Your most valuable asset is your intellectual capital, i.e., the knowledge of your employees. Unfortunately, when people move on, their knowledge goes with them. There are two different, but not mutually exclusive, strategic approaches for managing tacit and explicit knowledge.

Radiant uses four guiding principles when it comes to supporting our customers on their journey toward digital transformation.

Radiant knows that maintaining a competitive edge it is not only about connecting with customers in new and innovative ways, but also about continuously keeping them engaged.

When we consider customer needs, Radiant considers all stakeholders — executives driving strategy, employees executing work, and customers engaging.

Most of the work in the US today is mental labor as opposed to manual labor. As individuals get more skilled in their work and become experts, their value to your organization grows. Once an employee reaches relative expertise, their absence (or loss) becomes a significant detriment. But capturing their knowledge allows businesses to reduce the impact of absence substantially. However, effective knowledge capture is difficult because experts tend to have a high omission rate when attempting to detail and record their decision-making.

Consistency is the most fragile design principle that influences user trust and familiarity with your UI design or product.

Improving consistency polishes the look and feel of your design. Additionally, it purges imperfections, unwanted content, or navigation flows that mislead the user.

Design consistency drives coherence and creates a harmonious uniformity among discrete UI elements. Consistency is fundamental for a good UX because it creates an experience that users can rely upon every time they interact with your brand or product.

UX Designing is an area that needs creativity, flexibility (in thought and action), and a data-driven approach. It’s an area of art and science that includes interpersonal and functional roles.

It requires more than the mastery of some useful wireframing tools, interaction design, visual information architecture, visual design, and human-computer interaction. UX design is all about a user-centric approach and excellent problem-solving skills.

As the world becomes more digitized, billions of internet users provide personal information or sensitive data like credit card details online. Though some online businesses provide a secure channel on their websites for transactions, data privacy issues are still enormous. This is causing unprecedented privacy incidents and inconveniences.

The darker side of not handling privacy thoughtfully includes data loss, identity theft, personal information misuse, cyber threats, and significant financial losses.

If minimizing failures, shortening development cycles, and smoothening the product development cycle are your paramount goals, then Digital Twin adoption should be your top priority. Digital Twin offers visibility on your production line and helps predict the future of various processes. It essentially helps maximize OEE, optimize productivity, and improve business profitability. In other words, Digital Twin empowers engineers to analyze, explore, and assess physical assets, processes, and systems using virtual tools.

Building Block of Digital Twin

Digital twin needs data, algorithms, sensors, and a data access framework. This is called a digital thread that runs through the data network, establishing links and correlations. The digital thread ensures that all the process components can access the latest sensor data in a product lifecycle (sample shown below).

Wrapping up

Digital Twin can integrate system insights, improve visibility on machine states, and trigger appropriate remedial business workflows. Embracing this potentially disruptive technology now can help businesses remain relevant, competitive, and digitally transformed. Radiant is playing a key role in advancing this new technology!

While some user experience research (UXR) problems can be solved using one research method, others benefit from mixed-methods.

In our previous blog, The Fundamentals Of MLOps – The Enabler Of Quality Outcomes In Production Environments, we introduced you to MLOps and its significance in an intelligence-driven DevOps ecosystem. MLOps is gaining popularity since it helps standardize and streamline the ML modeling lifecycle.

A recent search on LinkedIn for technical writing job opportunities returned over 50k results in the United States. The deliverable types included internal-facing work processes, quality assurance workbooks, developer training resources, and external-facing consumer support content, proposals, and marketing. The delivery platforms had print, digital, and a blended format. The industries were just as diverse—aerospace and defense technology, oil and gas, computer software development, consumer electronics, and pharmaceuticals. All this was just within the first page of the results.