Digital Transformation and IT Transformation: The Questions Behind the Conundrum

Dennis Drogseth

EMA has just completed some new research on "Digital" and "IT Transformation." Our goal was to discover what the truth really is surrounding these critical (and sometimes overused) terms. In order to optimize the depth and value of this unique research, for the first time ever EMA partnered with the IT Transformation Institute.

We will be delivering a webinar sharing some of the highlights of this research on September 30.

We embedded a simple definition within our questionnaire, just to make sure our respondents were on the same "proverbial" page. So we defined "digital transformation" as directed at optimizing business or organizational effectiveness via digital and IT services. And "IT transformation" as an initiative focused on optimizing IT performance for business or organizational needs and outcomes. While the two terms do seem like hand-and-glove fits (and should be), the recent buzz around digital transformation has set it apart in the minds of many.

We looked globally across North America, Europe and Asia Pacific (APAC) with more than 300 respondents, about 30% of whom were business leaders and the rest largely came from the IT executive community. We wanted to investigate how digital and IT transformation complemented each other (or didn't), how business leaders and IT leaders viewed this critical arena — where were the views similar and where did they differ? And we wanted to investigate geographic differences, as well.

In turn, we wanted to project this "transformational heat map" on what we believed to be a number of transformational prerequisites. These included:

■ Organization and politics: Who's leading the charge in digital transformation? In IT transformation? We asked both in terms of role and organizational association, and in terms of both drivers and ongoing oversight.

■ Technologies: Were technology investments drivers, supporting players, or non-central to transformation? We examined this question in detail from operations to ITSM; from analytics to automation to service mapping; from customer experience, to security, to financial and IT governance; from revenue generation and brand awareness to business process impacts.

■ Metrics: How did both IT and digital transformation efforts measure success? What were the predominant preferred metrics in terms of operational performance, financial optimization and business outcomes?

■ Cloud and DevOps: How are these ground-shaping foundations of the digital age affecting digital and IT transformation? How and where are they integrated into transformational efforts?

■ Processes and Best Practices: To what degree do industry best practices apply to transformational efforts? And what are the preferred best practices for digital transformation in particular?

■ Transformational Partners: Where are transformational leaders seeking to partner and how successful are those partnerships, whether from IT management software vendors, systems integrators, business consultants, or transformational specialists?

■ Success Factors: What is the magic formula (in terms of all of the above and more) for transformational success? Is it the same for IT and for digital transformation? And how do business stakeholders and IT stakeholders view success rates, obstacles, and priorities for going forward?

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Digital Transformation and IT Transformation: The Questions Behind the Conundrum

Dennis Drogseth

EMA has just completed some new research on "Digital" and "IT Transformation." Our goal was to discover what the truth really is surrounding these critical (and sometimes overused) terms. In order to optimize the depth and value of this unique research, for the first time ever EMA partnered with the IT Transformation Institute.

We will be delivering a webinar sharing some of the highlights of this research on September 30.

We embedded a simple definition within our questionnaire, just to make sure our respondents were on the same "proverbial" page. So we defined "digital transformation" as directed at optimizing business or organizational effectiveness via digital and IT services. And "IT transformation" as an initiative focused on optimizing IT performance for business or organizational needs and outcomes. While the two terms do seem like hand-and-glove fits (and should be), the recent buzz around digital transformation has set it apart in the minds of many.

We looked globally across North America, Europe and Asia Pacific (APAC) with more than 300 respondents, about 30% of whom were business leaders and the rest largely came from the IT executive community. We wanted to investigate how digital and IT transformation complemented each other (or didn't), how business leaders and IT leaders viewed this critical arena — where were the views similar and where did they differ? And we wanted to investigate geographic differences, as well.

In turn, we wanted to project this "transformational heat map" on what we believed to be a number of transformational prerequisites. These included:

■ Organization and politics: Who's leading the charge in digital transformation? In IT transformation? We asked both in terms of role and organizational association, and in terms of both drivers and ongoing oversight.

■ Technologies: Were technology investments drivers, supporting players, or non-central to transformation? We examined this question in detail from operations to ITSM; from analytics to automation to service mapping; from customer experience, to security, to financial and IT governance; from revenue generation and brand awareness to business process impacts.

■ Metrics: How did both IT and digital transformation efforts measure success? What were the predominant preferred metrics in terms of operational performance, financial optimization and business outcomes?

■ Cloud and DevOps: How are these ground-shaping foundations of the digital age affecting digital and IT transformation? How and where are they integrated into transformational efforts?

■ Processes and Best Practices: To what degree do industry best practices apply to transformational efforts? And what are the preferred best practices for digital transformation in particular?

■ Transformational Partners: Where are transformational leaders seeking to partner and how successful are those partnerships, whether from IT management software vendors, systems integrators, business consultants, or transformational specialists?

■ Success Factors: What is the magic formula (in terms of all of the above and more) for transformational success? Is it the same for IT and for digital transformation? And how do business stakeholders and IT stakeholders view success rates, obstacles, and priorities for going forward?

Related Links

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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