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ITFM & TBM Program Maturity: Technology and Data

Jul 22, 2020 | By Wil Cleaveland

This blog is the third in a series of posts regarding ITFM & TBM Program Maturity. Get the full guide.

Maturity Factor #3: Technology

Without a doubt, the biggest obstacle to higher degrees of ITFM/TBM maturity is a lack of robust, purpose-built technology to support the function. Despite making considerable investments in Project, Asset, and Contract Management platforms, IT Leaders have historically opted to rely on Excel — vs. a dedicated ITFM/TBM solution — for as long as possible.

The problem with traditional spreadsheets isn’t that they’re totally unfit for ITFM/TBM; the problem is that they simply aren’t scalable.

For smaller organizations, Excel may work just fine – albeit with fewer capabilities and more manual effort. But in the context of a larger organization, the weaknesses of Excel become very apparent: sluggish performance, overwhelming complexity, and human error to name a few.

To reach higher levels of maturity, organizations must have the technological means to:

1) Model costs across a complete catalog of defined, business-facing IT services.
2) Connect directly with source systems for automated data collection and manipulation.
3) Provide self-serve persona-based reports and monthly bill of IT to consumers.

In other words, the viability of using spreadsheets for ITFM/TBM depends on your organization’s size and maturity goals.

For example…

• If you’re managing less than $30~ million in annual IT spend and you’re only interested in low-maturity outcomes like service cost transparency, then a dedicated solution could be overkill.
• However, if annual IT spend exceeds $30~ million or you’re seeking high-maturity outcomes like showback/chargeback, then a dedicated solution is easily justified.


Maturity Factor #4: Data

Organizations frequently cite problems with data quality, completeness, and availability as the main hurdle to launching a new ITFM/TBM program. However, these fears are often unfounded. ITFM/TBM programs rarely have the luxury of perfect data (or even great data) on day one, and it’s in no way a prerequisite.

In truth, pursuing perfect data before ITFM/TBM is a catch 22. Because the best way to improve bad data is by taking the plunge and creating an impetus to fix it – for the simple reason that you don’t know what you’re missing until you understand what you need. Plus, there’s less incentive to get things right when there’s no accountability and dollars aren’t at stake.

The structure provided by an ITFM/TBM program makes the process of boosting data quality far more guided and intentional. And nearly all organizations can use what data they do have (along with quality proxies) to produce usable analysis from day one – laying the foundation to iterate and improve over time.

Furthermore, Data maturity is measured with these three markers:

• Understanding and availability of required data
• Quality and completeness
• Collection, normalization, and storage

Rudimentary organizations are essentially starting from “ground zero” when it comes to data. They have no concept of what data they need, its quality and completeness, or how to collect it. Foundational organizations have started attempting to tackle those three problems by identifying the correct systems of record and interrogating them thoroughly.

Foundational organizations’ data may still be problematic and their plans for collection/validation might not be refined; but the important thing is that the organization knows what data is needed, where it resides, and how it can be improved.

After an organization has achieved a Foundational level of maturity by identifying the correct systems of record for required data and beginning initial quality improvements, the next step to reach a Sustaining level of maturity is to simply keep addressing quality and completeness issues even further. Thankfully, the process of standing up an IT service cost model often aids data improvement efforts significantly — by illuminating the biggest gaps and areas of inconsistency.

Maturing a program from a Sustaining to Progressive level of maturity is primarily about how data is collected and stored. Sustaining programs may have a firm understanding of their data, but they still face many issues when it comes to collection and normalization.

To achieve the final levels of maturity, an organization must remedy those problems by establishing one source of truth and automating all data collection and validation, usually with the help of a dedicated ITFM/TBM solution.


Read the full guide to learn how to choose a partner that understands the maturity journey.

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