We all know that in order to make data-based decisions, it’s often not enough to just look at a single source of information. Often, truly new insights are only gained by combining several pieces of information.

Whether it’s merging sales figures from different departments, integrating customer feedback from multiple channels, or simply compiling financial data from different accounts, the task of combining data can be annoying. Especially for business users, this process often feels like navigating a maze without a map. So why exactly is it so tricky and time-consuming?

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1. Data Inconsistencies

One of the most common issues when combining data from different sources is inconsistency. Data might be stored in different formats, use different naming conventions, or have varying levels of detail. For example, one system might list a customer’s name as “John Smith,” while another records it as “Smith, John.” If you’re not familiar with how databases work, identifying and resolving these discrepancies can be frustrating.

Another common inconsistency is with date formats. One dataset might use “DD/MM/YYYY,” while another uses “YYYY-MM-DD.” These small differences can cause significant headaches when trying to merge data. For non-technical users, the challenge is compounded by the lack of tools or knowledge to standardize this data efficiently.

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2. Data Silos

In many organizations, data is kept in silos, meaning that different departments or teams have their own systems and processes for storing information. A marketing team might use one software for managing customer relationships, while the sales team uses another. When it’s time to combine this data, you may find that these systems don’t “talk” to each other easily.

For some users, accessing these silos can feel like trying to enter a locked room without a key. The process of extracting and combining data from these separate systems can be incredibly time-consuming, especially when you’re unfamiliar with the tools needed to do so.

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3. Lack of Standardization

Even when data is accessible, the lack of standardization across different sources can make combining it a nightmare. Different departments may have their own way of categorizing and recording data. For example, one team might track revenue by product line, while another tracks it by region. Trying to merge these datasets into a cohesive whole without being a data scientist can be like trying to fit together pieces of different puzzles.

This lack of standardization often requires manual intervention to align the data, which is both time-consuming and prone to errors.

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4. Complex Tools & Software

Even with the right data and some standardization, the tools used to combine and analyze larger amounts of data can be intimidating for some team members. Programs like SQL, Python, or even advanced Excel functions require a certain level of technical proficiency. Without this knowledge, users may feel overwhelmed and unable to complete the task efficiently.

This complexity can lead to a reliance on technical teams, which can cause delays and bottlenecks. Business users often find themselves stuck in a loop of requesting help, waiting for responses, or trying to learn new tools on the fly — all of which can slow down the process significantly.

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5. Data Security & Privacy Concerns

When dealing with data, especially sensitive or personal information, security and privacy concerns come into play. Combining data from different sources often means transferring it between systems, which can expose it to potential breaches if not handled correctly.

For some users, navigating the security guidelines and understanding the implications of data privacy laws like GDPR can be confusing. The fear of making a mistake that could compromise data security adds an additional layer of stress, making the entire process more daunting.

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Conclusion: The Path Forward

Combining data from multiple sources is a task that requires careful consideration, attention to detail, and often, technical know-how. For non-technical users, this process can be especially challenging due to data inconsistencies, silos, a lack of standardization, complex tools, and security concerns.

While these challenges may seem overwhelming, there are solutions. User-friendly data integration tools are becoming more available (such as DataMonkey ;) ), and businesses are increasingly recognizing the need for easy, day-to-day data access. With the right support and resources, the daunting task of combining data can become more manageable, allowing everyone to make the most of the valuable information at their fingertips.

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