Data collection is the foundation of any serious shared service center research. Without reliable data, even the most well-designed dissertation loses credibility. In practice, gathering information in shared services environments is more complex than in traditional organizational studies. Processes are centralized, stakeholders are distributed, and performance indicators are often tightly controlled.
For those working on academic projects, especially in the context of shared service center dissertation research, understanding how to collect and validate data is often the most challenging phase.
Shared service centers operate as centralized hubs that deliver services such as finance, HR, procurement, or IT to multiple business units. This structure creates unique data environments:
Because of this, data collection requires both technical understanding and methodological precision. It is not just about gathering numbers but interpreting operational realities.
Primary data is collected directly from the source. In shared services, this often includes:
Secondary data provides context and benchmarking:
Combining both types is essential, especially when applying mixed methods approaches.
Interviews provide deep insights into processes, challenges, and perceptions. In shared services, they are especially valuable for understanding inefficiencies and hidden bottlenecks.
Well-structured interview guides, such as those found in SSC interview question frameworks, help ensure consistency.
Surveys allow for scalable data collection across departments. They are useful for measuring satisfaction, performance perception, and process effectiveness.
Most shared service centers rely on ERP systems. Extracting data from these systems provides objective performance metrics such as:
Benchmarking is critical for evaluating efficiency. Using frameworks like SSC efficiency benchmarking helps position findings within industry standards.
1. Access Determines Everything
Even the best methodology fails without access to data. In shared services, access is often restricted due to confidentiality.
2. Data Is Fragmented
Information is rarely stored in one place. Financial systems, HR platforms, and ticketing tools all contain partial views.
3. Context Is Critical
Raw numbers mean little without understanding processes behind them.
4. Mixed Methods Win
Combining interviews with system data creates a fuller picture.
5. Validation Is Not Optional
Cross-checking data from multiple sources reduces risk of errors.
A finance SSC wants to analyze invoice processing efficiency:
Most discussions focus on methods, but practical challenges are often ignored:
Addressing these issues early can save weeks of rework.
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Data collection is not an isolated step. It must align with the overall methodology. A well-structured approach, such as outlined in SSC dissertation methodology frameworks, ensures coherence between research questions, methods, and findings.
Consistency is key. Each data source should directly support research objectives.
The best method depends on your research objective. If the goal is to measure performance, system data extraction is essential because it provides objective metrics such as processing time and cost efficiency. However, numbers alone rarely explain why issues occur. That is why interviews are equally important—they uncover process bottlenecks, communication gaps, and operational realities that data alone cannot show. Surveys can complement both by capturing perceptions across a broader audience. In practice, combining methods produces the most reliable results because it balances quantitative accuracy with qualitative depth.
Shared service centers are complex environments where data is distributed across multiple systems and departments. Access restrictions are common due to confidentiality concerns, especially in finance or HR functions. Additionally, data is often fragmented, meaning that no single source provides a complete picture. Another challenge is ensuring consistency—different systems may define metrics differently. Finally, human factors such as resistance to participation or biased responses can affect data quality. Overcoming these challenges requires careful planning, clear communication, and multiple validation steps.
Reliability comes from triangulation—using multiple sources to confirm findings. For example, if system data shows delays in processing, interviews can confirm whether the issue is due to workload, system limitations, or human error. Surveys can further validate whether these delays impact customer satisfaction. Another important step is data cleaning, which involves removing inconsistencies and verifying accuracy. Documenting assumptions and methodologies also strengthens credibility, especially in academic work where transparency is essential.
Relying solely on quantitative data is risky because it lacks context. Metrics can show that a process is slow, but they do not explain why. Without qualitative insights, conclusions may be incomplete or misleading. For instance, high processing time could result from system inefficiencies, lack of training, or poor process design. Each requires a different solution. Combining quantitative and qualitative data provides a more comprehensive understanding, making findings more actionable and meaningful.
One of the biggest mistakes is collecting too much data without a clear purpose. This leads to confusion and wasted time. Another common issue is relying on a single data source, which increases the risk of bias or incomplete insights. Poorly designed surveys and leading interview questions can also distort results. Additionally, failing to validate data or ignoring inconsistencies can undermine the entire research. The most effective approach is focused, structured, and validated data collection aligned with clear objectives.
The timeline varies depending on the scope of the research and access to data. In academic projects, data collection can take anywhere from a few weeks to several months. Interviews require scheduling and coordination, which can cause delays. System data extraction may also take time due to approval processes. Surveys need time for distribution and response collection. Planning for delays and building flexibility into the timeline is essential to avoid last-minute issues.
Benchmarking is not always mandatory, but it adds significant value. Without benchmarks, it is difficult to determine whether performance levels are good or poor. Benchmarking provides context by comparing results against industry standards or similar organizations. This helps identify gaps and improvement opportunities. However, it is important to use up-to-date and relevant benchmarks to ensure accuracy.