The first paper in our ‘Performance is Data’ series emphasized the significance of data management in performance. In this second outing, we pivot to performance transformations. Specifically, we present the business drivers that propel the operational overhaul, discern why a large proportion of transformations fail, and present a new approach with its differentiating aspects.

What are the key drivers of transformation today?

Successful transformations balance expansion with efficiency.

A combination of macroeconomic, industry, and firm-specific factors drive large-scale transformations. New market or asset class expansion, M&A activity, or taking advantage of modern technology capabilities propel firms to fund new initiatives. While services could expand, this also presents an opportunity to consolidate and reorganize operations and systems.

As firms embark on a change initiative, they encounter redundant applications, fragmented architectures, and overlapping processes. This significantly inhibits the cost-of-service delivery. Foray into private markets require new calculation capabilities and sophisticated analytics.

Not all of these may have to be handled in-house. Asset servicing providers and market data platforms have extended their offerings. Front-to-back outsourcing platforms have emerged, allowing asset management firms to focus on their core competence. Some services could scale by function, while others could redact due to outsourcing. Market data-driven platforms reduce the burden of sourcing, normalizing, and enriching data.

Regardless of the driver and the path taken, a new ecosystem must be designed. It would encompass modified data flows, governance, and enhanced service levels. The switch is often challenged with knowledge gaps, resourcing issues, and long-drawn timelines. A carefully designed transformation can decrease friction and deliver greater value.

What does a Strategy-led Transformation look like?

A strategy-led transformation, while beneficial, is heavily prone to increased cost and project extension risk.

Most commonly, transformations are strategy-led by consulting firms and executed in three phases.

  • Current State Assessment: It commences with understanding the current state through interviews and structured questionnaires. Process and data flow diagrams are created to realize the critical pain points.
  • Benchmarking & Analysis: Thereafter, a series of collaborative brainstorming sessions help identify potential options. The firm is placed in the context of its peers and industry best practices.
  • Target State Definition: A target operating model with a multi-year roadmap is defined. List of projects that progressively enhance the current state is detailed. Critical Success factors are established. Scorecards are defined. Progress is monitored periodically.

Under such an approach, project risk tends to arise in the following ways:

  • Experience-based decision-making: There is no substitute for experience. However, quickly obtaining a detailed understanding of a firm’s landscape is often difficult. Comprehension limits pose issues; subsequently, the information required to make decisions is missing.
  • Inflexible Structure: Firmwide transformations necessitate concerted discussions with various business units & IT departments. Some departments could undergo significant developments quickly, altering the proposed model. Substantial effort is required to understand the changing landscape, assess the impact and tweak the model. Costs soar, and timelines expand.
  • Delayed Benefits¹: The benefits of integration in a large transformation program becomes more visible with increased connections and bridges between systems. It could take anywhere between 24-36 months to realize this. The longer the drag, the higher the risk.

What differentiates a Data-driven transformation?

Data-driven transformations unearth greater detail to overcome blind spots.

A data-driven transformation is a metric-driven approach right from the get-go. A comprehensive inventory listing all data elements from data stores and vendor products to API libraries and reports is created in the assessment phase. Most transformations require a cleanup of their existing IT systems², especially those stemming from taking advantage of modern capabilities.

A firm-wide data inventory provides a basic understanding of your landscape. It can answer questions such as:

  • Which system(s) contains the most critical data?
  • Where does the bulk of reporting reside?
  • How modern is my firm’s landscape? (API vs. Feed distribution)

Data collection is only the first step. Enriching the inventory and summarizing can lead to further insights. Here are some examples:

  • Which systems curate new data?
  • Which data points are used in decision-making?
  • How redundant is my data across systems?
  • Which is the gold source(s) for each data domain?

A consolidated inventory can reveal interesting patterns that can only become apparent by looking at data holistically. 70% of transformations fail, and one of the important reasons is the need for more information to define initiatives in sufficient detail³. Not only does the inventory illuminate your understanding of the current state, but it could also present novel options for the target state.

Source: Meradia

Data-driven transformations do not fundamentally alter strategy-led approaches. Rather it augments by providing an additional lens – the critical ‘data perspective.’ Inventory capture can run parallel to interviews during current state assessments.


Data-driven transformations increase confidence, deliver value early and reduce risk.

By providing a jump-start into understanding a firm’s systemic landscape, transformation agents can now glean the inventory and derive value in several ways.

  1. Validate interview responses prior to designing the target state – Enable educated predictions about timelines.
  2. Find the magnitude and usage of Excel vs. different applications – Uncover potential to solve the spreadsheet chaos and ascertain operational maturity.
  3. Obtain a consolidated but in-depth view of the systemic landscape – Greater visibility into the extent of technical debt and relative usage of vendor products.
  4. Recognize similarity of data between various systems – Mix and match to supply alternate solution options. Interesting views of the new ecosystem could appear. Informs Scoping and Impact Analysis.
  5. Know completeness of data – Data quality issues are understood sooner rather than later.
  6. Differentiate pass-through and calculated data – Define targeted business requirement documents (BRDs) with mapping info.
  7. Understand reports and dissemination data points – Develop a business case for report rationalization and accelerate design of APIs or vaults.

It tends to broaden understanding and fill experience gaps.


Strategy led-transformations are delayed due to a lack of relevant data, information asymmetry, and dependency on subject matter experts.  A data-driven approach with enriched inventory could serve as a valuable resource by promoting data literacy, filling knowledge gaps, resolving ambiguities, and overcoming skill discrepancies. It complements a strategy-led approach by helping overcome its pitfalls. It sets the stage for quick wins, without which the success of your transformation might be at high risk.

Data-driven approaches require strong governance right from the start. Watch out for the following paper in our series, where Andrew Jacob, CFA, discusses the importance and benefits of performance-led governance initiatives.


¹Why Transformations Fail – John P Kotter, Harvard Business Review, 1995. Accessed on 06/05/2023.

²Why so Many Digital Transformations Fail – Thomas H. Davenport and George Westerman., Accessed on 06/01/023.

³Early Strategic Assessment – Mckinsey., Accessed on 05/23/2023.

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Jose Michaelraj, CIPM, CAIA

Jose Michaelraj brings strong domain, process, and technical skills to Meradia client engagements. A performance subject matter expert with strong derivatives knowledge, Jose has worked on dozens of performance transformation projects. He is well-versed in technical skills, including the ability to code, write SQL, and Python. Jose has been analyzing, designing and implementing data management and performance solutions on several leading platforms including FactSet and Eagle. Jose is an expert in performance operations technology, derivatives processing, and designing custom attribution systems. Attempting to maximize business value while reducing Operational risk is his favorite pursuit.

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