Is TOM the New Fad? A Search for the Target Operating Model
Optimizing operating models can noticeably improve efficiency and performance. Why do so many organizations struggle to upgrade their tech stack?
The institutional investment world loves headline-dominating acronyms. Remember BRICs at the turn of the century, VaR in 2008, FAANG around 2016? The latest to make headway within the world of investing over the last couple years? Target Operating Model, or TOM. Interestingly enough, it has nothing to do with a specific investment theme; TOM focuses on the processes and technology that support investments—as well as other industries.
Organizations are realizing that the tech stack underlying investment processes has become chaotic and hard to operate. Target Operating Model is the fancy term that investors use when they look at the mess that is their technology stack and feel they must re-think the process before deciding on the best path forward.
In this article, we’ll spell out what underlies the interest in TOM and the challenges institutional investors face while optimizing their TOM.
Why Use a Target Operation Model?
What’s fueling the interest and the significant amount of dollars thrown at this task? Put simply: Organizations are recognizing that there is room for improvement within their tech stack.
Most institutional investors will nod with familiarity at the following example:
The private equity team buys a data feed to gain transparency into the underlying portfolio companies; the public equity team buys an analytical engine which they must feed manually; the back- and middle-office team rely on an accounting system provided by their custodian, and they use Excel to represent performance for assets held away. Meanwhile you, the CIO, still don’t have a consolidated picture of your investments.
Ring any bells?
How Did Institutional Investors Get Here?
Software suddenly became cheap, and individual users could buy it under the radar (read: without going to RFP). When an investor looks for a solution, the criteria are typically confined to the investor’s own asset class, the investor’s own job function, or worse, a combination of the two—for example, a software to monitor stock lending yield for the long equity book. Mind you, the cheapness we’re referring to is mainly a function of the SaaS delivery method, which has shrunk the providers’ cost base (we don’t have to ship installation CDs anymore and work on costly, bulk releases).
But there’s a more profound reason demonstrating why reviewing TOMs has become important, and it still involves the SaaS nature of software delivery. Consider Figure 1 for example, where data processes are depicted under a non-SaaS versus a SaaS world.
In the Non-SaaS world, content is produced and enriched linearly throughout the various steps in a process. For example, in the commonly used reporting workflow, someone has access to the data, extracts the data, and sends it along to someone else, who in turn will calculate performance figures in Excel. Then, the Excel is sent to someone else for a final review and proper rendering in PowerPoint. Finally, an entirely different individual presents this to the board. The shortcomings of this process are obvious, yet many of us have careers filled with similar operating data flows.
In a SaaS world, content is centralized in the cloud and is constantly being changed by most of the people in your firm. Your middle office manager may be updating the NAV of your largest private equity investment while you are preparing the board reporting deck. While the spider-shaped architecture underlying a SaaS software stack has its advantages (e.g., you don’t have to wait for someone else to give you inputs for your steps to be performed—it’s not an assembly line), it creates challenges too. For example, you need to define a well thought out hierarchy of user permissions to ensure your colleagues will only interact with the data under their jurisdiction and at the appropriate times. Relatively speaking, this should be an easy problem to solve.
Why Isn’t This an Easy Problem to Solve?
The more complex problem is depicted in the chart below. Figure 1 is unfortunately a bit oversimplified. Reality looks more like Figure 2, where investors have inherited multiple SaaS systems by virtue of the individual vendor acquisitions led by members across their own team.
Now you have systems which can potentially hold several sources of truth, but all of which are incompatible with each other. Some investors rush to solve the problem by programming ‘connectors’ between the systems, as depicted in Figure 3.
But this only creates more problems!
The number of connections needed among ‘n’ systems is equal to n*(n-1) /2
First, it’s a massive drain on your internal resources. Your team will be bogged down with the creation and maintenance of interfaces between systems that were designed with different data models in mind. It’s also a tall order to require vendors to talk to each other. Despite the loud noises made on matters such as API interconnectivity and interoperability, you are usually left all alone with the task of piecing it all together.
Second, complexity will grow more than linearity. Refer back to Figure 3 as an example. You need 6 binary connectors if you operate 4 systems, as depicted by the arrows in the Figure. If you had 5 systems, you’d need 10. For 6, 7, 8, and 9 systems you’d need 15, 21, 28, and 36 connectors respectively. More generally, the number of connections needed among ‘n’ systems is equal to n*(n-1) /2 which means that, in mathematical parlance, the number of connections grows with the power of n.
Simplifying With an Enterprise Data Management System
To simplify the mess in Figure 3, investors often find themselves building something like Figure 4, where a central Enterprise Data Management System (EDM) is acting as a central hub connecting several SaaS systems.
An EDM is supposed to contain the source of truth; the SaaS softwares reference it to perform various workflows.
Setups like those in Figure 4 strain the practicality for most institutions. Investors often start to wonder whether it makes sense for them to build and own an architecture of such scale and complexity. The linear process depicted in Figure 1 was perhaps messy and inefficient, but at least those involved still felt connected with their primary responsibility of making investment decisions. Figure 4 is many levels removed from investment decisions—the team’s mental load starts to be filled by asynchronous scheduling of refresh tasks and data mappings rather than interpreting said data. It begs the question, “Even if I can....should I be doing this?”
Back to Square One
A setup like Figure 4 could be more easily solved if there was a SaaS provider who could play a centralized role like on the left side of Figure 5. For that to happen, said SaaS provider should offer a suite of functionality capable of adding value across numerous workflows. Our team at SEI Novus developed the Ledger functionality for this very reason; Ledger bridges the traditional gap between accounting and analytical systems, a permanent cause of operational friction.
While the ideal SaaS provider was a pink unicorn up until a few years back (when new platforms were popping up like mushrooms on a rainy day), things today are different. A flurry of acquisitions and consolidations has led to fewer providers with more resources to tackle several workflows (see here for a list of recent transactions).
Regardless of whether one opts for a setup like the one on the left or the right in the figure below, the reality is that SaaS architectures have led investors to rethink their processes, exchanging linearity for interactivity.
Which Is Right for You?
Many investors are understandably overwhelmed when it comes to identifying their organization’s TOM, leading them to enlist the help of investment technology consultants. In Part Two of this blog post, we will discuss best practices for investment teams conducting a search for portfolio data management and analytics technology.
If you’re interested in learning more about how SEI Novus is bridging gaps between accounting and analytics, check out our ebook discussing shadow accounting and our latest contribution to the workflow: Ledger.
Information provided by SEI through its affiliates and subsidiaries. This information is for educational purposes only and should not be considered investment advice. The strategies discussed herein are complex and are not suitable for all investors.