Effective digital transformation is essential for continued operations in the remote, multi-site, digitally dependent post-COVID world we live in today. Even before the pandemic, IDC predicted that worldwide spending on digital transformation would reach $2.3 trillion by 2023 – and that number is only increasing as time goes on.  

And at the core of any effective digital transformation effort is good data structure, which requires effective data governance and data strategy. Here’s everything you need to know about data structure, common roadblocks to attaining clear data structure and how to get it right.  


What is Digital Transformation and Why is it Inescapable Today?  

What is Digital Transformation?  

Accruent - Blog Post - Why Good Data Structure is Critical for Your Digital Transformation

Digital transformation is the process of moving operations, tools and processes from a traditionally offline or siloed environment to a digital environment with the goal of offering more understanding, increased alignment and greater business value. Ultimately, digital transformation can:  

  • Create insights and data points that will measurably improve the customer experience with the intent of increasing sales, loyalty and brand advocacy.
  •  Enable and unlock intelligence across an organization to highlight how and where you can take time and cost out of the process, with the intent of reducing waste and lowering the cost of doing business.

And it’s shown to work: according to McKinsey, data-driven organizations are 23 times more likely to acquire customers and 19 times more likely to be profitable as a result. What’s more, Forrester notes that insights-driven businesses are growing at an average of more than 30% each year and they were expected to take $1.8 trillion annually from their less-informed peers starting in 2020. We suspect that, in the wake of the pandemic, the actual number was even higher.  

Yet, despite the years of reports and insight into its importance, for years digital transformation has been seen as a “nice-to-have” for businesses. This is particularly true of businesses in asset-intensive industries, many of whom have been hesitant to move to the cloud or overhaul their technological infrastructure. Instead, these organizations have continued to favor their legacy systems or take a patchwork approach to modernizing their systems, processes and operations.  

And for a while, that mindset worked: legacy on-premise systems functioned well enough and offered security, existing processes felt comfortable, and – by ignoring the call to digitize – these organizations sidestepped any risk associated with investing in unproven, expensive new technology.  

COVID-19 Has Accelerated Digital Transformation Initiatives  

That all changed with COVID-19. Suddenly, organizations needed to run remote operations, facilitate social distancing, remain compliant and manage assets in multiple sites and locations to stay alive – which means that effective digital transformation became vital to staying afloat.  

Pretty much all organizations responded by erecting a decent technological infrastructure as quickly as possible. For asset-intensive organizations, this included cloud-based tools, mobile features and a more digitized data repository.  

But it’s been an uphill battle, particularly for organizations that didn’t have the most important part figured out: their data integrity and, more broadly, their data and asset structure.  

Here’s why data structure is the key to effective, long-lasting digital transformation – and how to get it right for long-term operations.  


Digital Transformation Starts with Strong Data Structure  

The Broader Context: Data Here, Data There, Data Everywhere  

At the heart of data structure is the data itself. After all, the goal of digital transformation is to align all your data to inform and transform operations – and that’s only possible if the tools, data strategy, data management, processes and analytics are all connected and properly aligned. All of that, in turn, is only possible if your data is well structured and well governed.  

And there’s a lot of data to get in order. Today’s asset-intensive organizations live under mountains of data coming in from various sources, including:  

  • Work orders and related documentation
  • Asset data, including historical maintenance records, related documentation
  • MRO inventory data  
  • Data coming in from various other mission-critical tools – things like an electronic document management system (EDMS) or a computerized maintenance management system (CMMS)
  • Other enterprise business tools (i.e. manufacturing, quality, accounting and marketing systems)
  • Pen and paper data
  • Data that lives in the heads of skilled employees, otherwise known as tribal knowledge

Unstructured Data is a Huge Pain Point for Many Businesses

The problem? For most organizations, this data is siloed, fragmented and unstructured. By unstructured, here, we mean that the data is inaccessible and/or incompatible across locations and departments. There are many reasons for this:  

  • Knowledge is often spread by word-of-mouth from tenured, skilled employees with no documentation, standardization or information governance.  
  • Platforms and systems are purchased independently of one another and do not properly integrate. Accounting data, for example, lives separately from asset data, so big picture financial insight across departments isn’t possible.  
  • Organizations have a mix of pen-and-paper, on-prem and cloud-based SaaS tools. This inconsistency leads to incomplete information, poor document version control and more.  
  • Tech and strategies are implemented without universal buy-in, cultural shifts, training, and adoption from technicians and other primary users.
  • Processes and documentation are not standardized or universally available, and good data governance is not in play.  

Bad Data Leads to Poor Asset Structure  

For asset-intensive organizations, this snowballs into poor asset structure. Think about it: for each asset, an organization should have a repository of accessible related information, including blueprints, maintenance history, parts information, compliance data and warranty information. This is the information that’s required not only to maintain assets but also to allocate resources, make capital plans and justify an asset’s place on the balance sheet.  

But if, for example, inventory data is siloed or data isn’t inputted uniformly, that information is not easily available. More broadly, there won't be comprehensive asset insights or automations that can streamline maintenance tasks or drive big-picture money-saving decisions.  

This can ultimately lead to:

  • Incomplete information
  • Out-of-date information
  • Failed digital transformation efforts
  • Complicated system validations
  • Difficulty maintaining continuous compliance
  • A higher total cost of ownership for major
  • Cybersecurity concerns

This lack of structured data also guarantees that broader digital transformation efforts and more advanced tools – those powered by AI and the Industrial Internet of Things (IIoT) – will fail. AI, IoT, and data-driven automations and insights are only possible when they’re powered by rich, expansive, comprehensive data. That’s the only way they exist. And such applications are at the heart of any comprehensive digital transformation efforts. Bad data, then, means no advanced tools or transformation efforts will ever yield the results you’re looking for.

It’s imperative, then, that you get your data structure right.  


How to Improve Your Data and Asset Structure  

So how do you sidestep these concerns and get your data structure right? There are three things to consider here: the data itself, your broader structure and ongoing data governance.  

1. Get Your Data Itself in Order  

First, you need to cleanse, review and consolidate the mission-critical data that lives in your various business systems into one coherent, standardized system. This requires that you answer key questions about your data itself, including:  

  • What data do you have?  
  • What data are you collecting regularly?  
  • How is that data used/how does it inform broader strategies?  
  • What does that data tell you about your customers, about your KPIs, about your business?  
  • What data are you missing? Where are there currently gaps in your data needs?  
  • What data do you want that you’re not already collecting?  

You need to know what you have before you can understand what to do with it and starting at the data layer will help you truly understand your online and offline information so you can glean actionable insights and make any technological investment worth your while.  

2. Get That Data Organized Using the Right Tools  

Next, you need to migrate that data, error-free, into a well-structured tool that meets your business’ needs. The right tool can help with data structure, data governance and the generation of data insight. More specifically, it will help:  

  • Ensure your data initiatives are standardized and repeatable.  
  • Automate manual processes of analytics, which become difficult to manage as companies gather more data. AI automations can also increase your team’s internal bandwidth while eliminating human error and biases.  
  • Ensure that your organization can easily manage the flow, quality and governance of your information, which is key to any effective insights or transformation efforts.
  • Your team continue to generate high-quality, actionable data and insights at a fast pace.

So, what does that tool – or suite of tools – look like? It will vary depending on your industry, data sources and broader goals, but the right tool will facilitate a wholistic data understanding and strategical approach. This will require technology that can:

  • Collect and orchestrate your data.
  • Use that data intelligently to inform your team and your broader strategies.
  • Combine tech and data insight to deliver superior, actionable and data-driven business insights.

What Are the Right Tools for Asset-Intensive Organizations?  

Accruent - Blog Post - Why Good Data Structure is Critical for Your Digital Transformation

For organizations in asset-intensive industries, two tools that check all these boxes are a computerized maintenance management system (CMMS) and an electronic document management system (EDMS) - and they should be used together if you want to make the most out of each tool and truly modernize your operations.  

If you run an asset-heavy organization, you already likely use a CMMS or an EAM. A CMMS is an enterprise business system used to manage work orders, track inventory parts and execute preventive maintenance. The goal? To digitize and automate maintenance operations to better deploy preventive maintenance strategies, develop better maintenance practices, stay organized, and ultimately save time and money. And a CMMS already has a certain degree of document management and data structuring built in, so you may think that you have the structural foundation you need to modernize with a CMMS alone.  

But you don’t. That built-in data structuring isn’t robust, and it won’t be enough as you try to take your next steps to modernize. Moving into Industry 4.0 and truly undergoing digital transformation in the manufacturing industry means deploying things like machine learning, sensors, and the digital twin to move toward a more predictive state. These innovations require both next-level, advanced data structure and, more broadly, a truly connected technological environment. The document management functionality that exists within a CMMS is simply not powerful enough to structure and connect your data in this way – which is where the EDMS comes into play.  

Using an EDMS along with your CMMS provides that advanced, nuanced, comprehensive data structure and moves you one step closer to that predictive, truly connective digitally transformed environment. In other words, it gives your CMMS the horsepower it needs to connect your systems, demystify your data and propel your business into Industry 4.0.  

And this is a technological stack that far too few businesses are currently taking into consideration.  

3. Get Your Team on Board  

Finally, you need to make sure your team is on board. Enterprise-wide buy-in and clear, consistent governance are essential to the long-term success of any tool or broader digital transformation initiative. To this end, it’s important that you:  

  • Have conversations to align objectives and make sure that your team sees the business value of the tool you choose.  
  • Make your broader team responsible for key metrics. This will help foster a culture of cooperation.  
  • Share your broader roadmap to transformation with your team. Big initiatives take time, and the more you share about your objectives, goals, drivers and timetables, the less friction you’re likely to encounter.  
  • Establish guidelines on how interactions with various departments within your organization should be defined, maintained and scaled to achieve business goals.  
  • Establish processes and procedures needed to manage data and solution life cycles in your company, including the roles and responsibilities of each team member.  

This will not only help you achieve digital transformation, but also increase the utility of your data, support compliance efforts, strengthen your company culture and increase your technological agility over time. And if there’s one thing any modern company needs, it’s the ability to remain agile and flexible in the face of adversity.  


Final Thoughts  

The world is moving fast and companies that digitally transform their businesses and modernize their operations will lead the pack and outpace their competition, particularly in a post-COVID world. But getting this right requires sound data structure and asset structure – so that’s where you must start.  

Learn more about how to optimize your operations and lead your industry in our webinar Operational Excellence: What Is It and How Can You Achieve It?