From a business perspective, technical KPIs – such as application uptime and response rates – are merely a means to an end. The end is the optimization of business processes and opportunities. Technical KPIs are only useful if they serve that goal.
Hence why CIOs and IT organizations often endeavor to translate technical KPIs into data that reflects business outcomes. It’s a practice that has become increasingly easy in the context of modern, cloud-based applications, which offer rich tooling for collecting technical data and mapping it onto business operations.
A challenge facing many IT leaders, though, is that not all IT resources are cloud-based. Many organizations still depend on software that wasn’t built from the ground up for the cloud era. For instance, on-premises Enterprise Resource Planning (ERP) platforms often host much of an organization’s most critical applications and data. Getting technical data from these systems and translating it into insights that business leaders can interpret can be a real challenge.
Fortunately, it’s also a solvable challenge and, indeed, bringing insights from all systems to bear in conversations about business operations is critical for any organization seeking to optimize its processes fully.
The Role of Technical KPIs on Business Decisions
On their own, most technical KPIs are of limited use outside of technical contexts. Tracking latency rates, for instance, can help IT teams identify and mitigate problems. But, this insight doesn’t directly improve business outcomes, like speeding up order-to-cash cycles or invoice processing.
Yet, when IT organizations turn technical data into data business leaders can understand, the information becomes valuable to the organization as a whole. It closes the gap between technical teams and end-users, which, in turn, helps identify opportunities for improving technical systems in ways that directly benefit the business. For instance, knowing that a high latency rate in a finance app delays the book-closing process presents an opportunity to speed up business operations by reducing latency.
The ability to translate technical data to business data can also help justify IT budget requests and gain buy-in for the agendas of IT leaders – a not-insignificant consideration in an era when CIO tenures have, in some cases, been cut short.
The Challenge of Collecting Business Insights from Enterprise Systems
For most modern apps, it’s easy enough to translate technical KPIs to business insights. The applications and their host environments include rich tooling for collecting data and correlating it with business outcomes.
For example, it’s not very difficult to track the total cloud infrastructure costs incurred from hosting an application, then divide that by the number of monthly user logins as a way of mapping hosting costs onto user engagement.
But in other types of environments, gaining insights like these is often harder due to challenges such as:
- Limited metrics availability: Some platforms don’t expose a rich set of technical KPIs at all, let alone make it easy to translate them to business outcomes.
- Lack of native monitoring logic: Similarly, some applications may not include the code necessary to collect relevant technical metrics and align them with business outcomes. And while it’s possible to add this logic, doing so may require the implementation of custom code – which leads to other challenges because systems like SAP no longer fully separate integrated code customization.
- Siloed data: In some environments, it’s difficult to move data between applications. This makes it challenging to correlate distinct metrics in ways that provide insight into their meaning from a business perspective.
- Lack of real-time data streaming: Some platforms only support periodic polling or data pulls, making real-time reporting and analysis impossible.
- Data quality limitations: Although running on-premises software doesn’t necessarily mean that the data it hosts is poor in quality, this is often the case because organizations frequently overlook on-premises resources when adopting modern data governance standards. Low-quality data is difficult to interpret meaningfully.
In short, many enterprise platforms and applications, although still absolutely critical for business operations, were simply not designed for a world where IT leaders face constant pressure to translate technical insights into business insights.
Bridging the Gap Between Traditional Systems and Business Insights
That doesn’t mean that organizations can overlook on-premises or other traditional systems when they leverage technical insights to inform business strategy. On the contrary, to do so would be a huge oversight because many of the most mission-critical IT resources for large enterprises often reside in these environments. Failing to analyze the KPIs they generate dramatically reduces visibility and insights.
Instead, IT leaders must find ways to integrate traditional IT assets into business-oriented KPI strategies.
One way to do this is to modernize platforms fully so that they support the same data collection and analysis techniques that work in other environments. This can certainly be done, but the caveat is that full-scale modernization projects often cost huge sums and take multiple years to complete.
A more strategic approach is to pursue incremental modernization efforts that bring platforms up to speed with modern business needs, but without requiring a full overhaul.
Exactly how to do this depends on the platform in question. To cite one real-world example, I recently worked with a large Fortune 100 enterprise running a major enterprise software platform on-premises. To enhance the organization’s ability to collect relevant technical and business insights from the platform, we lifted-and-shifted it into the cloud, where it became possible to take advantage of cloud-based monitoring and analysis tools.
In a perfect world (or at least, one where time and budget were no consideration), we would have fully modernized the system. But, since the company didn’t have the capacity for that at the time, we did the next-best thing, which was to move the environment into a modern cloud. The apps it hosted continued to use the same architectures as they did on-premises, but the business can now at least take advantage of cloud-native monitoring techniques.
This is an example of the type of incremental modernization that is effective in bridging the gap between traditional systems and the need for accurate, real-time KPIs that are relevant to the business – and it’s the type of change that every enterprise should be pursuing in a world where effective business decisions hinge on having effective data from across all facets of the IT estate, not just those that are cloud-centric.
Published: MSP Today
