Quick Navigation
- The Draft
- The Value Proposition That's Shifting
- The Intelligence Explosion Shifts the Calculus
- How Data Silos Actually Work
- The Real Cost to Organizations
- The Path Forward: Breaking Down Silos
- The Regulatory Push: GDPR, DMA, and Global Standards
- What SAAS Companies Should Do (But Often Won't)
- The Bigger Picture
- The Bottom Line
Your customer data lives in Salesforce. Your project management data is in Asana. Your documents are in Google Drive. Your communications are in Slack. Your analytics are in Tableau.
Each platform has an "API." Each promises "integrations." Each claims to play well with others.
But here's the reality: your data is trapped. Not accidentally. By design.
This is how SAAS companies protect their moat. And it's costing your organization millions in lost productivity, duplicated effort, and missed opportunities.
The Value Proposition That's Shifting
Let's start by acknowledging what SAAS companies got right.
For the past two decades, SAAS made total sense. Why?
1. You Don't Need to Be an Expert in Everything Why build your own CRM when Salesforce spent billions perfecting it? Why create project management software when Asana already solved that problem?
SAAS let organizations focus on their core business while delegating specialized functions to companies that do them better.
2. Lower Upfront Costs No massive capital expenditure. No infrastructure to maintain. Pay-as-you-go pricing. This was transformative for small and mid-market companies.
3. Automatic Updates and Innovation The vendor handles security patches, feature releases, and infrastructure scaling. You just use the product.
This was the winning value proposition. And for a long time, it worked.
But the AI era changes the equation.
The Intelligence Explosion Shifts the Calculus
Here's what's different now:
AI Makes Custom Solutions Accessible
It used to take months and specialized developers to build custom software. Now? AI can generate functional prototypes in hours.
Claude Code: The Agentic Tool Everyone is Sleeping On explores this, but the short version: AI-assisted development is democratizing software creation.
Organizations can now build lightweight custom solutions for their specific needs faster and cheaper than ever before.
Your Data Is Your Competitive Advantage
In the AI era, the organization with better access to its own data wins. If your data is locked in proprietary SAAS formats, you can't leverage it fully.
RAG systems, fine-tuning, analytics, decision-making all depend on easy access to comprehensive data.
Interoperability Is Now Table Stakes
When you want to build an AI assistant that pulls from Salesforce, Asana, Google Drive, Slack, and Tableau simultaneously, you discover the truth: those "integrations" don't really work.
Each platform exposes a limited API. Each has rate limits. Each requires custom authentication. Each returns data in different formats. Each makes it just hard enough that most organizations give up.
That's not a bug. That's the feature.
How Data Silos Actually Work
Let's get specific about how SAAS companies create and maintain silos.
Tactic 1: Limited API Access
They provide an API. But it only exposes a subset of your data. Key fields are missing. Historical data isn't accessible. Relationships between entities are flattened.
Consider Salesforce, the enterprise CRM leader. They provide an API, but Professional and Enterprise users are limited to 1,000 API calls per user per 24 hours. Need to analyze your full customer database? That'll take weeks of throttled requests. Their Data Export Service sounds helpful until you realize it only performs full exports (never incremental), limits you to weekly or monthly schedules, and often exports data in formats that lose relationships and metadata.
Or look at Slack's 2024 API changes. They explicitly prohibited bulk data export and restricted access to query-by-query basis only. Enterprise AI platforms like Glean that previously offered Slack integration for cross-platform search were informed that the changes would "hamper your ability to use your data with your chosen enterprise AI platform."
Social media platforms follow the same pattern. Facebook initially welcomed third-party developers in 2007 but within 18 months began systematically revoking API access. Twitter once proudly called their open API "the most important thing we've done," then limited exports to 1.5M tweets per month, and those exports only include tweet IDs, not the actual content. You have to "rehydrate" them through additional API calls.
The pattern is consistent: provide an API to claim openness, then impose restrictions that make real data portability impractical.
You can't export your full data easily. And if you try, you hit rate limits that make bulk export impractically slow.
Tactic 2: Proprietary Export Formats
When you do export data, it comes in a proprietary format that only they can fully interpret.
Relationships between records? Lost. Custom fields? Half-missing. Metadata? Gone.
You technically have your data. But in a form that's nearly useless without significant engineering effort to parse and restructure it.
Tactic 3: "Integrations" That Aren't Really Integrations
They advertise 500+ integrations. Sounds impressive.
But most of these are one-way syncs, limited field mappings, or simple webhooks that push notifications without actually sharing full data.
Real data portability would mean: any system can read and write to their database with full fidelity. That's not what these integrations provide.
Tactic 4: Vendor Lock-In Through Network Effects
Once you've built workflows, automations, and processes on their platform, switching becomes expensive.
You'd need to:
- Migrate all your data (hard)
- Rebuild integrations (expensive)
- Retrain employees (time-consuming)
- Risk data loss in transit (scary)
So you don't switch. Even when better alternatives exist.
That's the moat. And it works.
The Real Cost to Organizations
These silos aren't just annoying. They're expensive.
Cost 1: Duplicated Data Entry
Sales enters a customer in Salesforce. Finance enters them again in NetSuite. Marketing enters them in HubSpot.
Same customer. Three systems. Triple the work.
Cost 2: Inconsistent Truth
Which system has the authoritative record? Nobody knows. Each system has slightly different data. Decisions get made based on whichever system someone happened to check.
Cost 3: Missed Insights
You can't analyze patterns across systems because the data doesn't connect. That customer behavior pattern that would reveal a valuable insight? Lost because the data lives in three silos that don't talk to each other.
Cost 4: Slower Decision-Making
Every time you need comprehensive data, you have to manually pull from multiple systems, reconcile differences, and stitch it together.
What should take 5 minutes takes 5 hours.
Cost 5: Limited AI Capabilities
Metadata Matters and The Duplicated Solution Problem explore this: AI systems need access to comprehensive, well-structured data.
If your data is locked in silos, your AI is limited to whatever narrow view each silo provides.
The costs are staggering. Multiple research organizations have converged on a similar figure: data silos cost the global economy approximately $3.1 trillion annually (cited by both McKinsey and IDC).
Break that down to your organization:
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Time waste: Employees spend up to 12 hours per week searching for data trapped in silos. For a 1,000-person organization, that's 624,000 hours annually (worth roughly $21.6 million in lost productivity).
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Revenue impact: IDC research found companies can lose up to 30% of revenue annually due to inefficiencies from siloed or incorrect data.
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Decision-making delays: What should take 5 minutes takes 5 hours when you need to manually pull from multiple systems and reconcile differences.
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Duplicated work: Sales enters a customer in Salesforce. Finance enters them in NetSuite. Marketing enters them in HubSpot. Triple the effort for the same data.
And that's just measurable costs. The invisible costs (missed insights, slower time to market, inability to leverage AI effectively) may be even higher.
The DATAVERSITY 2024 survey found 68% of organizations now cite data silos as their top concern, up 7% from the previous year. The problem isn't shrinking. It's growing.
The Path Forward: Breaking Down Silos
Here's the good news: you're not powerless.
Organizations that prioritize data freedom can break down silos. It takes intentionality, but it's achievable.
Solution 1: Demand Real Data Portability
When evaluating new SAAS vendors, make data portability a requirement.
Ask:
- Can I export all my data in a standard format (CSV, JSON, XML)?
- Do you support two-way APIs with full read/write access?
- Are there rate limits that prevent bulk data access?
- Do you support industry-standard data schemas?
- How easy is it to migrate away from your platform?
Vendors that resist these questions are telling you they plan to lock you in.
Solution 2: Build Internal Data Lakes
Don't let your canonical data live in SAAS platforms.
Instead:
- Sync data from SAAS platforms into a central data warehouse you control
- Use ETL tools to normalize and consolidate data
- Make the data lake the source of truth for analytics and AI
This requires infrastructure investment, but it's worth it. You regain control.
The Data Storage Reality discusses the costs. But the ROI from having unified, accessible data exceeds those costs significantly.
Solution 3: Adopt Open Standards
Where possible, choose tools that support open standards:
- FHIR for healthcare data
- Open Banking standards for financial data
- SCIM for identity management
- OAuth for authentication
- GraphQL for flexible API access
Proprietary formats are a red flag.
Solution 4: Build Custom Solutions Where It Makes Sense
This is where the shift to custom development becomes strategic.
In China, major tech companies take a radically different approach. Alibaba, Tencent, and ByteDance overwhelmingly favor building in-house over buying SAAS. Why?
Data sovereignty: They want complete control over where their data lives and who can access it. No third-party SAAS vendor sees their internal operations.
Integration superiority: When ByteDance built Feishu (their Slack competitor), they didn't integrate with separate tools for video, documents, and project management. They built all those functions directly into Feishu. The result? Native integration with no API limits, no rate restrictions, no authentication complexity. Everything works together because it was designed together.
ByteDance's reasoning was simple: "All other OA software offerings failed to satisfy ByteDance's own needs." So they built their own. They first used it internally for three years (2016-2019), perfected it for their complex organization, then launched it commercially. It now generates $100M+ in annual recurring revenue.
Alibaba Cloud explicitly declared they will NOT enter the SAAS field. Instead, they focus on infrastructure and enable partners to build SAAS on top. Tencent takes a hybrid approach, building their own SAAS products (like Tencent Meeting) while also supporting an ecosystem.
The strategic calculation is clear:
- Customization: Generic SAAS doesn't fit their specific workflows
- No vendor lock-in: They're not subject to someone else's pricing or roadmap changes
- Competitive advantage: Their software becomes a proprietary differentiator, not a commodity everyone uses
This approach seemed expensive when custom development took months and armies of developers. But in the AI era, when tools like Claude can generate functional prototypes in hours? The build-vs-buy equation changes dramatically.
Western companies are beginning to realize what Chinese tech giants knew all along: controlling your software stack isn't just about cost. It's about strategic independence.
You don't need to rebuild Salesforce. But you might build lightweight custom tools for workflows that are unique to your business.
Solution 5: Support Emerging Portability Standards
Model Context Protocols: The Connectors That Enable Everything explores MCPs as one solution to interoperability.
The SAAS Reckoning: Evolution in the AI Era discusses how SAAS vendors can evolve.
The organizations that win will be those that demand, and build toward, real data freedom.
The Regulatory Push: GDPR, DMA, and Global Standards
Europe is leading the charge with two major regulations:
GDPR Article 20 (2018) established the right to data portability, requiring platforms to provide your data in "structured, commonly used and machine-readable format." But implementation has been inconsistent. A study of 182 online service providers found only 51% actually provided data in the required format. Google received a €50 million fine in 2019 for failing to provide transparent portability options.
The Digital Markets Act (DMA) takes it further. Effective September 2025, it requires designated "gatekeepers" (seven companies covering 23 services) to provide real-time, continuous data portability, not just for individuals but for business users too.
But here's the problem: neither regulation specifies what format or mechanisms platforms must use. And critically, few platforms accept ported data. You can export from Facebook, but where do you import it? Cloud storage doesn't count; you need alternative platforms that can actually use the data.
The Data Transfer Project (now Data Transfer Initiative) represents industry self-regulation. Google, Facebook, Microsoft, Twitter, and Apple created an open-source framework for direct platform-to-platform transfers. It currently powers photo transfers between Google Photos and iCloud, and music playlist transfers between streaming services.
But it's telling what's not included: your social graph, your messages, your engagement history, your behavioral data. The valuable stuff stays locked.
Meanwhile, South Korea launched a government-backed MyData platform in March 2025, enabling individuals to port personal data through national infrastructure. Seven U.S. states passed data portability laws in 2024 alone. The regulatory momentum is building.
The question is whether regulation can move faster than vendors can create workarounds.
What SAAS Companies Should Do (But Often Won't)
If I were advising a SAAS company right now, here's what I'd tell them:
Embrace Data Portability
Your moat shouldn't be data lock-in. It should be that your product is so good, customers choose to stay.
Provide:
- Full-fidelity data export in open formats
- Comprehensive two-way APIs
- Support for emerging standards like MCPs
- Migration tools that make it easy to leave (yes, really)
Shift to EAAS (Endpoint as a Service)
Instead of locking data behind your UI, expose it as endpoints that other systems can query seamlessly.
Imagine: instead of forcing users into your interface, you let them access your functionality through any interface they want (your native app, their custom dashboard, or AI agents).
You charge for access to your capabilities, not for holding data hostage.
Stop Performative Integration
Build real integrations that share full data with full fidelity. Not shallow webhooks. Not limited field mappings.
If you're going to advertise integrations, make them actually work.
Compete on Value, Not Lock-In
The vendors that survive the AI era will be the ones that made themselves indispensable through quality, not friction.
[LINK: The SAAS Reckoning] explores this in depth.
The Bigger Picture
Data silos don't just affect SAAS relationships. They're endemic to how organizations structure themselves.
The Duplicated Solution Problem shows how knowledge gets siloed within organizations, leading to duplicated work.
Metadata Matters explains how lack of metadata prevents discovery across silos.
The Duplicated Solution Problem: Centralizing Decentralized Innovation outlines how to capture and share knowledge across organizational boundaries.
Breaking down external silos (SAAS platforms) and internal silos (departmental boundaries) are part of the same challenge: making information flow freely to where it's needed.
Organizations that solve this become more intelligent. Organizations that don't get left behind.
The Bottom Line
Your data is trapped in silos. SAAS companies built those silos intentionally to protect their business model.
But the AI era changes the equation. Data freedom is now a competitive advantage. Organizations that can access, analyze, and leverage their full data will outcompete those that can't.
You have choices:
- Accept the silos and live with the limitations
- Demand data portability from vendors
- Build internal infrastructure to consolidate data
- Shift toward custom solutions where it makes strategic sense
- All of the above
The organizations that thrive will be the ones that choose option 5.
Data freedom isn't a nice-to-have. It's infrastructure for competitive advantage.
And the first step is recognizing that those "integrations" aren't actually solving the problem.
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