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.

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.

[From the Editor: This week's Cutter IT Advisor is from Charalampos Patrikakis's introduction to the April 2015 issue of Cutter IT Journal, "Next-Generation Production Management" (Vol. 28, No. 4). Learn more about Cutter IT Journal.]

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).

Clients come in all sizes and descriptions. Some are a veritable fountain that continually spews ideas and changes. This may seem like an enviable situation, but don't overlook the need for convergence to a solution. Their behavior can cause the team to spend too much time on non-value-added work as they do their analysis of the scope implications and contribution to business value. A strategy to postpone some suggestions to the next version might work. Others don't seem to have any ideas to share.

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.

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.

This Advisor points out that decision making, which is a critical aspect of the architect's role, is really a management role in disguise, delegated down by senior management. It explains as well that architecture is really a management discipline, even if it doesn't feel quite like one.

Data is everywhere. IBM projects that, every day, we generate 2.5 quintillion bytes of data. In relative terms, this means 90% of the data in the world has been created in the last two years. These numbers are a strong indication of the ubiquity of big data and the corresponding need for analytical skills and resources, because as the data piles up, managing and analyzing these data resources in the most optimal way become critical success factors in creating competitive advantage and strategic leverage.

As mobile apps become increasingly sophisticated and important, they are changing the ways in which people interact with the IT environment. Mobile apps are accessed differently than is desktop software in that mobile apps are always available and become an immediate part of personal interactions. As they are used for social purposes -- for scheduling and meetings, to access travel details, and to perform innumerable mundane functions that coincide with the needs of ordinary life -- mobile apps leave a rich trail of data that can be exploited, for better or for worse, by companies wishing to enhance their marketing efforts. But these data streams also provide extraordinarily rich pickings for nefarious purposes. Added to this risk is the growing use of business apps that connect to corporate systems and data, creating an increasing security threat across a wide range of vectors.

While it might sound like a little bit of voodoo, it's not. Prosperity thinking ties back to organizational and group theory from Tuckman to Ouchi. If we adopt a consistent attitude (Ouchi) and it's one where we see the good in the team, the organization, and the world around us (Tuckman), we will have more positive outcomes

In the previous Advisors in this series, we pointed out that development can be sorted out into three classes (see Figure 1):

Figure 1 -- The three types of development efforts.

Connected devices are set to dramatically change healthcare in general and clinical and pharmaceutical studies and the treatment of chronic diseases, in particular. The key to utilizing sensor data generated by personal health, fitness, and medical devices is to correlate it with other more traditional healthcare and medical data -- such as doctor-created medical records, clinical research, and individual genomes -- data sets that are typically unstructured, fragmented, and not easily integrated or analyzed.

Although a lot of social media content is textual, consumers are also using rich media types, including sound, video, and photos in their postings. So how does one extrapolate consumer sentiment from such sources? You can use basic keyword search or simple text analysis to analyze the metadata associated with such files, but more often than not, such data offers just a bare-bones, summary description of the file. Typically, it does not capture the intention of the author or what is going on in the photo.

The Apple Watch is significant because it legitimizes the market for wearables (in general) and smart watches (in particular) by dramatically increasing the visibility of smart watches. (Smart watches are wearables designed to run 3rd-party apps; general wearables are fitness bands, activity trackers, and the like, that do not run third-party apps.) It is also leading to a wave of innovative new products -- including apps designed to work with the Apple Watch, as well as new smart watch offerings from other vendors that have been inspired by Apple's innovation. And, perhaps most importantly, the proliferation of smart watch users will offer companies a new and very dynamic touchpoint for engaging with customers.

The Apple Watch is the biggest announcement in wearable tech this year. And, in just a few days (24 April), it will start to appear on the wrists of large numbers of consumers.

Most people know that innovation requires time to think, reflect, experiment, fail, revise, and explore. But many have likely not contemplated how directly cost pressures can impact innovation efforts. Psychologist Donald T. Campbell developed a model of innovation in 1960 that can help us understand just what's at stake. The Campbell model was inspired by Darwinian evolution. It portrays innovation as a two-step process, as follows:

The Agile principle of delivering working software frequently, in the shortest feasible timeframe, can scare DW/BI teams into avoiding Agile practices if they can't conceive of how they would deliver working software in a matter of weeks. However, teams that think creatively about how to work toward this principle have found effective approaches. In this article, I provide a case study that shows how one team approached this challenge: by identifying the business value in the steps of the DW/BI delivery process.

Externalizing business logic has long been a goal for EA. Business rules and complex event processing (CEP) are two ways to achieve this. So what are the differences between business rules and CEP architectures? And how can we use EA to prepare for using business rules or CEP?

One day Alice came to a fork in the road and saw a Cheshire cat on a tree. "Which road do I take?" she asked the cat. "Where do you want to go?" asked the cat. "I don't know," Alice answered. "Then," said the cat, "it doesn't matter." -- Lewis Carroll