Predefined source or reference frameworks such as TOGAF, Zachman Framework, IFW, or DoDAF are all very different. We need a simple way to first identify the factors that we need from each framework, and then combine them to create several relevant checklists or frameworks. This tool is the EA metaframework. This Executive Update provides an explanation of the different types of architecture frameworks, describes the role of the metaframework, and shows how architects can use it to create multiple integrated practical frameworks.

Organizations seeking to incorporate effective analytics programs will likely encounter several challenges along the way. Whereas many of these can be dealt with in the short term, others will require solutions that we do not know to exist at the present time.

Big data has already demonstrated many successes, and experts assert that cognitive computing systems can actually make the context behind decision making “computable,” acting as a proxy for human intuition. It is that convergence — human creativity supported by relevant information — that offers the greatest potential.

It is hardly necessary to explain or justify that security is a concern when we think of applying Internet of Things (IoT) technology to industrial applications, but it is useful to consider how it differs in this context from the consumer domain.

Successful digital transformation enables the organization to embrace innovation, to develop new products and models, and to rapidly realize those to create value. Although there are no blueprints for success, we will examine three best practices that can allow businesses to increase speed and reduce cost, reach customers with an excellent user experience, and experiment with new products, features, and models.

When designing products and services for the Internet of Things (IoT), organizations should make defining procedures for ensuring customer privacy an initial priority to avoid embarrassing and potentially costly surprises down the road.

This Executive Update explores the impact of the IoT on traditional business and technology architectures and the role of EA as an effective methodology for developing and implementing IoT strategies. We examine business architecture and how it integrates IoT-driven processes with traditional processes. 

Business process change and cognitive computing do not necessarily overlap, but in business environments, they often do. Put simply, we will increasingly use cognitive computing techniques to improve business processes.

The key to Agile management is that the organization and analytics that implement the interlocking sense-and-respond loops up and down the organization enable overall organization agility.

How do you compute the valuation of something that has no clear definition? There are a couple of ways to respond: accept that it is good enough to have a very general value proposition for activities that somewhat fit under an “architecture” umbrella, or delineate specific architecture strategies and initiatives whose value can be measured in a manner similar to other investments that enterprises are used to.

Architects are moving from single frameworks to instead work with a set of frameworks -- each finely tuned to serve a particular purpose. This new report by Roger Evernden explores a simple and pragmatic approach to developing a set of architectural frameworks that can evolve and be used as a constant guide to direct and manage EA. (This report is complimentary for Cutter Members. To purchase, please click the button below.)

Successful IoT use cases can emerge only by moving away from a product-centric approach that focuses on one-time sales and toward enabling an ecosystem of collaborating devices and services working in concert to build a longer-term, value-based customer relationship.

Roboethics owes its existence as a new discipline to robots and algorithms, but these are not themselves the real ethical threat. Rather, the threat comes from robotic and algorithmic approaches to situations where the human edge is critical to ensuring results that are fair and beneficial to individuals and society at large. Computers may or may not be involved; it’s the approach that matters. Addressing the threats needs to happen at multiple levels.

Traditional Agile does not consider enterprise architecture as a key part of the process but assumes that architecture guidance is being provided in the background. Traditional enterprise architecture (EA), however, has also failed to evolve and the majority of EA teams are under pressure due to the increased adoption of Agile within enterprises. Thus, the traditional role of EA has been under attack by the emergence of Agile within enterprises and its adoption beyond the IT domain.

In this Executive Update, Part I of a two-part series, we examine cloud-based data warehousing and platform as a service (PaaS) offerings designed to support data management for analytical applications. Part II will cover the trends, developments, and considerations pertaining to the adoption of self-service business intelligence (BI) environments.

In practice and training, we should use EA methodologies as a way to assist human thinking, not as a mechanical, technology-driven form of “box-checking.” Above all, we should understand that we create EA models so that we can use them as a basis for human analysis. The models are the beginning, not the goal.

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.

In this Executive Update, I recommend some books that I have found most inspiring in my life as an enterprise architect. It is a very personal list. It comprises the books that have had the most profound influence on my enterprise architecture (EA) ideas and practice. 

Establishing the organizational foundation for service leadership where everyone is the customer should be a high priority. In my leadership experience and interactions with service leaders around the world, I have learned that being an effective leader comes down to one thing: care and they will care.

Connected consumer devices and Industrial Internet applications can generate incredible amounts of data from sensors and other operations — data that can be difficult to process using traditional data management and BI tools due to the diversity and size of the data sets involved. Achieving business value from this massive data stream requires the use of big data storage and analysis technologies that can scale to meet the ­constantly increasing demands placed on organizations.

For innovation to be valuable to an enterprise, it must have real tangible business impacts and be well positioned within the organization. Linking it to the underlying enterprise architecture (EA) demonstrates how ideas have evolved. Enterprise archi­tecture needs to be able to address real innovation. If the architecture changes, what effect does this have on innovation and its related strategies? Innovation management and enterprise architecture go hand in hand. Although each can be successful in their own right, it’s when they are used in conjunction with each other that the full benefits are realized.

In this article, the author focuses on the security and privacy challenges inherent in IoT implementations and proposes solutions to help build a feeling of trust between all parties. In doing so, he answers such questions as “How do we assess the security risks in IoT?” and “How can the distinct interests of the sources and the collectors of personal data be expressed in a way that satisfies both parties and increases privacy and access control?”

IoT devices have many advantages, but also vulnerabil­ities and risks. Many smart household appliances are poorly protected (if at all) against cyberattacks. This means that any script kiddie with minimal hacking skills can use them to break into the home network. If, a few years ago, someone had said that TVs and refrigerators could be compromised by hackers to send malicious emails, you would have laughed at them. But such an attack indeed happened on January 2014 — the first known cyberattack to use smart household appliances.

Historically, social engineering exploitations in computer systems were limited to traditional Internet communications such as email and website platforms. However, in the Internet of Things (IoT), the threat landscape includes vehicles, industrial control systems, and even smart home appliances. Add to this mix naive users and default passwords that are extremely weak and easily guessed, and the threat becomes greater. As a result, the effects of a deception-based attack will now no longer be limited to cyberspace (e.g., stealing information, compromising a system, crashing a Web service), but can also result in physical impacts.

In this article, Claude Baudoin provide examples of the risks and a ­discussion of the methods available for mutual identification, authorization, and access control between IoT devices and control systems, as well as protection of data and commands as they cross the network. He also discusses how policies and risk management, not just the technology, need to be components of the overall approach.