Industrial Data Fabric solution best practices – Part 1, organizational readiness and alignment

Industrial Data Fabric (IDF) solutions on Amazon Web Services (AWS) enable manufacturing and industrial companies to build the foundation for digital transformation to help optimize operations. IDF helps align different data types in the time domain with context as it combines many disparate data sources like time series data from machines, maintenance data from EAM and Enterprise Resource Planning (ERP), production order details from MES (Manufacturing Execution Systems), and scheduling and planning systems and material management systems. It is through this process we discover the semantic relationships between the different data types as well as the content of these data types.

IDF solutions on AWS help leading companies harness data from disparate sources with unprecedented speed and effectiveness. (Read more on this in previous blogs such as Getting Started with the Industrial Data Platform on AWS and Succeeding with Industrial Data Platforms.) Yet, implementing an industrial data foundation is not without its difficulties—from strategy to implementation to production support and evolution. 92% of executives cite cultural issues as the key barrier to establishing a data-driven culture, according to a recent survey by Harvard Business Review.

This two-part blog series speaks to some of the higher level considerations when implementing a modern industrial data strategy, and distills some of the best practices to help organizations accelerate IDF implementations and increase the value they bring enterprise-wide. The best practices are organized by the stages of an IDF implementation, as shown in figure 1 below. In this post, we focus on the alignment and organizational readiness stage. In part 2, we will provide insights on the foundational build, launch, and scale & optimize stages.

Figure 1. IDF implementation stages

IDF journey and challenges

The best practices in these blogs address some of the most common challenges that organizations across industries face when implementing an industrial data foundation (see Figure 2 below). One of the first potential impediments to large strategic initiatives such as IDF is the alignment of stakeholders across business units (BUs). BUs have often evolved with specialized processes, and organizations are rightfully skeptical that one common architecture could fit many different BUs. Alignment of stakeholders across BUs is imperative to launch a successful IDF effort, because the return on investment and innovation opportunity is much greater when an IDF can be used enterprise-wide. Complex team structures and the organization of people, processes, and technology can also be a challenge. Simplifying these with what AWS calls a single-threaded leader and two-pizza teams can help. Finally, enterprises implementing an IDF benefit greatly from a culture of calculated risk-taking to meet evolving business objectives while at the same time mitigating security risks.

Figure 2. Challenges with implementing IDF

More specifically, manufacturers often struggle most with finding, accessing, understanding, and preparing data for use in applications – from analytics to automation. Many have grown through acquisition or integration, resulting in different technology standards and silos of data that make collaborating and deriving common insights difficult. Others are challenged with legacy infrastructure and applications that worked well for decades but no longer provide the agility to compete in the age of generative artificial intelligence (AI). Finally, the convergence of operational technology (OT) in factories and information technology (IT) in offices simultaneously offers the promise of seamless integration and the risk of exposure of critical assets.

To help meet these challenges during the alignment and organizational readiness stage, as well as the foundational build stage, AWS Professional Services (ProServe) can help. AWS ProServe is a global team of experts that can help you realize your desired business outcomes—together with your technology partners and team. ProServe offers highly skilled specialists, proven methodologies, and accelerators to help with everything from closing skill gaps to planning and orchestrating implementation in order to give customers a jump start with IDF.

Alignment and organizational readiness

The alignment and organizational readiness stage encompasses the initial discussion of Industry 4.0 transformation opportunities for OT/IT convergence to drive shop floor excellence, technical debt reduction, scalability, and plant adoption. Shop floor excellence is a critical element in this stage to enable organizations to meet customer demand and maintain profitability. ProServe can help organizations reimagine their own industry leading shop floor excellence by facilitating alignment across divisions and alignment with industry leading practices.

Goals

In the Goals part of this stage, it is critical to lay the foundation for success in the subsequent steps. Two key considerations in this stage can make the difference between a fast, smooth IDF implementation and one fraught with unexpected issues and delays.

1: Start with a mission statement

Most organizations implementing IDF have identified some challenges, such as data silos or fragmented analytics tools, but few take the time to create a mission statement to guide the effort. A mission statement can be as simple as “Company X is facing a competitive landscape shift to AI-powered automation and needs to harness all assets with the right architecture to be the smartest and fastest in the industry.” Though short, a well-constructed and thoroughly vetted mission statement can galvanize support and build the momentum necessary to accelerate an IDF effort and keep it focused on the most important business objectives. Don’t shortchange this effort—experience has shown that writing a concise and focused mission statement takes time. Remember the quote from Mark Twain, “I apologize for such a long letter—I didn’t have time to write a short one.” A good mission statement should contain a few key pillars from the IDF North Star Vision Architecture (NSVA) (see figure 3 below) to help the team understand the purpose and value of items in the scope of the IDF.

2: Evangelize enterprise-wide applicability from the start

Different BUs, such as manufacturing and research and development (R&D), can have very different data needs. Manufacturing may need finely tuned and highly optimized edge models for quality assurance and predictive maintenance that run efficiently at scale for long periods of time, while each scientist in R&D may want to create a new model every day to experiment with different approaches and iterate rapidly to a new solution. Different divisions can also have very different cultures and may not believe that a solution for one division could be directly applicable to another. A common stall point for IDF implementations is delivery of a highly successful solution in one division, with other divisions concerned about scaling. With IDF, however, quite the opposite is true, and the goal is to start with a data backbone that is applicable enterprise-wide. Evangelizing the power of the common data backbone, and the fact that it can be tailored to individual divisions early on is essential to accelerate IDF implementation. An executive sponsor from the C-suite is usually the best person to champion this activity.

Discovery and design

With the right preparations in place and the IDF effort kicked off, the next stage is to discover and document requirements and create an NSVA as well as a detailed design for the first phase of the effort, typically one use case at one site. The NSVA articulates the architecture standards, tool choices, selection guidelines, and road map for the ultimate enterprise-wide implementation of the IDF across BUs and use cases, specific to the implementing enterprise. An example NSVA reference architecture is shown in Figure 3 below.

The key to success in this phase is to “begin with the end in mind” and “think globally, act locally.” While it may be tempting to grow the scope of the NSVA and first use case, remember the IDF mantra: “Think big, start small, and go fast.” Too many organizations become paralyzed trying to design the perfect initial use case with the most comprehensive features. Starting with a simple but impactful scope, thoroughly vetting it against architecture standards, and aligning it with a strategic vision quickly lead to a firm IDF foundation.

Figure 3. Example NSVA

3. Architect for the enterprise, design for the first use case(s) and site(s)

The discovery and design stage should experience a healthy tension between the need to facilitate a specific use case at a specific site and the need to remain relevant and extensible to the enterprise. In eagerness to start the first use case and realize immediate benefits from the IDF, it is all too easy to forget the mission statement and the NSVA, but it is essential to take the extra time to make sure that design decisions align with the NSVA and that the architecture for the initial scope is extensible to future use cases across the enterprise.

4. Conduct an AWS Well Architected review before finalizing the NSVA

An architecture review board (ARB) review of the NSVA is a best practice that pays dividends in this stage to help verify extensibility. AWS Well Architected—which helps cloud architects build a secure, high-performing, resilient, and efficient infrastructure—provides six pillars to organize an architecture review, along with numerous best practices and lenses for specific technology domains and use cases, such as the Internet of Things (IoT), data analytics, and serverless applications, which can all play a role in IDF architecture. The review should include stakeholders who each represent one of the pillars from across the enterprise to reduce the chances of missing a key requirement or constraint that could slow or even derail an IDF implementation in the future. Comprehensive representation in architecture and design reviews is also essential to building buy-in and momentum to carry the IDF beyond the first phase.

5. Capture and present relevant findings and recommendations, even outside the scope of IDF implementation

While diligent focus on scoping and running the IDF effort is important in the discovery and design stage, findings should be documented and summarized for executive sponsor review, even if they are only tangentially related to implementing IDF. Systemic issues, such as difficulty identifying data owners or siloed decision-making, are common, especially in large organizations, and they can impact the success of IDF no matter how well-architected the solution is. It is important to document and raise issues that could impact IDF in the future. Findings and recommendations from this stage offer value beyond the IDF effort.

Prerequisites

6. Identify and address prerequisites early

The IDF contains proven architecture patterns that provide a jump start to digitizing and becoming data driven, and relies on some foundational elements. Network connectivity is one example. While the IDF has patterns for autonomy at the edge in case of interrupted connectivity, the full benefit of IDF comes when data can be aggregated and made available widely across the organization, so reliable networking with enough bandwidth to remote sites and facilities is key. Data ownership is another common example—no amount of technology can overcome a lack of a clear organizational structure for data ownership, which needs to be in place for a successful IDF implementation.

Completing a successful alignment and organizational readiness stage is often half the battle in implementing IDF. Organizations that overcome key obstacles in this stage are extremely well positioned and likely to succeed in subsequent stages, where the real value of the IDF comes together.

Look to Part 2 in this series to learn insights about the foundational build, launch, and scale and optimize stages.