How AI Can Help Your Business Organize and Understand Scattered Data

Data is the lifeblood of any business. Yet, many companies find themselves overwhelmed, swimming in vast amounts of unstructured information without fully understanding it. This lack of clarity can lead to missed opportunities, inefficiencies, and decisions driven more by gut feeling than by facts.

If your business data is scattered across multiple systems, making it hard to access and interpret, artificial intelligence (AI) and machine learning (ML) solutions might offer the clarity you need.

In this blog, we’ll explore how AI can help businesses organize and understand their data, with real-world examples from ThinkNimble projects.

[Learn more about AI for data management in our recent webinar, “Making Your Data Useful with AI.” Watch the full replay here].

Common Data Challenges Businesses Face

Businesses often store data across multiple systems and platforms, creating significant challenges when trying to derive meaningful insights. Here are some of the most common pain points:

• Manual Data Entry and Transfers: Employees often spend valuable time manually entering and transferring data across systems. For example, copying information from emails to a CRM or shared document platform like Salesforce.

• Disconnected Context: Data arrives without a broader context—like an email regarding a project not linked to the appropriate client or project record—making it hard to interpret accurately.

• Siloed Knowledge: Knowledge often lives in individual emails or in employees’ minds, making it difficult for teams to share information and collaborate effectively.

We recently worked with a trip-planning company that faced this exact challenge.

Their data lived across platforms—Salesforce, email, travel booking systems, and more—making it challenging to centralize information and share insights across the team.

Without a unified system to manage and interpret data, employees wasted time searching for information, and important trends went unnoticed.

How AI Can Help Organize and Understand Data

This is where AI for data management becomes invaluable. Through data aggregation, categorization, and analysis, AI can uncover insights that would otherwise be hidden.

Here’s how AI can address these challenges:

1. Data Aggregation: AI can centralize data from different sources into a single, unified view, improving visibility and offering a holistic view of operations.

2. Automatic Categorization and Contextualization: AI can categorize incoming data and connect it to relevant records. For example, linking an email about a trip to the correct client or trip project. This saves time and makes data easier to interpret.

3. Anomaly Detection: AI detects outliers, like an unusual expense in a financial dataset, alerting the team to review it. This is especially useful for identifying potential errors or uncovering unexpected trends.

4. Data Prioritization: When facing large datasets, AI can identify the most important data points out of hundreds, helping teams focus on what matters for decision-making.

Case Study: AI for Data Categorization in Trip Planning

For our trip-planning company, data scattered across Salesforce, emails, call logs, and travel booking APIs posed a major barrier to efficient planning. AI provided the answer.

By aggregating data into a central hub, AI categorized incoming information, linked it to relevant clients and projects, and even flagged anomalies for review.

With improved data access and categorization, their team could plan more effectively and provide better service to clients.

Potential Pitfalls of AI: Understanding and Avoiding Bias in Data

While AI for data aggregation and understanding can offer incredible value, it’s essential to remain aware of potential biases in your data.

AI models learn from historical data, which means they can replicate biases if they’re present. For example, if a financial institution’s data shows bias in loan approval, AI might learn and perpetuate that bias.

To mitigate this risk, you can review AI’s outputs regularly and remove biased data points to ensure fair and ethical practices.

Final Thoughts: Taking Control of Your Data with AI

If your business is struggling to understand and use data effectively, AI provides an efficient solution for organizing, categorizing, and analyzing scattered information.

With a strong data strategy and the right AI tools, companies can gain actionable insights, detect anomalies, and reduce bias in their data analysis.

Before diving into AI, it’s critical to consider your data integration strategy. Connecting data from various systems lays the foundation for AI’s effectiveness. And as always, human oversight remains essential for ensuring that AI’s insights are accurate and unbiased.

---

Interested in exploring AI solutions for your data? Check out our webinar, "Making Your Data Useful with AI," to see AI in action and learn how it can transform your business data management. Watch the replay here.