Essential Elements of Digital Transformation

Ness Digital Engineering

Digital transformation across industries is happening at a rapid pace. The rate of change is accelerating and has a much broader economic and social impact than the previous revolutions.

New architectures that utilize the state-of-the-art in connected consumer electronics are emerging, unequaled global internet connectivity, efficient cloud computing and storage, and swarm intelligence.

Most organizations on their digital transformation journey realize that their needs can only be partially satisfied by standard, out-of-the-box services.

As a result, these businesses are increasingly seeking unique platforms to maximize their competitive advantage and meet their digital transformation goals.

This is where Ness steps in.

Ness DNA in engineering new products, designing digital transformation frameworks, modernizing legacy platforms, and our platform engineering competencies differentiate us.

In addition, having worked with many of the world’s largest industrial and transportation OEMs, component suppliers, and leading platform providers, we understand the importance of delivering value to the industry while keeping the end consumer in mind.

Digital Transformation Framework

The digital transformation journey envisioned by many industrial players is an endurance marathon achievable through a phased approach.

A common trajectory starts with:

  • Enhancing instrumentation
  • Digitizing remote monitoring of simple processes
  • Embedding basic automation with humans in the loop for support and corrective oversight
  • Applying machine learning
  • Broadening AI-powered services to control the equipment with minimal human involvement.

It is the dream, but only part of the picture. Jeanne Ross et al., in their 2019 book Designed for Digital 1 (MIT CISR), described the digital platform as only one of five building blocks for a successful digital/data transformation in a sample of approximately 200 organizations.

The others include the operational backbone (with its ERP, MES, and CRM), a shared understanding of the customer needs, an accountability framework, and an external developer platform to increase reach to the broader ecosystem.

The accountability framework and governance mechanism drive the organization’s ability to scale the most successful initiatives.

In addition, it provides the foundation needed to turn them into enterprise-grade solutions.

The digital platform drives the need for several core capabilities, including cloud platform engineering, continuous software delivery at scale (Ness refers to this as intelligent engineering), and edge software development for connected assets or data and analytics.

The following examples provide additional context to the digital journey.

Successful Digital Transformation of Businesses
transformation of business
Successful Digital Transformation Framework for Businesses


Our first example focuses on a leading map provider who followed a typical path when moving geospatial data processing pipelines to the cloud.

As a result, their data products are used in over 100 million vehicles processing over 28 TB of collected data daily.

Data processing at scale typically manifests in two workload forms – batch and streaming.

In addition, the cloud has greatly accelerated operations.

The adoption of container technology and container orchestration has empowered teams with operational abstractions that resemble aspect-oriented programming but for infrastructure—the sum of these yields’ greater agility and more frequent releases.

Ness gathered a similar experience in the intelligent transportation space by deploying an event-driven ticketing architecture in Asia.

Again, it leverages a scalable messaging broker to process events.

These events originate from the social network client LINE and public transport gates across the transport network.

A cloud deployment accelerated the delivery and allowed the team to focus on distributed system challenges associated with event ordering.


In our second example, software product development adapted to meet accelerated demands in release cadence without sacrificing quality.

Agile practices have entered the automotive industry under adaptive agile to inject flexibility at the lower tip of the V-cycle under rigorous architecture oversight.

Practices such as continuous integration or delivery have complimented the ways of working for software teams across any industry.

Ness helped a leading enterprise software vendor for content management boost its test coverage from 15% to 90% while reducing environment provisioning times from 8 hours to 4 minutes.

The overall cycle time for tests is now four days instead of 16.

Automation and a balanced test pyramid are critical to the intelligent engineering of modern digital solutions. In addition, intelligent engineering has become even more essential as the number of teams spanning the overall product family grows.

The Scaled Agile Framework cadences teams against a known release train and COVID-savvy town hall meetings enable coordination with practices.

Automation and common CI/CD pipelines help teams remain nimble and meet agility requirements.

Such was the case for our customer, a large gas turbine manufacturer.

The digital platform provides data from connected assets and supports processes along the entire package lifecycle from the cradle to the grave with 25 teams coordinated by three trains.

Edge Software Development

It brings us to our next topic, edge software development. Edge-side software in connected gas turbines has shifted the business model from selling the turbine to the energy it provides (often referred to as servitization – see Professor Andy Neely 2).

The software stacks at the edge include constrained devices and concentrators or gateways powered by more advanced microcontrollers.

You can find Edge Computing in consumer devices such as entertainment media boxes in hotel rooms and automotive electronic control units (ECUs).

One example where we combine intelligent engineering with tremendous advances in microcontrollers is active safety for a provider of radars, thermal night vision cameras, advanced driver assistance, and autonomous driving software.

Work includes requirements engineering (with tagging of the Functional Safety ASIL level), design, coding (according to MISRA C/C++ ruleset), module, integration testing, and validation testing, in line with AUTOSAR 3. x / 4.x and SPICE norms.

It allows our partner to ship their solution package, including ECU, to more OEMs while ensuring adherence to the automotive industry’s increased development and validation standards.

Electrification and autonomous driving trends influence ECU development. Ness has witnessed this first-hand with an innovative Austrian partner for developing, integrating, and testing ECUs for the powertrain of electric vehicles.

Similar to the previous example, Ness optimized software development processes and methods.


Data and analytics are a fundamental part of the digital journey as organizations continue to generate increasing amounts of data and make new decisions about their optimal placement.

Data governance remains an essential capability along with modern data processing techniques, including big data batch and stream processing or applied machine learning.

Our partnership with a radiology imaging company led to the design and implementation of an end-to-end imaging data processing pipeline, including a data forge, a feature store and catalog, a flexible data science sandbox, and finally, a systematic model-serving approach.

Cloud-based data processing can also accelerate the development cycle of a data product, as was the case with ‘driving smoothness’ for our partner, a telematics service provider. It involved processing over 35 data attributes, including GPS and data acquired via an onboard unit.

In the case of more advanced engineering applications involving structural computations, Ness leverages a distributed computing cluster to perform a solution search of systems of differential equations with parameters obtained through finite element analysis.

This enterprise-grade architecture allows the organization to expose the calculation engine to turbine engineers who perform What-If analysis for their specific conditions.

Independent of novel data engineering and data science approaches, fundamental capabilities such as data governance, architecture, data strategy, road mapping, and meta and master data management remain essential as critical decisions regarding algorithms and production data due to delegation.
Data Analytics Capabilities Of Ness


Ness is committed to cultivating innovation with an ecosystem of leading enterprise partners complementing our core strengths.

As a result, Ness enjoys long-running and deep-standing relationships with our customers on the digital transformation journey.

Here are some examples of possibilities resulting from the new capabilities mix:

  • Ability to manage hierarchies for tens of thousands of assets across multiple sites and roles.
  • Compute remaining useful life computations with requisite integrations to enterprise applications and semantic data alignment.
  • Edge-to-Cloud system testing involving multiple systems and communication paths & IoT protocols.
  • Implementation of ML pipelines which let data engineers and data scientists collaborate on multiple experiments using various datasets with clear provenance.
  • Ensure the highest unit and functional test coverage of all platform digitalization components in an automated fashion for shorter release cycles and greater customer satisfaction.

Digital technologies are evolving rapidly. As a result, they provide a host of new opportunities for businesses to capitalize on the power of their data, create efficiencies, improve quality and performance, and drive profitable growth.

Innovation is a never-ending process, and Ness is committed to improving our understanding of these partners to provide lasting value.


Ness is a digital solutions company with product engineering in our DNA, backed by a global collective of software engineers, data experts, user experience designers and innovators.

Combining core competence in engineering with the latest in digital technology, we build customer-facing platforms and software products that help businesses thrive in the digital economy. As your tech partner, we help engineer your company’s future with cloud and data.

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