer loyalty.

Organizations can build resiliency in their employees by helping them successfully adapt to change. Resilient organizations are not satisfied with the status quo and continually seek opportunities for constructive change.

In this issue, we investigate how production management can benefit from embracing recent developments in ICT by providing insights into the practical use of these technologies from industry experts and researchers.

In this article, we focus on the difficulties posed by small series production in ramp-up and a novel production control solution that is helping to alleviate them. The risks of such production are not comparable to big series automotive ramp-up scenarios. Production and control strategies thus need to be adapted accordingly.

The idea of a Living Lab has emerged over the last few years as a means of deploying an ecosystem that will not only allow the evaluation and fulfillment of requirements in the context of a research project, but will also enable users, experimenters, and practitioners -- from several research domains -- to test and exploit its functionality. Contrary to test beds, which focus on the requirements of the projects that initiate them, a Living Lab supports a greater scope and allows itself to evolve into more mature stages, thus ensuring its applicability for many years.

n this article, we present an approach that brings together a new generation of multi-agent systems (MASs) that can build plans and schedules in real time based on the information collected from different resources, including legacy systems (MES, ERP, etc.); data generated by workers, sensors, and the MASs themselves; and enterprise integration platforms that follow different paradigms in order to guarantee fast delivery of business value, maximal coverage of systems accepted by the platform, and security.

In this article, we briefly describe our view on an ontology-based production management system, focusing particularly on the strategic planning tool. We describe its architecture and present a use case that demonstrates the tool's operability. The difference between our proposed approach and the already existing solutions centers mainly on the use of MASs to provide what-if game simulation to explore different degrees of freedom (DoFs).

There is an organizational pattern I find that is frequently the root cause for problems in many organizations. I call this pattern the "project congestion," and it is regularly found in product organizations that have several dozen to several thousand members. A standard way to keep the finances of these organizations in control is to set up projects of one or more years that are supplied with a certain budget and work toward some business goal.