How the Tech Knowledge Curve Is Flattening
Technological advancements are enabling users at all levels to more easily engage with and benefit from sophisticated tools. And accessibility is transforming technology from a specialized skill to a more user-friendly experience. By adopting modern, intuitive solutions that simplify complex tasks, organizations can enhance efficiency, improve decision-making, and mitigate risks, all while fostering greater innovation and productivity.
Clayton M. Christensen wrote in the Fall 1992 issue of Production and Operations Management about the technology S-curve being a useful framework when companies consider the change from older to newer technologies. He writes, “…[Technology] is specific to particular products or processes. As such, it is distinct from knowledge, whose value may not be unique to specific products or processes.” 1
Fast-forward to January 9, 2007, when Steve Jobs and Apple announced a technological change that, per the definition from Mr. Christensen, vastly improved the measured level of performance. The transformational change most of us now hold in our pockets and quite literally at our fingertips throughout the day was the introduction of the iPhone.
Almost two decades after Apple’s ubiquitous innovation, technology has grown to be an essential part of everyday life, enabling us to handle basic tasks with ease. Accessibility empowers individuals through well-designed technology without the need to be a tech expert. Businesses, meanwhile, benefit from impressively sophisticated tools that they put directly into the hands of their employees to transform operations and allow their teams to achieve more with fewer resources and reduced risk.
Flattening the tech knowledge curve to empower the customer
A colleague once told me they met with the chief investment officer at a life and annuity insurance carrier during a presentation and technical demonstration. Afterwards, the CIO appreciated the demonstration and understood the value but was having a difficult time embracing the technology and knowledge needed to use what had been demonstrated.
Despite my colleague’s assurances that the rest of the meeting was a great success, I could feel the opportunity slip away from our fingers. In that moment, it was clear that not only did we need to give technology to the CIO to empower them, but the tools needed to be user-friendly so that the CIO could play around with the tools and use them to derive value. To be truly user-friendly, for example, to let users rename data columns or modify chart properties, the tool needed an intuitive interface that didn’t require direct manipulation of the underlying database structures or code changes across multiple programming languages.
For a CIO balancing multiple responsibilities over the course of a day and reporting quarter, how they use technology can be remarkably different than their counterpart in investments, or middle- and back-office roles who might appreciate having more advanced capabilities.
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The evolution of technology accessibility
It didn’t seem that long ago that someone wishing to learn the ABCs of programming might peruse the compendium of authoritative programming books at the library or at a brick-and-mortar bookstore. “The C Programming Language” by Brian W. Kernighan and Dennis M. Ritchie or Bjarne Stroustrup’s, “The C++ Programming Language” were widely popular. Fast-forward a few years and now rather than just a programming book, someone looking to not only understand how to program but where to apply it can now leverage self-paced learning technology, such as programming courses, online compilers, or tutorials. In other words, technology for learning technology and one small step for making that arduous uphill tech curve climb has become a little less imposing.
Today’s firms are poised to transform not only their back office, but all business functions by leveraging advanced capabilities that empower people to do more with less and with less risk. A vast menu of technologies allows for unique partnerships and interconnectivity. Solutions built on legacy systems with on-premise infrastructure and complex interfaces will struggle to meet modern expectations for cloud connectivity, low-code development, and agile front-end design. The result: an uphill knowledge curve that frustrates users and customers and potentially stunts expectations for growth.
Blueprinting how technology and systems can partner with each other to solve client challenges is evidenced by an article from SPHERE CEO Rita Gurevich in Forbes Technology Council:
“Tech companies often have complementary products that will solve end users’ pain points. Think about the integrations between popular SaaS products, such as Zendesk Support and Slack. When used together, Zendesk Support and Slack enable teams to deliver impactful customer support experiences.”2
Beyond the experience alone, which one would hopefully expect to be favorable, the effort to learn new technologies, new software, and modernized solutions does not have to be an arduous journey.
Key technologies, trends, and data evolution driving democratization
Software-as-a-service continues its multiple journeys around growth, adoption, and modernization. In many cases, part of modernization lies with scaling computing capacity via cloud capabilities meaning that teams can rely less on on-prem software and solutions. Teams responsible for the operations and data management supporting front-office decision making are reaping the benefits firsthand.
Most recently, there’s a drive toward proactive technology. In other words, don’t show what happened or what is already known; show the unknown or foreboding issues and what can be done to mitigate problems before they rear their ugly head from any number of lenses of risk: operational, due diligence, business continuity, political, liquidity, credit, currency, market and asset-specific, to name a few.
Externally in the marketplace, mergers and acquisitions activity in the buy-side segment yields technology discussions across front-, middle-, and back-office departments. PWC notes in their 2025 outlook, “We expect that dealmaking activity will include acquisitions focused on both revenue and margin growth, with the intent of accessing new markets, enhancing capabilities and improving operational efficiencies. Given the significant costs of investing in technology and AI, the advantage goes to larger companies that have the investment capacity.”3 For example, what if Manager A wants to increase AUM 15% through a strategic acquisition of a specialized asset manager and both the acquirer and acquiree have completely different tech stacks? The immediate follow-up question might be: does Manager A adopt the tech stack of Manager B; does Manager B adopt the tech stack of Manager A; or is there a balancing act to designing the ideal state? There are several considerations at stake as part of that over-arching question: cost, time, personnel, knowledge [like Mr. Christensen notes], legal contracts, underlying data/models, client communications/disclosures, and more.
How can we apply a decision-making framework specifically to hedge fund and institutional asset managers? The investment data backing billions in global AUM comes in all shapes and sizes, from structured data like transactions, valuations, security mastering and accounting to unstructured data like borrower behaviors, supply chains, and GP/LP statements.
The data pyramid behind the flattening tech curve
With volumes of potentially disparate data, it becomes incredibly challenging to source, scrub, and finalize data to both internal and external consumers. People and business groups that are part of these processes leverage various technology solutions, coupled with their own familiarity, to support the modernization of front-, middle- and back-office departments with automation, high data quality, and high-risk mitigation.
From a technical perspective, if we join the technology, user persona, and the data itself, we might organize the framework into a data pyramid, sliced in three components, with each component more important than the next and its output only as reliable and meaningful as the layer beneath it.
Top of the pyramid
At the top of this data pyramid is the finished product: externally (or internally) facing output, ranging from mutual fund factsheets, GP or LP statements, board reports, investor presentations, regulatory reporting, or performance/risk metrics of certain strategies, managers or individual assets. The output can be and should be readily available for audiences and accessible through easy-to-use self-service front-end interfaces, dashboards, or readily queried via low-or-no-code platforms. As was discussed earlier, the range of technical knowledge potentially varies and each consumer of the data should be freely empower to digest the data in a manner comfortable to them.
Middle of the pyramid
The middle of the pyramid is the data layer in its intermediate data form: beyond sourcing it is scrubbed, finalized, and joined with other data components to support the top reporting. Heavy-quality assurances, reconciliations, remediations and large transformations occur here to organize the data and continue its journey up the data pyramid. This all constitutes a rich meta data experience buttressed by bitemporal database models with capabilities for ensuring transparency, timeliness, auditability, and data lineage are intact.
Bottom of the pyramid
At the foundation of our data structure lies raw data in its most basic form. This can range from clean, well-organized columnar tables to completely unstructured, chaotic information. This layer, often referred to as a data lake, typically contains data from multiple sources, with varying update frequencies and formats. While it may seem disorganized, this raw data is crucial for higher-level processes in this data pyramid. It serves as the starting point for data synthesis and concordance operations, forming the essential base for all subsequent data transformations and analyses.
In between each layer of the pyramid are the technologies that transform the data on its journey. At each layer of the data hierarchy, we observe a trend toward simplification. The technology and expertise required to manage and interpret data are becoming more accessible, allowing various software applications and systems to easily navigate the data transformation processes.
The technology that supports the top of the pyramid can be used by any of the front-, middle,- and back-office business units. However, for top-line reporting in our example, it may be assumed that top-line senior management are telling the story with the data they have. In this case, organizations may consider providing user-friendly technology to empower them. In-between the triangle base and the middle layer is probably the most important part of the technology stack because it is where that powerful technology starts to shape all various data points into consumable and reasonable output.
The future of a flattened tech knowledge curve
The result of a flattened tech curve, as my colleague notes in her blog, Break Your Dependency on a Fickle and Expensive Talent Pipeline, “[…] A robust data platform can automate processes, allowing operators to pull reports and perform tasks through intuitive user interfaces rather than relying on complex coding or database queries. This shift toward self-serviceability not only minimizes the need for extensive technical proficiency but also opens opportunities for employees to focus on higher-value tasks, driving productivity and innovation within the organization.”
Institutions that move to transform not only the data management processes, but also adapt to flattening the tech and knowledge curves, will be able to manage regulatory risk, enhance operational efficiency, swiftly meet investor demands, and achieve better returns.
Sources:
1. Exploring the limits of the technology S‐curve. Part I: Component technologies." Production and Operations Management 1.4 (1992): 334-357, Christensen, Clayton M.
2. By Forging Strategic Partnerships, Tech Companies Can Add Greater Customer Value—And Increase Revenue, Forbes, April 6, 2023.
3. 2025 Outlook Global M&A Industry Trends, PwC, January 28, 2025.
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