This thought piece is written from my perspective as a former pricing consultant, reflecting on patterns I observed across industries dealing with legacy systems and siloed data. It represents my personal take on how pricing reviews can serve as a strategic entry point for transformation.
While I regularly use AI as an ideation and productivity tool, I chose not to at all for this piece — I wanted to share a raw perspective to encourage an organic discussion.
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There were two common challenges I faced during my time as a pricing consultant, especially when working with large multinational clients. Firstly, the use of legacy systems and software to manage data created significant bottlenecks in our analytic work. Secondly, the fracturing of data into isolated silos that made it difficult to reconcile data and build the bigger picture. These created data blindspots where information could not be linked and promoted an inertia within an organisation to assess its own data. The difficulty of accessing data resulted in companies abandoning healthy pricing practices as the increasing strain to execute periodic strategy reviews began to drain organisational will. Allowing legacy systems and data silos to discourage strategy review protocols can lead to a significant risk of organisational misdirection as empirical evidence is less likely to inform strategic decision-making.
It may seem that the intuitive response to these challenges is to upgrade data infrastructures and centralise data softwares and systems, however, I would argue that it is by maintaining robust pricing practices that organic change can develop. While upgrading systems would, indeed, make life easier, the ultimate abandonment of pricing practices, not the technical ease of accessing or reconciling information. Even though we were faced with these data challenges as consultants, it was still very much possible to deliver valuable insights to our clients, albeit more difficult than need be. It is when the slow software and splintered datasets discourage a company from confronting its own data that the problem emerges. Organisations sit on top of an inaccessible goldmine of information because it is difficult to drill into it. Yet, it is exactly by going through the painful process of implementing periodic pricing reviews that the importance of the data will surface, with a greater understanding of how to overhaul these systems and benefitting from reaching the insights they hold.
Legacy systems
Failing to develop data infrastructure as organisations scale can lead to data management systems becoming increasingly cumbersome as demands evolve. Ironically, as a company’s needs grow more complex, the effort to migrate data systems also becomes more strenuous, leading to outdated systems and software be using long after they are unfit for purpose. The result is slowdowns, bottlenecks, and analytical inertia.
For example, one of our largest clients used an outdated software that would take an entire day or two to export a small dataset. If even a single column of data was missing, the entire process had to be repeated. In this project, we had scheduled our data collection for a single week but were receiving primary data months into the project; primarily due to the difficulty of the client team to extract their own data. It is these sorts of technical difficulties that can discourage organisations from putting in the effort to assess their own data and generate new insights. This leads to missed opportunities and a handicapped capacity to evaluate the effectiveness of their own strategies.
Attempting to modernise legacy systems might be an intuitive approach, however, I would argue that this should be preceded by building an organisational understanding of a company’s data needs. Yes, slow software is problematic, but the ultimate issue is how cumbersome systems discourage frequent data reviews and lead to an incomplete understanding of the data. In order to build effective data infrastructure, a company must first build a foundational understanding of its data needs and challenges, which it does by confronting its own data.
Companies can build an internal understanding of these data needs and challenges by implementing periodic pricing strategy reviews. Not only should this be implemented as an essential pricing practice, it will also help to create an organisational muscle memory and pinpoint the weak spots within the legacy systems. This familiarity with the data will encourage effective infrastructure reforms in two ways. Firstly, members of an organisation will become more vocal about the frustrations of the data challenges they face and with a greater understanding of the direction that the infrastructure should be evolving towards. Secondly, the insights that are produced as the result of periodic pricing strategy reviews, even with ineffective systems, will highlight the opportunity costs of having such inadequate systems, further encouraging investment into data software, systems, and infrastructure. There is no quick fix for poorly built data infrastructure, but the compounding benefits of pricing reviews can promote an organic roadmap to data restructuring and highlight the ROI on upgrading data infrastructure.
Data silos
Legacy systems are not the only challenge that large and scaling organisations face. Neglecting to manage and keep track of splintering data can lead to data being compartmentalised in silos. The effort to maintain these separate silos can lead to the bigger picture being lost in the data fragmentation. The result is blindspots that prevent a comprehensive understanding of the data and makes organisations susceptible to poor decision-making.
A common example of this is how departments use different datasets or software systems that make data reconciliation difficult. This creates an artificial separation of interests along departmental or software lines where it is easy to overlook opportunities that would have been easier to explore with data unity. In a case involving our largest client, a department used two separate data systems for a single product line, splitting transaction data and client information in a way that could not be reconciled. I met with the department heads to understand how they reconciled the two datasets and the answer was that they didn’t. We were unable to generate granular insights for a significant product line because the two data tracking systems had been calibrated poorly, creating a blindspot instead of insights.
Though this was a somewhat extreme example, the real challenge is not a lack of centralisation, but a lack of top-down strategy driving cohesion. Indeed, centralising data systems would make insights more accessible, however, the absence of top-down strategies means there is no organic need to reconcile the data – these data silos can continue operating in isolation without interrupting operations. Moreover, relying on the convenience of a centralised system to grab easy insights is not a robust strategy. Trying to reconcile siloed data should arise as part of a natural process to review your own data.
Periodic pricing reviews are part of a healthy pricing strategy and also provide a driving force to unite data that has fractured into silos. Departments would begin organising their own data to generate the reports needed; there would be cross-departmental collaboration to understand how data across the company is interrelated; the weak spots and challenges of the software and data infrastructure would be surfaced, etc. Not only will this help to generate insights to fine-tune the company’s strategy, but individual departments will also be better able to leverage their greater understanding of the wider data to better execute their own responsibilities. The whole is greater than the sum of its parts.
In conclusion, legacy systems and fragmented data do cause disruptions – but the solution is to implement periodic pricing reviews and adhere them, even if querying the data does get challenging. A greater understanding of how better to develop these internal systems will also emerge as a result of contending with an organisation’s own data inefficiencies. The pain of dealing with inadequate systems is often a symptom of a growing organisation – but the real issue is when this pain prevents good pricing practices or leads to their abandonment. By strictly adhering to a strategic reviews, the will and understanding to overhaul outdated and fragmented systems can remain strong in an organisation.
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