Strategic advice to leverage new technologies

Designers are working feverishly to add connectivity to products, weighing the potential value to users, as well as risks. For example, the capability exists today to make toilets smart, but is there really a need for a toilet that can be flushed remotely? On the other hand, a smart oven can be triggered remotely to heat up by the time you return from work. It could be a great time saver, but is it a fire risk? As designers grapple with these questions, there are also a host of design issues that they must keep in mind while moving toward IoT capabilities. In this Executive Update, we explore the top 10 design considerations.

A value archetype is a basic model for value creation based on digital data streams (DDS). Archetypes are not exclusive classes of value creation, meaning you can adopt one for a specific set of customers and another for a different set. In classifying companies, consider the five value archetypes described in this Advisor.

While there are many ways to represent a business pattern, there is also value in having a more formal or consistent model. At the other extreme, it would be a mistake to be too rigorous by only ever applying one modeling approach. As a general principle, any model must be suitable for the use to which it is applied.

Cognitive computing is beginning to impact a range of industries due to 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. 

Are there opportunities you’re missing to mine your organization’s data, to create value from the real-time flow of big data? Can you achieve improved customer experience through increased, data-based experimentation?

Corporate information security can be challenging due to the numerous avenues that an attacker can traverse. All companies need methods to secure the hardware, software, and communication channels of mobile systems, internal networks, email traffic, and so on. Before developing a method, managers should engage technical and nontechnical staff to answer the following question: “How do we store, process, and transmit data in a secure manner?” As we explore in this Executive Update, to answer this question effectively, you’ll need a teamwork process in place that engages many people who are not thought of as security experts. 

The most common pressures driving Agile data analytics investments are data stored in silos and poor data quality impacting decision making. Both complaints betray the burden of legacy business systems and analytics. Data in back-end IT systems is very often difficult to access, difficult to integrate, difficult to normalize, and therefore difficult to harness to answer the questions posed by the business. Mired in these inflexible and out-of-date sources of data, the efforts to create front-end tools and reports will have limited success.

By leveraging business architecture as the mechanism to connect across business areas and drive toward a collective intent, IT will be in a better position to plan its strategy and roadmaps. What comes down the pipe from the business should be self-consistent and offer a holistic picture of the collective intent of the organization.