Methodical software development processes can unlock hidden value in your digital transformation projects. A well-implemented Agile framework that incorporates adaptive, evidence-based planning, iterative delivery, and automation can reduce risk, shorten release cycles, and diminish workforce disruption. Effective test automation, including strategies such as Test-Driven Development, Behavior-Driven Development, and Acceptance Test-Driven Development, can increase predictability but also add their own unique challenges.

We live in exciting times where the digital innovations materialized by EDGE Computing, SDN, and IoT applications translate into significant business use cases. By 2025, the Edge Computing market is expected to be worth $3.24 billion, with 26 billion smart devices generating at least 500 ZB of data a year.

Everything is being transformed by these technologies ‘ high-powered computing and networking capabilities, from the administrative processes in factories to the power grids in Smart Cities.

Once the business case for Robotic Process Automation (RPA) implementation is approved and a decision is made to move forward, the next step is to understand RPA platforms available and find the right partner who can assist in the transformation. As discussed in the first article in this series, the combination of technological and business factors will help select the RPA platform.

The following are key features to keep in mind:

The continuous digital revolution calls upon enterprises to abandon the crumbling, almost ready-to-expire legacy systems and replace them with more agile modular applications and database management systems.

New-world programming is an enterprise norm today that delivers cost-efficiency and faster time-to-value. Modern-day applications provide better support for multi-device compatibility, resource reusability, cross-platform functionality, and seamless integration with other business systems.

With the increasing complexity of modeling frameworks and their relevant computational needs, organizations find it harder to meet the evolving needs of Machine Learning.

MLOps and DataOps help data scientists embrace collaborative practices between various technology, engineering, and operational paradigms.

MLOps is a set of practices that infuses Machine Learning, DevOps, and Data Engineering practices for a reliable and data-centric approach to Machine Learning systems during production.

In our previous blog, “Enterprise BPM Transformation – Embrace the Change,” we discussed the transformational capacity of BPM and how it helps organizations gain better visibility that translates into higher productivity. Many companies invest in tools like Lucidchart, Visio, Modelio, Pega BPM, ServiceNow BPM, etc., based on their diverse and specific business needs. While each of them has benefits and limitations, we at Radiant highly recommend jBPM.

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.

Data is a crucial digital asset for any individual or organization in their decision-making journey. According to IDC, by 2025, global data will grow to 175 zettabytes. This explosion in data from multiple sources like connected devices requires deriving valuable insights to make smarter data-driven decisions. Data Science helps enterprises understand data better and optimize its utilization for time-consuming and expensive processes.

Business leaders often experience the heat to remain competitive, deliver quality-driven products and services, optimize costs, and improve productivity.

Many of them are leaning on Business Process Management (BPM) software to make their daily operational processes adaptable, agile, efficient, and reliable to remain relevant in a dynamic tech marketplace.

Radiant Digital has over twenty years of experience, helping customers re-engineer their business processes. This article is the first in a two-part series on business process automation. In this series, we will discuss the evolution of Robotic Process Automation (RPA), how to begin your automation journey, and how to promote success.