Digital transformation starts with a digital experience platform

How Iron Mountain enables the digital transformation and AI journey

Situation analysis

Digital transformation is a term that has existed for some time. It is also a practice (and trend) that is evergreen. In fact, technology has been used to drive better business outcomes for decades. What’s new is the focus on data feeding artificial intelligence (AI) models and analytics engines as key enablers of automated business processes.

The most recent wave of digital transformation has seen a second trend that has caused many organizations to reconsider efforts — generative AI (GAI). The use of foundational models and large language models (LLMs) to drive all facets of business operations has become essential. As a result, many organizations have rescoped transformation efforts to optimize deployments.

With such a focus on data-driven outcomes, the expectations across an organization are understandably high. Faster, better, and higher quality are not just platitudes; they are key metrics that determine success, regardless of whether an organization delivers a new product to the market or provides public services.

Indeed, digital transformation is the monetization of data.

The challenges many enterprises face when undergoing digital transformation can be mapped across four vectors — culture (people), operational (processes, procedures), technology, and data. Each vector is a critical element to the success of any transformational effort.

This research brief explores the tensions organizations face across these success factors while driving toward an AI-enabled, digitally transformed state. Further, this paper introduces the Iron Mountain InSight Digital Experience Platform (DXP) and explains how this SaaS-based platform is critical to the digital transformation process.

The potential of the AI wave is significant

Though digital transformation has been discussed as a relatively new trend, the underlying concept is not. The idea of transforming operations through technology can be traced back decades with business process automation (BPA). And over the different waves of transformation — from BPA to cloud to hybrid cloud — the current AI wave has incredible potential.

Not every technology trend has lived up to its full potential. Any seasoned IT or business executive can point to an experience where a technology investment in software or some service fell far short of its promise. These lived experiences can cause executives to be hesitant to embrace any trend that promises differentiation and advantages in an ever-changing market.

In this latest wave of digital transformation, the AI wave, Moor Insights & Strategy (MI&S), has heard cautious optimism from business and IT executives. Optimism about the potential and caution about the stakes and the impact of a failed initiative.

Indeed, the statistics justify the hopes and concerns of executives. According to global consulting firm Boston Consulting Group (BCG), approximately 70% of digital transformation projects have failed to achieve their potential. While 30% of surveyed executives enjoyed successful implementation, 44% realized only some value, and a shocking 26% considered their efforts a failure.

However, on average, those who did succeed realized an 82% increase in corporate capabilities and 66% more value relative to those who failed in their efforts. Further, on average, these digital winners achieved 1.8 times earnings growth over digital laggards — and more than double the growth in total enterprise value.

One of the key pillars to success is the deployment of business-led modular technology and data platforms. One of the top five challenges IT and business executives cite regarding transformation and modernization projects is making the right choices among disruptive technologies. Surprisingly, 93% of responding companies articulated struggling with this critical decision.

The above is not said to dissuade business and IT executives from embarking on digitization efforts. The benefits of successful digital transformation are both tangible and measurable. Articulating these challenges stresses the importance of proper scoping, planning, execution, and measuring such an endeavor.

Successful outcomes require successful planning

Proper planning increases the chances of successful project outcomes. Conversely, poorly scoped and planned digital transformation projects are guaranteed to fail by any measurement — be it an earned value, time to value, or customer satisfaction metric. This is especially true for heavily regulated, process-driven organizations or those with data that originates from multiple sources (physical, digital) in different formats and types.

There are many considerations to take into account to properly manage transformation projects. From the perspective of data readiness and information lifecycle management, there are specific “must haves” for any organization, including:

• Executive stakeholder buy-in and project representation that spans every business unit and every operations function.
• Organization-wide accounting of all the data that resides across the enterprise, along with every source of data — from applications to IoT devices to the physical assets that populate the closets, offices, warehouses, and storage facilities that house these assets.
• An agreed-upon process for transforming the raw information within these artifacts into data. That is, how data is collected, cleansed, tagged, and staged for use in AI or other methods of automation. In other words, unified asset management (UAM).

A natural output of this process should be a requirement to identify a platform that can manage this process of ingesting data and information across the enterprise — both physical and digital, regardless of its origination or format.

MI&S recently explored the opportunities and challenges of AI-driven digital transformation in the enterprise. This research report outlines strategies for success that we believe can deliver exponential value to traditional transformation efforts. That research can be reviewed here.

Digital transformation is built on a digital platform

Data is the fuel that drives digital transformation and modernization in the enterprise. Generally speaking, the more data available, the better. However, data must have relevance to drive effective change — be it in analytics platforms or GAI models that help organizations dramatically drive down the time to value or production.

Data relevance begins with a comprehensive information lifecycle management (ILM) framework. One in which all sources (and potential sources) of data are accounted for. These could be analog readings from sensors in a decades-old power plant. Or they could be audio transcriptions of depositions at a law firm or film reels and recordings from a media production company. The sources could be X-ray films, medical records, and even hospital patients themselves. In today’s digitized world, everything and everybody is a potential source of data.

In the previously referenced MI&S research, unified asset management (UAM) is identified as a critical element of the ILM framework. UAM is the process by which an organization effectively locates all digital and physical assets and prepares them for use in workloads that drive automation. The cleansed, tagged data from this UAM process feeds the previously mentioned modular digital platform that drives organizational efficiency and automation.

The ideal modular digital platform can ingest, transform, and manage data across its lifecycle for use in applications across the organization and with external partners. It can also secure, store, govern, and eventually archive or delete data in an auditable manner.