“Agility” is the facility of quick response — the ability to be nimble. In general, to be agile entails the ability to detect changes in your environment as well as the ability to respond quickly and appropriately. Being “agile” (in the traditional sense) is about excelling in a constantly changing environment, much like a serious athlete who masterfully integrates the aspects of balance, speed, strength, coordination, and reaction to the dynamics on the field. Management has two roles in bringing agile behavior to the organization:

People in every organization that has Agile teams reach the point where they ask themselves: how far do we go with Agile? As we explore in this Executive Update, the answer depends on the strength of the Agile principles — not just in the teams — but in the larger organization.

In this insightful webinar, Cutter Senior Consultant Barry Devlin will describe a modern, comprehensive information architecture that combines the best of current data warehousing approaches and facilitates integration of cutting-edge systems.

It truly takes a village to build a nimble (Lean) and flexible (Agile) enterprise architecture (EA) program in this digital era. EA as a discipline may not have to change drastically to address digital transformation, but EA does have to play a much bigger role in embracing the change swiftly and facilitating the change across the enterprise.

Those of us born before 1990 know how much the world has changed in only a few years. Among a myriad of changes, we have seen obsolete technologies mercilessly replaced and witnessed a couple of global economic collapses, and several products and services that we could never have imagined before — like the Internet — have become part of our daily lives. Whenever I see new, life-changing technology, I can’t help thinking: how did we get here so fast?

Cutter’s Senior Consultants are experts in training management teams how to both meet the needs of the Agile development organization and pursue the strategic goals of the business. At the end of this two-day Agile Management session, your executives and managers will know how to balance empowerment with governance, align goals and metrics up and down the organization, and determine the exact amount of agility (or stability!) your organization needs to support its business objectives.

In practice, content and information management systems today haven’t fulfilled their promise. They don’t understand unstructured data, and they can’t directly act upon it. They work well only when people follow defined information governance processes.

One of the hardest things to teach or learn is self-awareness. By self-awareness, I mean the capacity to evaluate oneself accurately from the perspective of others and the ability to detect differences between our behavior and our values. So many leaders are unaware of what others around them truly think about them and at some point in these leaders’ careers, this inevitably leads to trouble. Usually these troubled leaders are constantly interpreting the world around them into their internal logic, thus preventing them from seeing the gap between their own internal understanding of themselves and what others think.

This article addresses the challenge of slicing data warehousing and business intelligence (DW/BI) user stories into small, business-valued deliverables to align with the Agile principle of "Deliver[ing] working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale."

The idea of a metaframework isn’t really anything new. It is simply a way of describing something that enterprise architects need to do.

Hex elementalization aims to create a platform — an architecture, an environment — encompassing end-to-end integration. The traditional way of thinking about data in 1s and 0s (bits) is unsuitable if we want to create a common “playground” for IoT. No matter how large or diverse the data is, it needs to be broken down into smaller chunks that will enable and ease interaction. These smaller chunks (i.e., hex elements) can be combined and related into meaningful information. Why “hex” elements? This article explains.

Traditionally, EA has focused on delivering a set of guiding principles, frameworks, reference models, blueprints, and roadmaps to support operational ­excellence as well as strategic business and IT alignment goals. Today this focus is shifting toward lever­aging collaborative, agile, disruptive, and innovative approaches to executing EA practices for digital transformation. Like many thought leaders, I believe that in order to successfully implement a new change, a new EA must be pro­active and customer-oriented. This new EA must be innovative enough to deliver tangible business results more consistently and more frequently to capitalize on the IoT opportunity. EA and IoT together help enterprises to leverage their capabilities (people, process, and technology) while establishing mechanisms or conduits for the digital transformation.

In this article, we propose a maturity framework that addresses the data management challenges for organizations seeking to develop and deploy IoT platforms. We will highlight this framework through a data management lens, exposing the implications and requirements for data collection and storage, data integration, and data analysis. Finally, we will suggest a set of prescriptions for organizations wishing to improve their level of IoT maturity in order to capitalize on this ­phenomenon.

All that has been said and written about the challenges associated with the Internet of Things (IoT) does not quite prepare you for the practical difficulties that crop up as you start implementing and deploying IoT solutions. Most of the publicly available knowledge about IoT challenges relates to high-level issues that are typically addressed through architecture and design decisions. One of our recent successful implementations, an enterprise-wide Remote Energy Management System (REMS), brought us face-to-face with an entirely new set of ground-level challenges, from data ingestion to data storage, to data processing, to data analytics and visualization. In this article, we share our experiences related to data management in the course of implementing the system, both issues that we headed off at the pass and those that we discovered and addressed along the way.

The IoT helps embed technologies into everyday products/devices, such as audio/video receivers, wristwatches, smoke detectors, and home appliances, which not only enables them to communicate online, but also to receive and process data and information from other devices in a dynamic fashion, in real time. Thus, the real revolution of IoT goes beyond embedding a sensor and sending signals over the Internet to developing a 360-degree context awareness by analyzing data from multiple sensors or sources using complex advanced algorithms, in real time, for improved decision making.

In this edition of Cutter IT Journal, we focus your attention on an important issue facing us in leveraging the potential of the IoT: data management and analytics. To do so, we bring to you five excellent discussions around the IoT. We plucked these articles from an ­overwhelming response to our call for papers, identifying those with a fine balance of the rigors of theory and research together with examples and case studies that discuss direct practical applications of IoT.

As we explore in this Executive Update, understanding the driving forces behind digital transformation, its effects, and the role that certain enterprise architecture (EA) best practices can play in embracing digital transformation will help organizations benefit in this challenging time.

Last week, Microsoft moved to beef up its own IoT offerings by buying IoT platform and services provider Solair. I like this deal because it gives Microsoft — which has been heavy on technology but short on actual IoT user stories — a company that can point to a good customer base with actual deployed IoT applications across various industries. Acquiring Solair, Microsoft also gets IoT hardware and IoT industry expertise, along with focused applications.

While the rise of financial technology companies and services has been gradual and steady over the years, beginning perhaps with the founding of PayPal in 1998, there’s been a particularly explosive boom in this sector just in the past couple years, with a new FinTech startup sprouting up nearly every week and disrupting the financial game in a major way. Indeed, companies like Stripe, Intuit’s Mint, Payoneer, and even such developments as Apple Pay are offering an array of fundamental changes in the many ways consumers and small businesses interact with their finances.

A frequent complaint we hear from Agile teams is that their self-organization is not respected and their manager routinely overrules their decisions. If you talk to the manager, he or she complains that the team doesn’t respect company policies anymore and makes decisions they’re not entitled to make. What seems to be a battle about power in many cases and like a confusion of self-organization with autonomy turns out to be an unfinished Agile integration into the organization.

In a recent Advisor, we called out an important function of architects: “to grapple with the elements of the enterprise that are disproportionate in their influence, and make them deliver [business value].” What is implicit in that statement is a recognition that “grappling” occurs because complex situations demand that we make the right tradeoffs and choices. It’s all about decisions. Who makes these decisions?

Somewhat like the data warehouse architecture before it, data lake thinking has focused mainly on the information/data contained therein — its types and structures, its modeling and usage, and so on. However, as we showed in Part II of this Advisor series, the IDEAL conceptual architecture emphasizes that information is only one of three spaces that require consideration. As we saw in that Advisor, process and people demand equal consideration. In Part IV, we discussed the aspects of process that deal with getting data into the lake and ensuring its internal consistency where required. This Advisor examines the other aspects of process: particularly choreography, as well as its supporting function of organization — the means of creating and managing all the processes of the data lake. We also briefly touch on utilization, which represents the applications that make use of the information to provide business value.

Enterprise architects are well aware that their subject is multidimensional and complex. So how on earth do architects manage versions across architectures, building blocks, components, and artifacts? There is surprisingly little written about this topic, despite its obvious importance. In this Executive Update, I hope to rectify this by outlining current best practices on the subject of architecture versioning.

Many large enterprises struggle with annual project budgeting cycles; big up-front planning; arduous governance, portfolio, program, and project management and control mechanisms; and other systemic efforts that, while intending to establish more predictability/control and less risk, often do just the opposite. This can cause Agile initiatives to falter or to collapse under the weight of an unrelenting command-and-control mindset.

The truly game-changing opportunities or challenges we face in our businesses are a blend of the complicated and the complex. Being able to understand the difference between the two domains and manage accordingly is thus the key to success. An inability to differentiate between complicated and complex leads to one of the most fundamental causes of business and technology failure — the illusion of control.