Strategic advice to leverage new technologies

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 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.

 “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.

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.

Compromises are inevitable in larger transitions. After all, you’re doing an incremental journey. To figure out which of the compromises are pragmatic steps and which of them are simply in the wrong direction, you need a clear picture of the business goals behind the Agile transition.