Assessing connections—evaluating system integrations and data flows

4.1 Introduction: The Importance of Connections

In any tech stack, connections refer to how your systems communicate and share data with each other. Strong connections ensure smooth data flow, eliminate manual workarounds, and reduce the risk of inconsistent information across platforms. When connections are poorly configured or lacking entirely, you can end up with data silos, duplicated efforts, and broken workflows.

Many people fixate on the number of connections or integrations, but quality trumps quantity. The goal isn’t to connect every tool—it’s to ensure the right data flows efficiently.

Often, you don’t need direct integrations between all your tools. Instead, focus on moving data into your data warehouse, where you can create a single source of truth. This approach minimizes redundancy, reduces complexity, and ensures consistency across your systems.

Before considering a switch to new tools, it’s crucial to evaluate the current connections and determine if small improvements—like API adjustments, automations, or a bit of engineering support—could help existing tools meet your needs. Clarify, the company writing this content, believes an ideal CRM should be self-sustaining, minimizing the need for manual updates by automatically pulling in and synchronizing essential data from various sources. Achieving this standard may require creative integration solutions, but the benefit is a more reliable CRM that empowers teams with up-to-date information.

In this lesson, we’ll explore methods to identify and resolve common connection issues, examine when it makes sense to enhance existing connections rather than replace tools, and offer examples to help you assess whether better connections could solve your data flow challenges.

4.2 Identifying Connection Issues

Connection issues often manifest in ways that affect team efficiency and data reliability. These issues can sometimes look like tooling limitations, but they are more likely related to system integrations and data flows. Here’s a closer look at some common signs of connection issues, along with examples that illustrate how they impact everyday workflows.

Data Silos

Data silos form when systems fail to share information, creating isolated data that’s accessible only within a specific platform. These silos limit the organization’s ability to access a complete view of customer interactions, which can hinder decision-making and lead to duplicated efforts.

• Example: A customer service team uses a ticketing system to manage support requests, while customer data sits in the CRM. When support agents need to view a customer’s purchase history to resolve issues, they must toggle between systems, slowing down response times. By connecting the CRM with the ticketing system, all necessary data could be accessible in one place, improving both efficiency and customer experience.

Manual Data Transfers

Manual data transfers occur when team members have to manually export, import, or update data across systems. Not only does this take time, but it also introduces potential for human error, leading to inconsistencies or incomplete data.

• Example: Marketing regularly exports lists of leads from the CRM, cleans the data in spreadsheets, and re-uploads it to an email marketing platform for campaigns. While it seems like a tool limitation, this is actually a connection issue. An API or middleware solution could automate data syncing between the CRM and the email platform, reducing errors and freeing up valuable team time.

Integration Errors

Automated workflows can sometimes fail due to errors within APIs or integration platforms. These errors cause incomplete data transfers, leading to missing information or delayed updates.

Example: An e-commerce team uses an integration to pull real-time inventory data from their inventory management system into their online store platform. However, frequent API errors cause the inventory data to lag, sometimes showing products as “in stock” when they are actually sold out. A deeper evaluation of the API health and a focus on error monitoring would help address this issue without needing to change the inventory management or e-commerce tools.

4.3 Evaluating Your System Integrations

Evaluating system connections requires a systematic approach to mapping integrations, analyzing data flows, and auditing the technology’s performance. This helps determine whether the integration setup meets the business’s needs or if simple improvements could enhance connectivity. Here are key steps in evaluating your current connections.

Integration Mapping

Integration mapping involves documenting all current integrations, data flows, and touchpoints between systems. This process highlights gaps or areas where data isn’t being shared effectively.

• Example: In a B2B organization, mapping reveals that lead scoring data from a third-party tool doesn’t automatically populate in the CRM, even though sales teams need this data for follow-up. By mapping all current integrations, the team identifies a missing connection and implements a solution, making it easier for sales reps to see lead scores directly within the CRM.

Guidance: List each tool in your tech stack and document how data flows between them. Identify systems with one-way or non-existent connections, as these are the most likely sources of data silos or inefficiencies.

For each integration, document:

• Where the connection exists
• Why it was set up that way

Understanding the reasoning behind integrations is key. Was HubSpot connected due to a technical limitation, or simply because someone didn’t know a better option? Identifying these nuances helps uncover underlying issues.

Data Flow Analysis

Data flow analysis involves assessing how data moves through your systems, where bottlenecks occur, and how well the data serves team needs in each system. This analysis helps uncover whether adding integrations or improving current ones could better align data flows with business objectives.

• Example: A sales team relies on customer enrichment data to qualify leads, but this data only resides in a separate analytics tool. To view it, team members must leave the CRM, which disrupts their workflow. Rather than moving to a CRM with built-in enrichment capabilities, the team sets up an API that pulls enrichment data into the CRM’s contact records. This solution provides the needed data without changing systems.

Guidance: Examine each system’s role in data flow, noting if critical data is being siloed or delayed. Consider whether integrating or automating data transfers could make information more accessible across teams.

Technology Audit

A technology audit reviews the health and reliability of your existing integrations, middleware, and APIs. This audit helps identify if performance issues stem from outdated or insufficient integrations.

• Example: A technology audit of a CRM integration shows frequent data syncing errors between the CRM and ERP system due to an outdated API version. Updating to the latest API and adding error monitoring significantly improves data reliability.

Guidance: Audit each integration for reliability, update outdated APIs, and set up alerts to catch issues early. Integrations that are unreliable or unsupported should be prioritized for improvement or replacement.

4.4 Improving Connections

Once you’ve identified areas where system connections are causing friction, consider ways to improve them. In many cases, simple adjustments to integrations can make existing tools work seamlessly, without the need for costly replacements. Here are some strategies to enhance connections within your tech stack.

Invest in Integration Platforms

Integration platforms like Tray, Workato, Zapier, MuleSoft, or custom APIs can facilitate seamless data transfers between systems, allowing each tool to perform its specific function while sharing data with others. These platforms offer flexibility to adapt your connections as needs change. Clarify offers such a diverse group because depending on your size and stage different tools will make more or less sense.

• Example:

A healthcare company uses a separate CRM and billing system that don’t natively integrate. By using MuleSoft to sync data between these systems, they ensure that patient billing information is automatically updated in the CRM, saving staff time and improving data accuracy.

• Example: In the early days of a company’s growth, Zapier is a great way to get started. Ultimately, it becomes too expensive at scale and you’ll need to find more cost-effective and data efficient ways to improve connection.

Guidance: Choose integration platforms based on your tech stack’s specific needs and scalability requirements. Consider using middleware that allows for loose coupling to maintain flexibility in your stack.

Data Synchronization Strategies

Implementing real-time or near-real-time data syncing ensures that the most current data is available to all teams, reducing the risk of errors caused by stale information.

• Example: A real estate company relies on real-time syncing between its CRM and property management system to provide accurate availability information to potential renters. Previously, the lack of real-time updates led to inaccuracies, but implementing a near-real-time sync improved the team’s ability to serve customers.

Guidance: Set up data synchronization for high-impact workflows, prioritizing real-time syncing for data that needs to be updated frequently (e.g., inventory or customer status updates). If full synchronization isn’t feasible, identify key data points that could benefit from near-real-time updates.

Regular Monitoring

Setting up alerts and dashboards for integration performance allows you to proactively identify and resolve issues before they affect workflows. Regular monitoring helps maintain smooth data flows and ensures integration health.

• Example: A retail chain sets up dashboards to monitor data syncs between its CRM, e-commerce platform, and warehouse system. Whenever a delay occurs, the operations team receives an alert, allowing them to resolve the issue promptly and keep data consistent.

Guidance: Use monitoring tools to set up alerts for any sync delays or API errors. Regularly review performance metrics to ensure your integrations continue to meet the organization’s needs and support reliable data flow.

4.5 Case Study: Enhancing Connections to Eliminate Data Silos

Situation: A retail company struggles with siloed customer data across its online and in-store systems. In-store purchases and customer interactions are recorded in a POS system, while online transactions and customer data sit in a separate e-commerce platform. This disjointed setup makes it difficult to track a customer’s entire purchase history across channels, leading to a fragmented customer experience.

Action: Instead of replacing the entire system, the company invests in an integration platform to bridge the data between the POS and e-commerce systems. They establish automated syncing, allowing customer profiles and purchase history to update across all systems in real-time.

Result: With a unified view of customer data, marketing teams can personalize campaigns based on both online and in-store behaviors. The integration enhances the customer experience and allows for more accurate reporting, without the need for manual data reconciliation or a costly system overhaul.