The Data Analyst Skills Gap: Why Business Process Understanding Matters More Than You Think

Data is everywhere, and the demand for data analysts is soaring. But despite the hype, finding truly effective data analysts remains a challenge.

The Growing Demand for Data Analyst Skills

The World Economic Forum's Future of Jobs Report consistently ranks data-related roles among the most in-demand. Big Data Specialists, Data Warehousing Specialists, and Data Analysts/Scientists are all top contenders, highlighting the increasing need for businesses to leverage data for better decision-making.

Beyond the Numbers: The Business Process Connection

Traditionally, data analysts focus on assessing databases and analyzing large volumes of transactional data to identify patterns. However, many companies struggle with the crucial first step: ensuring their data is actually ready for analysis.

The reality is that much of the valuable process data and context resides in malformed systems or local spreadsheets. While established companies should have robust systems in place, we often find that’s not the case. More often then not, established companies have established systems that haven’t evolved as quickly as processes have evolved. That leads to data being stored in multiple systems. We then find that successful data initiatives are driven by individuals who possess a unique combination of skills. These individuals not only understand data structures and pipelines but also have a strong grasp of discovery for business processes.

They can:

• Uncover the underlying processes that generate the data.

• Identify the additional data points needed to tell a complete story.

• Investigate why certain data is tracked in spreadsheets instead of systems.

• Develop solutions that accommodate the people involved in the process, not just the data itself.

In essence, these individuals are process analysts first and data analysts second. And this distinction is critical.

Why Business Process Understanding Matters

1) Data is rarely structured perfectly

Ideal systems that perfectly reflect our needs are rare. Real-world data is messy, incomplete, and often doesn't fit neatly into predefined structures. A deep understanding of the underlying business processes helps analysts navigate these imperfections and still extract valuable insights.

2) The big picture is key, but often hidden

Big-picture thinking reveals valuable insights, but connection points are often unclear. Foreign keys may be missing, or leadership may lack the granular understanding needed for data discovery. Understanding the overall business context and how different parts of the organization interact allows analysts to piece together the bigger picture, even when the data connections aren't readily apparent.

3) Spreadsheet tracking isn't inherently bad, if it's reportable

Data in spreadsheets can be valuable, provided it can be integrated into reporting. Many crucial processes are initially tracked in spreadsheets. A business process-oriented analyst can find ways to bring this data into the broader analysis without disrupting existing workflows.

4) Design before discovery

Start with the desired end result—the visualization—and work backward. Tools like PowerPoint and Figma are excellent for rapid mockups. By visualizing the end goal first, analysts can ensure that their efforts are focused on gathering and preparing the most relevant data. This prevents getting lost in the weeds of data exploration.

5) Avoid ETL or business logic in dashboards

Embedding ETL or business logic in PowerBI or Tableau reports leads to maintenance nightmares. Use dedicated data engineering solutions like Databricks, Azure Synapse, or Snowflake instead. When business logic is embedded in dashboards, it creates complex dependencies that are hard to maintain and update. Keeping the data transformation logic separate and in a dedicated environment ensures scalability and reliability.

6) Well-defined data problems are uncommon

Business problems are rarely clearly defined at the outset. Effective data analysts help define hypotheses and identify the relevant data sources. Often, stakeholders know they need "data" but aren't exactly sure what questions to ask or what data to look at. A process-focused analyst can help refine the problem, develop testable hypotheses, and pinpoint the data required for analysis.

Key Takeaway

Success as a data analyst requires more than technical prowess. A deep understanding of business processes is crucial for transforming raw data into actionable insights. At PMB, we provide data analyst services grounded in this principle. We collaborate with you to define your goals and desired outcomes, then work backward to implement scalable and sustainable solutions. Whether you need help troubleshooting existing reports or building new ones from scratch, we're here to help.

Let's discuss how we can unlock the potential of your data. Contact us today!