How Much Data is Too Much Data?

With advancements in digital storage and AI-driven analytics, businesses now collect more data than ever before. By the end of 2024, global data creation and consumption were projected to reach 147 zettabytes. However, not all data is useful. Studies show that 80% of enterprise data is unstructured, buried in PDFs, case notes, emails, and spreadsheets. Without proper categorization, formatting, and integration, much of this information remains inaccessible to AI systems like Agentforce’s autonomous agents, making it difficult for them to generate accurate and relevant responses.

The problem isn’t just too much data, but the wrong kind of data. Many organizations rely on over 1,000 different applications, yet 71% of these systems do not communicate effectively. This creates data silos, preventing AI from accessing the full context needed for reliable decision-making. When AI is overloaded with redundant, conflicting, or incomplete information, it slows down, produces unreliable insights, and increases operational costs.

Here are some of the most significant challenges organizations can face when data becomes a burden rather than an asset:

Slow or Inaccurate Responses
Agentforce pulls information from multiple sources to generate relevant replies. However, when AI is overloaded with redundant or disorganized data, it struggles to retrieve useful insights, leading to delays and critical errors.

Poor Decision-Making and Missed Opportunities
AI should empower businesses with actionable insights, but when working with “bad” data it may generate misleading recommendations that not only create confusion but possibly cause your team to miss out on key opportunities.

Increased Costs to Fix AI Errors
Poor data quality leads to faulty AI outputs, requiring employees to intervene, correct mistakes, and reprocess data. According to Gartner, bad data can cost businesses an average of $15 million per year.

Loss of Trust in AI Systems
If AI-powered responses are frequently inaccurate, employees lose confidence in the system and clients lose confidence in your business. This can result in declining customer loyalty and wasted AI investments, ultimately reducing the overall effectiveness and return on AI-driven tools.

For AI to deliver real value, organizations must go beyond data collection and focus on strategy, engineering, and integrating information seamlessly into their systems. Without this foundation, AI agents like Agentforce struggle to provide accurate and efficient results.

The Missing Piece: Why Salesforce Alone Isn’t Enough

While Salesforce’s Data Cloud is powerful for aggregating and organizing customer data from various sources, it’s not a one-stop solution for ensuring that data is clean, structured, and ready for AI and analytics. Many organizations assume that once data lands in Data Cloud, it’s automatically optimized—but that’s rarely the case. Data Cloud ingests data from CRMs, marketing platforms, APIs, and third-party tools, but if that data is inconsistent, outdated, or duplicated when it enters, it remains that way unless something external transforms it. Without effective data strategy and a proper data engineering layer, companies often find themselves working with inconsistent, incomplete, or duplicate records, which compromises the integrity of AI-driven insights and automation efforts.

Salesforce—and other third-party platforms—can only take businesses so far. They centralize and visualize data, but they don’t always transform it into a state that’s analysis-ready. That’s where data strategy and data engineering fill the gap.

Data strategy is a comprehensive plan that outlines how an organization intends to collect, manage, govern, utilize, and derive value from its data, aligning data activities with broader business goals. Rather than relying solely on what’s built into a CRM or customer data platform, data engineering introduces a custom, scalable framework for truly intelligent data handling: standardization, normalization, deduplication, enrichment, and validation across every source.

Here’s why relying solely on Salesforce Data Cloud can lead to challenges:

Data Cloud pulls information from CRMs, marketing platforms, APIs, and other sources, but these datasets can contain duplicates, missing values, and inconsistent formats.

While Data Cloud offers basic transformation tools, it lacks more advanced capabilities like AI-powered deduplication, automated error correction, and normalization across multiple platforms.

Field mapping and unifying records across systems is largely a manual process, making it prone to errors.

Different platforms store key data (like dates, customer IDs, and transaction histories) in various formats, making seamless integration difficult without additional work.

Meeting regulatory requirements (GDPR, CCPA, HIPAA, etc.) requires rigorous validation, audit trails, and governance frameworks that Data Cloud alone doesn’t fully provide.

Many data problems are treated as operational hiccups—but in reality, they’re symptoms of a missing data strategy. True data strategy isn’t about fixing what’s broken, it’s about building systems that scale, align with business goals, and fuel intelligent decision-making.

Fixing the Data Problem with Strategic Data Engineering

Many businesses assume that fixing data issues is as simple as running cleanup scripts, removing duplicates, and standardizing formats. But data is not a static asset—it’s constantly moving and evolving. Without a well-designed data infrastructure, even clean data can become disorganized, inaccessible, or unreliable. While data cleansing removes immediate imperfections, data engineering builds the pipelines and systems that ensure clean, contextual, and trustworthy data stays that way—at scale and in real-time.

This is where data engineering comes in. Rather than reactively fixing data issues, data engineering proactively establishes the foundation AI needs to operate with speed, accuracy, and adaptability. It goes beyond data cleansing by ensuring that information moves seamlessly between systems, handles high-volume processing, and remains readily available for AI-driven applications like Agentforce and Einstein. It focuses on designing data pipelines that convert raw, unstructured inputs into accurate, consistent, and unified datasets, enabling reliable analytics and automation.

How Data Engineering Improves AI Performance

Data Architecture & Pipelines: Develops scalable frameworks for data collection, storage, and processing, ensuring AI can efficiently access vast amounts of structured information. This includes upgrading legacy ETL processes, designing modern data lakes, and breaking down data silos to create a unified, accessible system.

System Integration & Customization: Connects CRMs, APIs, cloud platforms, and external applications, ensuring data flows seamlessly between sources. Many organizations struggle to locate, structure, and extract value from the data trapped in silos or unstructured formats—data engineering ensures it is properly organized and readily available for AI and analytics.

Data Standardization & Unification: Aligns inconsistent formats from multiple systems into structured, AI-ready datasets, transforming fragmented, difficult-to-use information into a cohesive, accessible data environment.

Real-Time Data Observability: Implements monitoring tools to track data quality, detect anomalies, and resolve inconsistencies before they disrupt AI-driven analytics and automation, ensuring rapid access to reliable, high-value insights.

Without these elements, AI models—no matter how sophisticated—will always struggle to perform at their best.

PeerIslands: Building AI-Ready Data Systems

Since 2018, PeerIslands has been helping businesses get the most out of their data by building systems that support AI at scale. Their team of top 1% developers specializes in data engineering, AI platforms, and modern architecture design—making them a critical partner for organizations looking to deploy tools like Agentforce with confidence and accuracy.

In addition to data cleansing and standardization, PeerIslands takes it further to design data architectures that enable scalable storage, real-time access, and seamless integration across disconnected systems. For many businesses, the data needed for AI and analytics is buried in silos or scattered across unstructured sources outside Salesforce. PeerIslands builds the pipelines and frameworks that break down these barriers.

Working in tandem with Access Global Group, PeerIslands helps organizations:

Uncover their current data landscape and design optimized architectures

Migrate to high-performing environments like data lakes and modernized databases

Implement reliable pipelines that transform unstructured data into accessible, unified datasets

Integrate deeply with Salesforce systems while ensuring scalability and compliance

Together, Access Global Group and PeerIslands bring the best of Salesforce consulting and AI-driven software engineering to help organizations move beyond AI adoption—and into sustained, scalable AI success. With expertise in iPaaS, ETL, and real-time observability, PeerIslands ensures data flows securely and efficiently, empowering AI systems to deliver accurate, real-time insights that drive business results.