In this webinar, Cutter Senior Consultant Bhuvan Unhelkar goes beyond descriptive and predictive analytics, beyond Hadoop/HDFS distributed data architectures, to help you focus on a strategic framework for adopting big data.

It is difficult to think “outside the box” when you don’t realize that you are inside a box. This is what happens when enterprises get seduced into romanticizing abstract ideas such as “architecture” or “agility.” It is important to steel oneself to the allure of these siren calls and stay focused on what is of value to the business.

Discover how you can combine big data analytics with a Lean approach to disciplined problem solving, enabled by a Lean management system, to optimize decision making, investment, and performance.

In response to the growing number of data breach incidents, the data-centric security vendors have introduced new, advanced database threat-protection solutions employing machine leaning (ML) and behavior analysis techniques designed to monitor and protect databases in real time. Although these advanced solutions are a fairly new development, we wanted to gauge the extent to which organizations are adopting them.

Here in Part III of this three-part Executive Update series, we investigate current techniques and best practices for managing IT project portfolios and strive to create a solid bridge between corporate strategy and IT investments. ITPM is one of the critical tools available to an EA practice to better ensure IT alignment with business strategy.

This Executive Update explores the mechanics behind various decision-making models and examines the boundaries and use cases for each. It discusses the qualitative value that experience or intuition can add to data-driven quantitative analysis, thereby providing the best approach to decision making. It is an attempt to ­understand the science of the art of decision making.

In this Executive Update, we explore methods for creating actionable intelligence from time series–based sensor data in order to solve specific business problems.

Speed is the new currency of business; customers are expecting organizations to deliver changes and new products at a faster pace. If you’re not going fast enough, you can guarantee that you will be choking on the fumes of organizations that are. New startups are forcing companies to rethink the way they deliver change, but the delivery of the best designs remains an issue. Whilst you may now have your design plainly articulated around the needs of people and outcomes, there is still the matter of resolving the delivery challenge.

When we talk about wearables, most of us have one or two specific devices in mind that we use to add tangibility to our thinking. Yet the examples that occur to us most readily are typically those that require the least effort, rather than those that lend themselves to thorough consideration.

Aspects of planning, implementing, and operating a big data platform affect the traditional enterprise viewpoints of business, technology, information, and infrastructure. Managing the change within one of these viewpoints may be difficult, but due to the technical and business reach of big data technology, ensuring that these changes are cohesive across viewpoints and can be successfully delivered within the organization is even more challenging.

Cloud-native design holds a lot of promise. Despite the effort needed to set up the tools, methods, and organizational practice around this approach, more and more organizations are following the successful implementations proven by Netflix and Amazon. But this is not a purely technology-driven trend. Adjusting all parts of your organization to work in a new way will enable your business to gain agility, which is crucial in a digital world. In this Advisor, I discuss the four most important benefits that come from cloud-native design.

Cognitive computing promises to transform 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 (NLP), machine learning (ML), and intelligent reasoning capabilities.

[From the Editor: In 2008, Cutter Fellow Rob Austin described the principles that need to be learned — and unlearned — in an innovation economy. Although nearly a decade has passed since he first wrote the words, his guidance still rings true today. We share his message again here.]

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.

Industry/domain-specific IoT solutions are typically built on the provider’s IoT platform, implementing frameworks and expertise designed to support popular IoT and Industrial Internet use cases. This Advisor provides some examples of what is available on the market.

Organizational transformations come in many forms, including divestures, joint ventures, taking a business public or private, and general market reorientations. In this Advisor, we explain enterprise architecture (EA) in relation to one type of strategic transformation: acquisitions. Acquisitions are one way that businesses seeking to digitize use to accelerate the journey to their destination and, in our experience, to specifically complement their innovation-management pipeline.

In this hour-long webinar with Edgar Barroso, you’ll discover how, regardless of the size of your company, you can take advantage of thinking about the future and act on transdisciplinary insights.

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.

The Great Chicago Fire of 1871 served as a wake-up call not only to Chicago, but to other cities throughout the United States that they needed to do much more to manage their fire risks. This Advisor asserts that as we move into an era of truly hyperconnected, ubiquitous, cyberphysical systems, there could be huge consequences if these systems fail either accidentally or through deliberate actions, and that now, more than ever, there is a need for robust, reliable, and secure approaches across the lifecycle.

Agile can mean different things to different people depending on the context in which it is being discussed. This Advisor shows four different aspects in which we can understand and use Agile in practice: "learning," "doing," "embedding," and "being." Each of these four words can also represent four phases for adoption and transformation of an organization to Agile. 

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.

As a CIO, you need to not only understand and embody the many aspects of leadership needed by your organization, but to mentor, coach, and propagate these leadership behaviors all the way down the line to the individual performers within your team. As the examples in this Advisor demonstrate, leadership is required at all levels within the organization, but at different levels, different aspects of leadership become more prominent.