Forecasting the Future of Digital Transformation in Quality Management Systems

Chris Nahil
By Chris Nahil on June 30, 2023

Within the technology world, we can make many educated predictions regarding forecasting the future. With digital transformation on many companies’ agendas, something that’s playing a key role is quality management systems and how they create synergy within a business.

Embracing Modern Technologies: Trends, Trustworthy Methods and the Shift To Cloud-Based Quality Management Systems

The world of technology moves at a rapid pace. Historically, many businesses had siloed systems to define and master data across multiple processes, and that used to suffice. We see those processes come together in a cloud-based quality management system (QMS).

Dependency on legacy quality management systems in the modern world is slowing businesses down. The out-of-date technology with systems that don’t speak to one another increases the likelihood of human error. When systems are manual, the view of an organization is limited, and data is disconnected. This is why the world is moving from on-premises, coded architectures to cloud-native platforms that create interoperability and standardize data in ways we’ve never been able to do before.

Exploring Current Trends in Digital Transformation: A Brief Overview

Over the next three years, it’s fair to say that businesses will invest heavily in technology like machine learning (ML) and natural language processing (NLP). These technologies have proven, in recent years, that they have a great deal of value in terms of their longevity and ability to improve a business in meaningful ways. They enable businesses to automate processes better while ensuring maximum interoperability.

Importance of Digital Trustworthy Methods 

These trustworthy technologies include:

  • Artificial intelligence (AI): This uses computer systems to simulate human intelligence to perform complex tasks.
  • ML: This type of AI uses algorithms to imitate how humans learn, helping it improve accuracy.
  • NLP: This uses computers to analyze and synthesize natural language and speech to better understand written content.

The benefit of these technologies is far-reaching, allowing businesses to analyze data in new ways while adding new automation capabilities to their processes. 

The Transition From On-Premises to Cloud-Based Quality Management Systems

On-premises software and data storage raises questions about security, accessibility and the viability of that data in an increasingly high-tech future. A cloud-based approach to data allows flexibility, security and visibility. This is why we’re seeing more businesses contemplate or actively move away from on-premises systems to cloud-based ones. 

Data can also be standardized and streamlined when brought together on the cloud via a QMS, evolving from non-structured to semi-structured to fully structured data. The technology world is thinking about data architecture very differently than in the past. 

The Rise of Digital Transformation

Forecast For the Next Three Years Based On Current Trends

Between now and 2026, it’s fair to predict that businesses will invest heavily in trustworthy technology, whether AI, ML, or NLP. We’ll also see the migration from on-premise solutions to more cloud-based ones. 

Digital Transformation Predictions For 2024, 2025 and 2026

Specific predictions for the future include:

  • By 2024: 65% of organizations that have deployed automation technologies will introduce AI (i.e., ML, NLP, process mining, task mining and intelligent document processing capabilities). 
  • By 2025: The percentage of enterprises in all industries that will have engaged digital business model transformation services to implement analytics-driven business decisions and management capabilities is expected to be 70%. 
  • By 2026: The market for software enabling hyperautomation will grow to over $1 trillion. 

Factors Driving Investments in Digital Transformation

As investment in digital transformation grows, there are several factors contributing to this, including:

  • Growing volume of data
  • Limited resources to support large volumes of data
  • More competitive market dynamics
  • Companies’ desire to innovate and automate
  • Growing resource costs
  • Evolving regulations
  • General advancements in technology

The Importance of Data Harmonization and Integration

Data harmonization is about consistency regarding the approach to a business’s quality environment. Benefits of data harmonization include:

  • Maintaining a consistent understanding across the organization
  • Leveraging the same data and, most importantly, the correct version of the data
  • Making more informed decisions
  • Taking action more quickly

The Need For Data Harmonization Across Enterprise Systems

Data harmonization doesn’t necessarily mean having only one system that manages quality – a business might have several systems, meaning there’s data in different places – but there must be a single source of truth. 

True harmonization means there shouldn’t be, for example, an approved supplier list located in multiple systems where several people manage said list at any one time. It’s a case of one system managing the list and other systems being able to access it if necessary. This means that if someone needs to make a change, it can be seen across all systems by all users, no matter where they are based.

Data harmonization integration across enterprise systems is essential to generating the best insights, ultimately improving business performance and giving us more meaningful results.

Challenges Faced When Integrating Systems During Digital Transformation

An integrated approach is key, but it’s not without its challenges. When integrating systems, organizations must decide how much of their legacy data they want to use and how much of it is and isn’t critical to cleanse the information and provide meaningful insights. 

Even if an organization implements something state-of-the-art, proper integration will be challenging unless it adopts the right governance. Correct, harmonized data models are required to harness the power of the technology and the data and streamline integration. 

Other challenges include:

  • Interoperability between systems, or lack thereof
  • Whether businesses are utilizing the full potential of their solutions
  • Immature data and/or poor handling of data

How Interoperability Adds Value: A Real-World Example of Digital Transformation

When we talk about interoperability between layers, it’s useful to think about it in terms of non-conformance. For example, when we think about analytics platforms that are pulling strictly from the QMS, with a data model present in the QMS, we get an insight into the performance of the non-conformance system as a whole. 

With all the data in place, a QMS software becomes incredibly powerful and a business can start overlaying that data with information in its enterprise resource planning (ERP) system. Many organizations working through digital transformation are looking at how a QMS and ERP will integrate, as this is where the data starts adding value. When an organization overlays quality information with its inventory, it can better make informed decisions based on those insights. 

Additionally, an organization can determine how the processing of data, which might look like a purely compliance-oriented metric, impacts inventory and what it’s doing with those goods. Connecting with ERP data to create a harmonized structure helps connect the dots between the inventory in the ERP system with what’s going on from a product quality and non-conformance standpoint and how the two relate to fulfilling orders and ensuring top quality for customers. 

The Shift Towards Hyperautomation

Definition and Importance of Hyperautomation

Hyperautomation is about using the right tool at the right time and place. This could be an amalgamation of tools – such as an ERP – but whatever it is, hyperautomation revolves around sewing together the data landscape to deliver exactly what’s needed, where it’s needed. This creates a seamless consumer experience. 

Composability and Decision Intelligence in Enterprise Architecture

This industry is packed with data and plenty of intelligence used to make important decisions, including regulatory intelligence and even rules within individual businesses. We’re now starting to embed this intelligence within applications so that those apps can make some decisions for us. That’s something we’ll see more of as technology and innovation increase.

The Role of Cloud-Based Approaches

A cloud-based approach is very on-trend right now, and with good reason. It enables flexibility to move around data and get into other places, where that data can be streamlined and made more accessible, visible, and far more secure. It requires thinking about data architecture in a new way.

Assessing Data Maturity and Best Practices

Understanding the Maturity Makeup of Data Architecture

Understanding data architecture’s maturity makeup means businesses must look at baselines and the maturity curve. Is the data really being mastered? Defining and mastering data enables composability and allows the business to get to a place where it’s using data as a currency.

Baseline, Platforming and Data-Centric Approaches

As a business moves through the digital transformation journey, platforming becomes important to determine whether data is being ingested effectively and used appropriately within an application. The result is a data-centric makeup or architecture that delivers essential information at the right time to the right end-user. They don’t have to worry about what’s happening under the hood – it’s all simply occurring seamlessly from their perspective.

Moving From Reactive to Proactive or Preemptive Data Management

When a business knows its data inside and out and the architecture is mature, the management of that data moves from being reactive – which wastes valuable time and invites problems – to proactive, which leads to pre-emptive management. This minimizes issues as they can be solved before they have the chance to occur. 

Quality Management Systems: Synthesizing Digital Transformation, Data Harmonization and Hyperautomation for Enhanced Performance

Bringing together the different elements of digital transformation, data harmonization and hyperautomation leads to enhanced, pre-emptive use of data and an exceptional experience for the consumer. When this balance is achieved, high quality becomes a given and something that’s standardized in efficiency.

There’s so much to consider with quality management, including compliance requirements that span nations, businesses and sectors alike. A single system that creates a holistic view of the organization while being flexible is key.

With an EQMS, these considerations become straightforward elements taken care of automatically, saving your business time and money while stripping problems to a minimum and providing a top-down view of everything happening. 

Want to know more about how cloud-based quality management can benefit you? Request a demo today.