If we can stay focused on the intent and spirit behind the words “agile” and “architecture,” maybe we can avoid the cycle of hype and despair, at least as far as agility is concerned. This requires discipline with both words and actions because they are both part of the real world.

In today’s business environment, it’s rare to speak with an enterprise leader who is not adopting some form of Agile development practice. Indeed, global companies of every size are adopting Agile practices and principles. However, traditional Agile does not consider enterprise architecture (EA) as a key part of the process and only assumes that architecture guidance is being provided in the background. As we explore in the accompanying Executive Report, EA leaders who have identified the need to change in light of the emergence of Agile have significant opportunities to help Agile projects move more quickly and be more effective.

In today’s business environment, it’s rare to speak with an enterprise leader who is not adopting some form of Agile development practice. Indeed, global companies of every size are adopting Agile practices and principles. However, traditional Agile does not consider enterprise architecture (EA) as a key part of the process and only assumes that architecture guidance is being provided in the background. As we explore in this Executive Report, EA leaders who have identified the need to change in light of the emergence of Agile have significant opportunities to help Agile projects move more quickly and be more effective.

Self-service BI can lead to increased, widespread dissemination of BI/analytics tools and practices across the organization; in effect, helping to promote a data-driven culture. It allows employees of all types — at least in theory — to more easily locate, access, and work with a range of information and data. This is accomplished in several ways, including via highly visual, intuitive self-service BI tools and automated data preparation and workflows that provide advice on various steps of the analytical process.

In essence, a Jeopardy!-playing application posed two different problems: understanding natural language (NL) so as to be able to identify the right question and then searching a huge database of general information for an answer that fit the question. Searching a huge database quickly was a more-or-less physical problem, but parsing general questions in English and then determining which of several possible answers was the right match for the question being asked were serious cognitive problems.

The idea that an organization should change from a particular language, such as one of those named above or any of the myriad of others that may be currently in use, to find DevOps success is a slippery slope and is not necessary to implement the DevOps principle of increased deployment frequency. Any language will work well with the right principles and practices in place.

Enterprise architecture plays a growing role in strategic discussions and decision-making, and many EA components are no longer contained within a single enterprise because they form part of a much wider environmental, social, or human ecology. In other words, we are at a crucial tipping point for EA, where the decisions that enterprise architects make play a vital role in our collective destinies and futures.

Interest in smartbots and intelligent virtual assistants employing AI, natural language processing (NLP), speech recognition, and other cognitive techniques for automating and enhancing customer interaction and experience is increasing.

Harmonization is a cloud provisioning and procurement decision-making approach relevant to every enterprise cloud buyer with large, complex, or sophisticated cloud computing needs. The organizations most likely to realize the long-term benefits of this new method are typically those with increasing cloud usage, particularly organizations with several long-term projects. This method is a collection of strategies designed to mitigate and manage the oft-overlooked cost impact of cloud sprawl. It provides oversight and influence back to enterprise IT, while minimizing the loss of developer agility in rapidly provisioning suitable cloud resource. Once implemented, it engenders a coordinated, sustainable approach to cloud procurement that reduces costs, improves governance, and allows for efficient procurement without compromising business agility. 

Farmers and the agricultural companies that service their needs deal with vast amounts of structured and unstructured data. Analysis of such data gathered from across a variety of growers and growing conditions, combined with data from other sources — including satellite and drone imaging, field-level sensors, weather, and other historic data — can provide insights to enable farmers to make timely decisions that can improve their yields and minimize losses due to unpredictable changes in weather.

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. In the balance of this article, we discuss several of the challenges and possible solutions, while addressing the components involved in any BDA plan.

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. In the balance of this article, we discuss several of the challenges and possible solutions, while addressing the components involved in any BDA plan.

This article explores a single concern: describing the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. A generalized model is a model that works across all data sources no matter what type of data is present. Generalized analytics can answer all questions, from simple to complex, across all data types. This approach leads to eight well-defined, objective attributes, which collectively form a precise capabilities-based definition of a NoSQL analytics system.

This article explores a single concern: describing the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. A generalized model is a model that works across all data sources no matter what type of data is present. Generalized analytics can answer all questions, from simple to complex, across all data types. This approach leads to eight well-defined, objective attributes, which collectively form a precise capabilities-based definition of a NoSQL analytics system.

This article argues for an overarching framework that will not only facilitate adoption of analytics and technologies, but will also provide a solid foundation for taking a strategic approach to big data. This framework is called the Big Data Framework for Agile Business (BDFAB v1.5), and its development is based on a review of the relevant literature, experimentation, and practical application.

This article argues for an overarching framework that will not only facilitate adoption of analytics and technologies, but will also provide a solid foundation for taking a strategic approach to big data. This framework is called the Big Data Framework for Agile Business (BDFAB v1.5), and its development is based on a review of the relevant literature, experimentation, and practical application.

How do we ensure that we are getting the most from big data, cognitive computing, and whatever lies beyond, to improve the probability of making the right decisions, in the right context, and for the right reasons? We believe that lessons learned in over five decades of Lean Thinking can help guide us forward in this journey, and we will use examples from the financial services industry to illustrate them.

How do we ensure that we are getting the most from big data, cognitive computing, and whatever lies beyond, to improve the probability of making the right decisions, in the right context, and for the right reasons? We believe that lessons learned in over five decades of Lean Thinking can help guide us forward in this journey, and we will use examples from the financial services industry to illustrate them.

I think I have finally have come up with a good theory to explain why there have been so many stories of government projects in trouble recently: the inhabitants of Talos IV are managing them.

 “Big data” and “analytics” are among the most overhyped and abused terms in today’s IT lexicon. Despite widespread use for almost a decade, their precise meanings remain mysterious and fluid. It is beyond doubt that the volume of data being generated and gathered has been growing exponentially and will continue to do so, intuitively validating the big moniker. However, other vital characteristics of today’s data, such as structure, transience, and — most disturbingly — meaning and value, remain highly ambiguous. Analytics also remains troublingly vague, as it is prefixed with ­adjectives ranging from operational to predictive.

 “Big data” and “analytics” are among the most overhyped and abused terms in today’s IT lexicon. Despite widespread use for almost a decade, their precise meanings remain mysterious and fluid. It is beyond doubt that the volume of data being generated and gathered has been growing exponentially and will continue to do so, intuitively validating the big moniker. However, other vital characteristics of today’s data, such as structure, transience, and — most disturbingly — meaning and value, remain highly ambiguous. Analytics also remains troublingly vague, as it is prefixed with ­adjectives ranging from operational to predictive.

Establishing a successful, sustainable business architecture practice is a journey that takes time, especially within large organizations. While there are always many ways to achieve an outcome, this Advisor shows a practical and well-tested path to accelerate the process of establishing and maturing a practice.​

Part I in this two-part Executive Update series covered the use of cognitive systems in banking and finance in three application domains: (1) research and discovery; (2) business intelligence (BI), advisory, and decision support; and (3) risk assessment, compliance, and fraud prevention. This Update expands on the topic and examines the use of cognitive technologies in banking and finance for enhancing customer service and customer experience management.

Part I in this two-part Executive Update series covered the use of cognitive systems in banking and finance in three application domains: (1) research and discovery; (2) business intelligence (BI), advisory, and decision support; and (3) risk assessment, compliance, and fraud prevention. This Update expands on the topic and examines the use of cognitive technologies in banking and finance for enhancing customer service and customer experience management.

For the purpose of this article, we define context awareness as the information necessary and ­sufficient to perform the intended func­tion of the device effectively and efficiently. Typically, but not always, the context can be ascertained comprehensively by answers to some or all of what we like to call “the four Ws”: where, when, who, and what. A simple IoT device with limited functionality may only need to answer one or two Ws, while a more complex IoT device may need answers to all four, and perhaps even to additional questions such as how, why, which, how much, and so on.