In the early stages of any company, regardless of size or industry, data can be both an asset and a liability. As teams focus on growth, product development, and customer acquisition, data management often gets pushed aside—leading to headaches down the road.

I experienced this firsthand at a company where we relied on two different data sources—Pipedrive for deal management and our internal CMS. These systems were not synced, resulting in confusion, errors, and an overwhelming amount of manual work. Eventually, the discrepancies between these systems became a bottleneck for decision-making. Here’s a closer look at what went wrong and the lessons learned, which can help you avoid common pitfalls early on.

🚩 The Consequences of Poor Data Management

1. Inconsistent Data Across Systems 📉

Our biggest challenge was having two data sources that didn’t communicate with each other. While Pipedrive handled deals and customer interactions, our CMS stored other crucial business information. Unfortunately, these two systems were not integrated. This led to a number of issues:

• Misspellings and Inconsistent Naming: Each investment associate named deals according to their personal preferences, leading to inconsistent and non-standardized entries.
• Missing or Mismatched Data: We frequently encountered deals that existed in Pipedrive but were missing from the CMS, and vice versa. Dates, names, and amounts didn’t match across systems.

The end result? We couldn’t reliably join the data from both sources, which made reporting and decision-making nearly impossible. ❌

2. Manual Data Validation ⏳

Because of the discrepancies, we found ourselves manually cross-checking every deal. This was time-consuming and prone to human error. Instead of having a seamless flow of information, we were bogged down in manual processes that should have been automated.

3. Lack of Data Trust 😬

When you’re constantly dealing with conflicting data, it’s hard to know what’s correct. This erodes trust in the data, making it difficult to rely on it for making strategic decisions. In our case, we couldn’t generate meaningful reports because the data was unreliable. When it came to financials, this became a serious issue, as we needed accurate numbers to track revenue, profits, and customer behavior.

❌ Common Data Management Mistakes

1. Neglecting Data Integration Early On 🔗

• Mistake: Using multiple systems without ensuring they are synced and integrated leads to data silos and inconsistencies.
• Solution: From the beginning, invest in tools and processes that allow your key data sources to communicate with each other. Integrate your CRM, CMS, and any other data platforms to ensure consistency across the board.

2. Lack of Data Standardization 📑

• Mistake: Allowing team members to enter data without any guidelines or naming conventions causes chaos down the line.
• Solution: Implement strict naming conventions, standardized data formats, and training for your team to reduce errors and ensure consistency.

3. Postponing Data Audits 🕒

• Mistake: Waiting too long to review and clean up your data can lead to unmanageable issues.
• Solution: Regularly audit your data to catch errors early on and ensure everything stays aligned. This will save you massive amounts of time (and money) later on.

✅ Key Takeaways

• Start early: Treat data management as a priority from day one, even if your company is just starting out.
• Standardize everything: Create consistent rules for data entry and enforce them across the team.
• Integrate: Ensure your systems talk to each other, reducing the risk of siloed or incorrect information.
• Regularly review: Frequent audits and clean-ups can help you catch and fix errors before they become a massive problem.

The consequences of bad data management can haunt your business as you scale. By focusing on clean, integrated, and standardized data from the start, you’ll set your company up for success and enable data-driven decision-making as you grow. 📈✨