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Error-Prone vs. Safety Net: Ensuring Accuracy in Your Data Management

  • June 28, 2024
  • Data Management, Data Operations
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error prone vs safety net in crm data management

Maintaining accurate and up-to-date data is more crucial than ever. Clean data that follows consistent naming conventions, formats, and other standards is essential for smooth operations and informed decision-making.

However, achieving this level of accuracy is a significant challenge. Low-quality, inconsistent data is a perpetual pain point for many organizations.

Consider the ramifications of pushing a bulk update to your CRM, affecting thousands of records, only to discover that a mistake has introduced errors across your entire dataset. The aftermath can be devastating. Errors proliferate, and there is often no straightforward way to roll back or fix these mistakes without a massive manual effort. Such an effort can take weeks of tedious manual work—or nearly as much time developing and troubleshooting Excel formulas to fix the issues created by the update. The effort required to fix the problem is not just a minor inconvenience; it can lead to significant disruptions in sales processes, reporting inaccuracies, and overall inefficiencies.

It’s clear that modern organizations must employ safety nets to manage these risks effectively.

CTA-clean-your-crm-dat

Typical Manual Data Maintenance: An Error-Prone Process That Causes Fires While Attempting To Put Out Others

For most companies, data maintenance and updates are typically handled in two main ways: manual updates through the CRM's user interface and bulk updates using Excel. Both methods are fraught with potential errors and inefficiencies, often leading to more problems than they solve.

The Problems With Manual Updates

Manual updates via the CRM user interface involve painstakingly filtering records to identify those that need updating, navigating to each individual record, opening the back end, and manually changing the necessary fields. This process takes a long time and is also highly prone to human error. Because the process takes so long, employees often do not employ secondary quality checks, leading to inconsistencies.

The Problems With Bulk Updates Using Excel

Bulk updates with Excel might seem like a more efficient solution, but this fix comes with its own set of challenges. This process typically involves exporting data from the CRM, manipulating it in Excel, and then re-importing it back into the CRM. Here are the pitfalls of this approach:

Designing Excel formulas to update records accurately is difficult. For example, standardizing job titles can be a nightmare due to the various formats, such as “CEO,” “Chief Executive Officer,” and “C.E.O.” Creating formulas that account for all these variations is challenging and prone to errors. And even with the best-designed formulas, manual oversight is required. Correcting lingering issues by hand introduces further risk of error.

Additionally, the process of re-importing data can create new problems if the CRM does not correctly identify existing records, leading to duplication.

If errors are introduced during a bulk update, rolling back changes is not straightforward. Often, there is no clear audit trail, and if there is there is no way to roll back to the pre-update data, making it difficult to identify which records were affected and how to correct them.

This is no way to manage data. Companies with large databases—and even those with smaller ones—need safety nets for data management. Let’s take a look at the different types of safety nets you can employ and how they work.

Why Safety Nets Are Critical for Data Management

In the fast-paced world of modern business, the importance of data accuracy cannot be overstated. A single error in your CRM can cascade through your systems, leading to faulty reports, misguided strategies, and lost opportunities. Safety nets in data management ensure that your data remains accurate, consistent, and reliable.

Identifying and Fixing Problems Early With Consistent CRM Data Audits

A consistent data auditing process is essential for catching issues as soon as they arise. Regular audits help you identify and correct problems before they escalate and impact your organization. With a robust auditing system, you can catch duplicate records, outdated information, and inconsistencies in data formats promptly, before they spiral out of control.

Audit Trails for Rollback Capabilities

A comprehensive audit trail that provides CSV reports of your data before bulk updates makes it significantly easier to roll back changes if something goes wrong. This feature is invaluable when dealing with large datasets where even a small error can have widespread consequences.

Previewing Bulk Updates

The ability to preview bulk updates before they go live in your CRM is another critical safety net. This allows you to see exactly what changes will be made to your records, giving you the chance to catch and correct potential errors before they affect your live database.

Maintaining Data Cleanliness With Imports

Importing data into your CRM via a CSV can often introduce inconsistencies and errors, especially if the data isn't properly formatted. Data imports may be likely to create duplicates if the CRM system is unable to identify records on the CSV that are the same as records that already exist in your CRM.. Safety nets that allow you to format and standardize data during the import process are crucial for maintaining the cleanliness of your CRM database.

Controlled Access and Permissions

Ensuring that only trained and authorized users can perform data maintenance and updates is another important safety net. By setting appropriate permissions, you reduce the risk of untrained staff making critical errors during data updates.

Mitigating Risks With Insycle

Without these safety net features, you risk pushing large-scale updates that impact many records in your database, potentially causing widespread issues that are difficult to rectify. Fortunately, Insycle provides all of these critical safety net features and more.

Related articles

The Hidden Costs of One-Off Data Cleanups

CRM Data Quality Checklist: Top 15 Issues

Four Phases of Customer Data Management Evolution

Example 1: With Insycle, Run All Bulk Operations in Preview Mode To Identify Issues and Approve Updates Before They Go Live

Insycle offers a powerful feature that transforms the way you handle bulk operations: the ability to run all bulk updates in preview mode. This feature is a game-changer for organizations, providing an essential safety net to catch and correct errors before they go live in your CRM.

preview or update

Insycle allows you to preview the changes before you run a bulk update, helping you catch errors before they are introduced.

When you run a bulk update in Insycle, you have the option to generate a CSV preview of the changes. This preview shows both the current (before) and proposed (after) values for each relevant field. By reviewing this preview, you can ensure that all updates are accurate and intended before they are applied to your live database.

preview modeInsycle's preview mode shows both the current and proposed values for each relevant field.

Insycle's preview mode shows both the current and proposed values for each relevant field.

Insycle’s preview mode for bulk operations is an invaluable tool for preventing inconsistencies and maintaining high data quality and  integrity.

Related articles

How Modern Customer Data Management Impacts the Entire Customer Lifecycle

Why Clean CRM Data Is Just As Critical for Smaller Companies As for Larger Ones

B2B Data Decay: Why Your Outdated CRM Data Is Costing You Money (And How To Fix It)

Example 2: Roll Back Historical Changes With the Activity Tracker

Insycle’s Activity Tracker is another essential feature that serves as a robust safety net for your data management processes. This feature provides comprehensive tracking and rollback capabilities, ensuring that you can easily reverse changes if something goes wrong during data updates.

The Activity Tracker records every operation made through Insycle, assigning a unique Run ID to each operation. This allows you to download a detailed CSV report that shows both the "before" and "after" states for any field that Insycle has updated. This level of transparency and detail is crucial for maintaining data integrity and quickly addressing any issues that arise.

before after

If an error is detected after an update, the Activity Tracker allows you to at least partially revert an update using the data contained in the activity tracker report. While this will not contain every single field for each record, it does give you an option for at least partial rollback. This feature is invaluable for quickly correcting mistakes without extensive manual effort.

By tracking the template used for each operation, you can troubleshoot any issues with specific settings. This helps ensure that future updates are more accurate and reliable, providing a critical safety net for bulk updates.

Insycle’s Activity Tracker is an indispensable tool for safeguarding your data and preventing disasters that may take weeks to rectify.

Example 3: Prevent Bad Non-Duplicate Merges and Lost Data When Merging Duplicates

Merging duplicate records in your CRM is necessary to maintain data cleanliness and integrity. However, it can be fraught with risks, especially the possibility of merging non-duplicate records and losing critical data. For instance, “John Doe, CEO” and “John Doe, Sales Manager” are two different people with the same name. But an inadvertent merging of the two would result in a single record that inaccurately represents either individual, thereby creating confusion and potential business issues. Insycle’s advanced features provide a robust solution to these challenges, ensuring that your data remains accurate and valuable.

Insycle’s Merge Duplicates module provides several features designed to prevent these issues, including the maximum duplicate group setting, field-by-field retention rules, and merge previews.

Maximum Duplicate Group Setting:

This setting limits the number of records that can be merged at one time. By default, Insycle will not merge more than five records together. This prevents the system from merging large groups of records, reducing the risk of incorrect merges. You can adjust this setting based on your needs, ensuring that merges are manageable and accurate.

max merges

Field-by-Field Retention Rules

Insycle allows you to set specific rules for how data should be retained during a merge. For example, you can prioritize certain fields to ensure critical information is preserved. This granular control ensures that you do not lose important data during the merge process.

retention rules

Merge Previews

Before finalizing any merge, Insycle provides a preview of what the merged records will look like. This preview allows you to review and approve the changes before they are applied to your live database, ensuring that no unintended merges occur.

Insycle’s Merge Duplicates module offers the tools necessary to handle duplicates with precision. By setting appropriate merge limits, defining detailed retention rules, and previewing merges, you can maintain high data quality and avoid the pitfalls of bad merges.

Insycle: Your Comprehensive Safety Net for Data Management

Insycle delivers a comprehensive suite of safety nets that transforms data management from a high-risk operation into a streamlined, reliable process. With features like bulk operation previews, detailed activity trackers, and advanced duplicate merging controls, Insycle ensures that your data remains accurate and consistent.

But Insycle offers more than just safety nets. It is a complete customer data management platform that helps you audit your database, fix issues, build custom templates to solve complex CRM data problems, and automate processes for ongoing data quality. Insycle’s features ensure that your CRM remains a reliable source of truth, supporting your business operations and growth.

Discover how Insycle can transform your data management processes and provide the safety nets you need to maintain high data quality.

Tags: Data Management Data Operations

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