Data as the Outcome of Every Process
Data is always the logical outcome of a process, and that holds true in software development, financial management, and human resources when information is gathered and is ready to be analyzed. Behind every process, there’s people building companies, products, and services that in turn produce even more data, which leads us to ask if Data is just a byproduct or the foundation.
When Data becomes the foundation, that tells us what worked, what didn’t, what could be improved, and where risks or opportunities lie, at least when it comes to the Global Business Services landscape. When there’s a timely execution, high-quality Data is no longer a byproduct, but transforms the current operational model into a target operating model (TOM) and unlocks scalability, resilience, and digital maturity. On the flip side, when you get poor-quality data as an input, that will also impact the output and most likely derail the transformation before it even starts.
Planning a transition begins with analyzing all available inputs, including the structured numbers in spreadsheets and dashboards, and also unstructured feedback from process experts, employees, and customers. From a human-centric standpoint, listening to what people think, talk, do, and say can help to uncover frustration and pain points that become actionable items for Leaders. Once the scope and resources analysis is complete, organizations define the TOM, location strategy, and governance.
The pathways can vary with GBS consultants on top companies performing lift-and-shift, lift-and-transform, or big-bang transitions. That means in theory there’s only something being moved, but in practice a lot of gaps are identified and prompting things to be changed. When that’s significant, automation and standardization become key levers for bigger transformations, which can involve multiple functions and geographies. At this point, many leaders are tempted to see AI as a golden bullet; however, that’s only as good as the quality of the data it is trained on. High-quality, reliable, and complete datasets are things that can make tools like automation and AI valuable in the context of transformations, rather than risky.
People, Processes, Technology, and Quality
Every transition starts with people, because without stakeholder engagement, change champions, and digital upskilling, there’s a big risk involved. “Digital” can become just another buzzword if people don’t become a critical aspect involved in any kind of transformational efforts.
Processes come next with harmonization, documentation, and risk controls, ensuring a stable foundation. Technology follows, enhancing mature processes through ERP systems, automation, and AI, and finally, stronger governance ties everything together, with well-defined roles and responsibilities supported by project management and change management offices.
Across all these phases, quality is the real differentiator, and not only with reliable data to accelerate transformation, more engaged people, robust structures enabling risk mitigation, and technology used as a lever. That prevents poor-quality data, which in turn only undermines trust, slows adoption, and wastes resources. There’s a cost of not doing things properly, and that became evident for any big organization over the years, not only because of results that can be followed on every quarter end, but over decades.
On paper, transitions sound straightforward, but in practice, they are fragile with any unreliable, inconclusive, or misleading Data. For instance, documentation on how the processes are carried out can be missing or incomplete, which impacts maturity assessments. Those can vary depending on who conducts them, and hold a lot of bias if the sending entities do it in a murky way. There’s a situation where ERPs are fragmented across multiple instances, and even when that mess of systems somehow still works, there is the biggest risk of post-go-live fatigue, where morale drops and adoption stalls.
I’ve been on both sides when the data reported for transitions reflects reality, and when it tells a different story. There were cases when transitions ran smoothly and other times that did not work as expected. Working as a Black Belt, part of my work was identifying what didn't work well after transitions and creating contingency plans to help, but the real difference always appeared in front of me because of the work of change managers and project managers which were there to ensure not only green metrics but early engagement, clear value communication, efficient knowledge transfer, and modular roll-outs starting with high-maturity markets.
Most importantly, people provide both the initial inputs and the outputs that form any baseline and post-improvement dataset. When the employees were out of energy, angry, and disengaged, the data suffered. The best organizations I’ve worked with invested in diversity and inclusion events, celebrated big wins, and made employees feel like they were part of something bigger. This kind of connection could directly improve the quality of the information they generate.
AI and Data Quality
Artificial Intelligence has a lot to do with powerful models and how fast they run, but the biggest factor shaping that is the quality of the data it learns from. Looking at GBS
organizations, clean data makes AI smarter, but bad data makes it not only unreliable but even dangerous. As AI spreads into different aspects of our lives in healthcare, finance, education, and daily apps, understanding data quality isn’t just a technical detail. That can help to get a more transparent and reliable AI.
Designing smarter algorithms or throwing more computing power once could ensure robustness, but the data that is representative and well-labeled allows models to learn fast. Poor data can lock an AI into mistakes, no matter how advanced the model is.
With billions of data points flying around from social media, sensors, or apps, humans can’t check it anymore on their own. That’s especially important when dealing with “live” data streams, like traffic updates or medical monitoring. New issues with data quality appear every day, for instance, Bias, Drift, and Lineage.
Quality now includes both technical fixes with cleaning, balancing, or augmenting data and organizational processes that are all about who checks the data, and how feedback loops are handled.
Studies show clearly that when training data has errors or gaps, AI performance drops. Because of that, Governments are starting to regulate AI more strictly (take, for example, the EU AI Act). These rules require that training data be non-discriminatory, easy to explain, and trustworthy. In the end, quality is not just about performance; it’s about legal safety and public trust.
Even when Data is “clean,” two hospitals might track patients in different ways, or two banks might define “income” differently. This “variety” makes it hard to merge data into a single picture, and mismatches can quietly ruin AI results.
Mitigation comes from moving beyond supervising or managing data to orchestrating it. That means aligning people, systems, and AI to ensure flow. By combining technical fixes like cleaning, balancing, and monitoring with governance, feedback loops, and cultural alignment, organizations can turn fragile data into resilient, trustworthy AI.
Quality as Strategic Differentiator
The success of GBS transitions does not hinge only on technology or project plans, but really depends on whether the data guiding those plans is trustworthy or not. Poor-quality
data leads to false assumptions, wasted investments, and disillusioned teams.
At the same time, recent research in data-centric AI shows that once technology and models reach maturity, the decisive factor for performance and transformation is no longer
algorithms but the fitness, representativeness, and reliability of the Data that is maintained
by enterprises. This reframes data not only as a product of operational processes but as an important strategic resource.
People and culture are the hidden drivers of quality: engagement, recognition, and inclusivity translate directly into better datasets. Organizations that invest in data quality orchestration as a strategic asset will not just survive transitions but thrive in digital transformation.