How does one measure culture? How can measures and metrics be used to influence positive culture change? Cultural measurement needs to focus on evaluating outcomes and processes that reflect behavior change. Two major types of measures that provide valuable perspectives are business outcome and enabling metrics, and we can apply both measurement types to culture change.

My previous Advisors referred to the use of business capability modeling and heat maps to better focus on business areas that require attention for an IT rationalization. In this Advisor, I expand on the use of reference models, what they are, and why to consider them.

Increasingly, government agencies, businesses, universities, and other organizations are working together to build AI systems that analyze geo-mapped and other location-based data. One such impressive new ML application uses Uber driver data to track and alleviate urban traffic congestion. It is designed to give urban transportation analysts and traffic engineers access to information about city traffic patterns in order to relieve bottlenecks, chokepoints and other problems.

Even when responses to diversity, equity, and inclusion disparities are obligatory, we’re still missing the mark. Truth is, we have most of the pieces. We’re not apply­ing the knowledge that we have, which is why we’re missing the big picture. This Advisor considers how businesses navigate employment.

One of the key advances in software development in recent years has been the automation of many tasks and services. Contemporary software automation goes far beyond simple task runners and is now beginning to augment artificial intelligence and machine learning into the process, which further widens the scope of what can be automated. However, not all tasks in software development are well suited to automation, and not all tasks should be automated. Where do you draw the line, and where is software automation most useful for developers?

While every UN member state should use the SDGs for framing their agendas and political policies, the responsibility and potential for meeting these goals also lies with every individual and organization. This includes governments, non-profit organizations, and even for-profit businesses, which can be a powerful mechanism for change.

It takes a lot more than rugged and hardened data architecture to ensure data quality. One way of thinking about data quality issues as they relate to data architecture is to examine and address them through a layered approach described in this Advisor.

The unemployment and income instability caused by COVID-19 has resulted in many people having difficulty meeting their credit obligations and many industries struggling to run their operations. Worldwide, governments and businesses have taken steps with exceptional speed to match this pandemic‘s unprecedented nature.

Often overlooked as financial filings to meet regulatory requirements, company annual reports provide an endless well of insight about how and how well companies articulate and integrate business strategy with risk management and performance measurement.

Unless an organization pursues a strategy of innovation for its own sake, then digital era products and services are still subject to the marketing lifecycle with profitability and reward appearing in the market growth and maturity stages. This means that organizations need to create structures and a culture within which they can simultaneously innovate and deliver the new inventions. There needs to be a marriage of innovation expense control and revenue generation. So how does a modern leader engender such a culture?

In this Advisor, we take a look at how COVID-19 is affecting automation efforts, based on findings from an ongoing Cutter Consortium survey on intelligent process automation.

In nature, apparently robust systems can be disrupted by a single new invasive species, while other systems can retain their resilience despite a sustained assault. The behavior of such sys­tems can be explained by the interdisciplinary theory of complex adaptive systems (CAS). This Advisor describes how we can use CAS theory to support organizational change.

Our findings indicate that 2021 looks to be the year that organizations really get serious with their IPA efforts. Consequently, we should expect to see more organizations move beyond the experimentation stages and start developing and deploying new IPA applications into production environments.

Many classic change management models fail to consider the significant dimensions of technology and innovation. This Advisor shares a case study involving a bank that devel­oped key performance indicators to track all dimensions of change, enabling the bank to constantly improve its capability to implement change.

In this issue of Cutter Business Technology Journal (CBTJ), we examine the role that fintech will play in facilitating economic recovery and growth.

In my last Advisor, “Using Heat Maps to Better Drive the Rationalization of Your IT Landscape,” I introduced the use of business capability heat maps to better focus on business areas that require attention. This Advisor further illustrates the approach and offers next steps in sharing your organization’s evolution with stakeholders.

A “next normal” is undoubtedly going to emerge once the COVID-19 pandemic slows down or resolves. But while the coronavirus pandemic has disrupted the economy and work routines, it also provides opportunities for organizations to implement continuous learning and make progress toward upskilling and reskilling employees.

While strategy, risk, and metrics coexist tangentially in many organizations, the nexus of the three reflects the degree to which management thinks and acts proactively.

Businesses have traditionally organized themselves to ensure optimal effectiveness in each of their business functions. In today’s business climate, however, shorter product lifecycles, demand for customization, rising consumer expectations, and the growth of automation and data challenge this model. This Advisor describes the building blocks of generic capabilities and describes three categories of capability-based players seeking competitive advantage.

The chief scientific officer — or CSO — plays a key role in research and technology organizations (RTOs), ensuring scientific leadership, setting medium- to long-term strategy, and influencing scientific investment, among other roles. In a recent study, Arthur D. Little (ADL) set out to understand the full scope of the CSO position in RTOs across the globe.

If you believe the undisputed research about diversity, equity, and inclusion improving outcomes, then ensure your behaviors match. If you value the importance of living in alignment with your values, then show the proof.

Heat mapping allows for high variability. It is a very universal approach to produce easy-to-read material that helps prepare decisions (e.g., for capability-based IT planning and IT rationalization). Heat maps may also show “hot spots” in the IT landscape and help communicate them.

Interest among organizations for developing detailed plans and strategies for adopting and disseminating intelligent process automation (IPA) technologies and practices is very high, pointing to accelerated adoption of IPA over the next 12-24 months.

Despite a lack of solid evidence, most stakeholders agree that the health economics of mHealth look compelling. If, however, mHealth implementation expands post-pandemic as an integral part of established healthcare over the long term, the question then becomes who should pay for mHealth. The answer relies primarily on two parameters: who benefits economically from mHealth and who bears the risks of healthcare costs.

How well is your company dealing with the pandemic? As the pandemic crisis deepened and all your business plans were invalidated rapidly, you probably tried to experiment and innovate at high speed. Experimenting can easily be done as an undirected action if there is no reflection happening. Only reflection allows us to come up with experiments that are based on an hypothesis (drawn from both experience and theory). Then we can innovate — by understanding how to measure the experiments in order to (in-)validate our hypothesis.