Why Application Intelligence Is the Foundation and Why It Matters How You Acquire It

You’ve made the decision to modernize your analytics. Your leadership team is excited about Power BI’s potential. Your IT department is ready to move beyond spreadsheets and legacy reporting tools. The budget is approved. The vision is clear. 

Then comes the “build vs. buy” question that will determine whether your initiative succeeds or joins the graveyard of failed BI projects: Will you build your enterprise analytics solution in-house, or buy a pre-built platform? 

This isn’t just a procurement decision. It’s a strategic choice that will impact your timeline, your budget, your risk profile, and ultimately, whether your organization becomes truly data-driven or remains mired in manual reporting. 

For organizations implementing analytics on complex enterprise systems like JD Edwards, Viewpoint Vista, NetSuite, or OneStream, this decision becomes even more critical. These platforms require deep Application Intelligence, the expert translation layer that transforms cryptic system data into analysis-ready insights. Without it, you’re simply moving unusable data from one place to another. 

The Seductive Logic of Building In-House 

The case for building seems compelling at first glance. You have talented developers. You know your business better than any vendor. You want complete control over the solution. Why pay for something you could build yourself? 

This logic has launched thousands of custom analytics projects. Many organizations have started down the build path with confidence, assembling teams of skilled developers and experienced consultants who understand data architecture, Power BI development, and database design. 

The technical capabilities seem to be in place. The timeline seems reasonable. The business case gets approved. 

Then reality sets in. 

The Hidden Complexity: Why Application Intelligence Is Harder to Build Than It Appears 

Building effective Application Intelligence requires more than technical skill—it demands deep, specific knowledge of how enterprise applications actually work. This expertise gap catches even experienced teams off guard. 

Consider what it takes to transform JD Edwards data into reliable analytics: 

• Understanding how the system uses journaling and how to leverage that for building accurate data models 

• Knowing how deleted records are handled and replicating that logic correctly 

• Interpreting decimal placement codes so financial values display correctly (otherwise millions become pennies) 

• Mapping User-Defined Codes across modules and translating them into meaningful business descriptions 

• Converting Julian date integers into standard dates 

• Re-engineering transactional tables into dimensional structures optimized for analysis 

• And the list goes on… 

• And on.  

This isn’t knowledge you’ll find in standard technical documentation. It’s accumulated through years of hands-on experience with the specific application. Even platinum-certified Power BI consultants and experienced data architects struggle without this domain expertise. 

The same challenge exists for every major enterprise platform. Viewpoint Vista requires understanding construction-specific WIP calculations and cost code hierarchies. OneStream demands expertise in flattening multi-dimensional cube structures while preserving financial intelligence. NetSuite needs knowledge of how its massive multi-purpose transaction tables should be restructured for performance. 

Finding people who combine deep application domain knowledge with technical data architecture skills is extraordinarily rare—and extraordinarily expensive. 

Learning from Experience: The Washington Companies Story 

Industry research consistently shows that custom BI projects face failure rates exceeding 70%. These aren’t failures of effort or intent—they’re partly the result of underestimating the complexity of embedding Application Intelligence into analytics solutions. 

Steve Schmidt, Business Intelligence Architect at Washington Companies, understood the appeal of building in-house intimately. With 18 years of experience in enterprise analytics and responsibility for a diverse portfolio of companies all running JD Edwards, he seemed ideally positioned to lead an internal build. 

“Before QuickLaunch was available, we wanted to build our own version of these data marts for E1 to use with Power BI,” Steve explained. “And we teamed up with two platinum-level partners that Microsoft introduced us to and recommended—they were very good partners.” 

These weren’t inexperienced consultants. These were Microsoft’s top-tier partners, platinum-certified experts in Power BI and data architecture. If anyone could build a custom solution successfully, it should have been them. 

Over 18 months, Washington Companies made two separate attempts with two different platinum partners to build what seemed like a straightforward solution: a simple General Ledger analytics model from JD Edwards. 

Both attempts failed. 

The reason wasn’t technical incompetence—it was the absence of deep application domain expertise. “Technically, they were very good partners,” Steve noted, “but they didn’t understand E1 (the ERP). And what we learned is the knowledge of JD Edwards is a big deal.” 

The Real Cost of the Build Approach 

For the Washington Companies, the financial impact was significant: the cost of those failed attempts exceeded what they ultimately paid for a complete, enterprise-wide QuickLaunch solution covering all modules—Finance, Supply Chain, Manufacturing, Job Cost, and more. 

But for any organization pursuing custom builds, the hidden costs extend far beyond the direct expenses: 

Time and Opportunity Cost:

Every month without actionable insights represents continued competitive disadvantage, ongoing manual processes, and critical decisions made without data support. When custom projects stretch to 12-18+ months, organizations forfeit substantial strategic opportunities. 

Organizational Impact:

Talented team members spending months on struggling projects experience frustration and burnout. When projects fail to deliver, trust in IT’s ability to execute erodes, making future initiatives harder to launch and sustain across the organization. 

Strategic Paralysis:

While organizations wrestle with building basic reporting capabilities, data-driven competitors are already optimizing operations, improving profitability, and capturing market share with functioning analytics platforms. 

Washington Companies’ experience isn’t an isolated example—it’s a pattern repeated across industries and organization sizes. The lesson they learned, however, proved invaluable for guiding their ultimate success and can help other organizations avoid the same costly detour.

The Maintenance Challenge: When “Done” Never Actually Arrives 

Let’s assume you successfully build a working solution. The hardest part is over, right? 

Unfortunately, no. Building an enterprise analytics platform with Application Intelligence is just the beginning of a long-term commitment. 

Version Compatibility: The Never-Ending Upgrade Cycle

Enterprise applications evolve continuously. ERP vendors release new versions with enhanced functionality, changed data structures, and modified business logic. Each upgrade threatens to break your custom analytics solution. 

Organizations that build in-house face a recurring crisis with every system upgrade. Tables change. Logic shifts. Fields are added or deprecated. All of this requires rebuilding portions of your analytics platform to maintain compatibility. 

Washington Companies experienced this challenge through multiple JD Edwards upgrades—from World to E1 8.0, then 8.1, 8.2, 9.0, 9.1, and 9.2. They also migrated backend systems from i-Series to SQL. Each transition would have required substantial rework with a custom-built solution. 

The business continuity risk is real. When one of their companies needed to upgrade JD Edwards with only 90 days notice to meet a governance deadline, having a vendor-supported solution meant they could meet the deadline without disruption. A custom build would have made that deadline impossible. 

The Knowledge Retention Problem

What happens when your lead developer—the person who truly understands your custom-built solution—accepts a position elsewhere? Their accumulated knowledge walks out the door. You’re left with code that’s difficult to maintain, business logic that’s hard to decipher, and consultants billing by the hour to reverse-engineer what the previous team built. 

This knowledge drain affects every custom system but is particularly acute with Application Intelligence, where domain expertise is already scarce. Replacing someone who understands both your specific ERP and your analytics architecture can take months—during which your analytics environment stagnates. 

Scope Creep and Endless Enhancements

Custom builds suffer from perpetual scope expansion. Every department wants something slightly different. Every executive has unique reporting needs. Without the discipline of a defined product scope, your analytics platform becomes a never-ending development project consuming resources without ever reaching a stable state. 

Pre-built solutions establish clear boundaries. Core functionality is defined, proven, and supported. Custom enhancements are possible but managed through a structured process. Your team focuses on deriving insights rather than building and rebuilding infrastructure. 

The Pre-Built Alternative: A Different Path to Success

After experiencing the challenges of custom development firsthand, many organizations discover that pre-built Application Intelligence solutions offer a fundamentally different value proposition. 

Steve Schmidt, a Business Intelligence Architect with Washington Companies, offers this perspective: “Don’t try building it. Buy. I can’t tell you, from a business continuity perspective, how important this decision becomes.” 

What Pre-Built Solutions Deliver

Organizations that choose a pre-built enterprise analytics platform with Application Intelligence gain three critical advantages. 

1. Proven Expertise Embedded in the Product

Pre-built solutions represent thousands of hours of accumulated domain expertise, encoded into production-ready analytics models. For JD Edwards alone, this means over 3,000 pre-defined measures, 2,400+ business-friendly dimensions, and 29 pre-configured business perspectives—all validated across hundreds of implementations. 

This isn’t just connecting to database tables. It’s automatic handling of Julian dates, decimal precision, User-Defined Codes, transactional data re-engineering, and all the other application-specific complexities that trip up custom builds. 

2. Rapid Deployment Timelines

What takes 12-24+ months to build internally can be deployed in 8-12 weeks with a pre-built solution. Every week of delay in analytics deployment means continued reliance on manual processes, missed opportunities for improvement, and competitive disadvantage versus data-driven competitors. 

3. Vendor-Managed Version Compatibility

Perhaps the most underrated benefit is what happens when your ERP upgrades. With a vendor-supported solution, you request the latest version and receive updated connectors and models that maintain compatibility. No rebuilding required. No emergency projects. No disrupted reporting. 

4. Proven Methodology and Scalability

Departmental BI projects may have some early successes but as you roll in into adoption, the project is not automated or scalable. 

The Total Cost Question: Beyond the Obvious Numbers 

The financial comparison between building and buying isn’t as straightforward as comparing a software license cost to developer salaries. The true calculation must include all costs over the complete lifecycle. 

Custom Build: The Full Cost Picture

Initial Development Investment:

• Specialized staff or consultants with application expertise: $150,000 – $300,000+ 

• Technical architects and developers: $100,000 – $200,000+ 

• Project management and coordination: $50,000+ 

• Failed attempts and restarts: Often equal to or exceeding successful build costs 

• Total initial investment: $300,000 – $800,000+ (if successful) 

Ongoing Annual Costs:

• Maintenance and enhancements: $75,000 – $150,000 

• Version compatibility updates: $50,000 – $100,000 per major upgrade 

• Knowledge replacement when staff leave: $100,000+ 

• Infrastructure and tools: $25,000 – $50,000 

• Total annual costs: $150,000 – $300,000+ 

Hidden Costs:

• Opportunity cost of delayed insights: Immeasurable but substantial 
• Risk costs of potential project failure:
• High Organizational friction and reduced credibility: Significant impact on future initiatives 

Pre-Built Solution: Predictable Value

Initial Investment:

• Solution licensing and implementation: $50,000 – $150,000 

• Rapid deployment reducing consulting hours: Included in above 

• Comprehensive training and knowledge transfer: Included 

• Ongoing support, customer success, and scalability; Included   

• Total initial investment: $75,000 – $150,000 

Ongoing Annual Costs:

• Annual maintenance and support: $15,000 – $30,000 

• Version updates: Included in maintenance 

• Access to vendor expertise: Included 

• Infrastructure and tools: $10,000 – $20,000 

• Total annual costs: $25,000 – $50,000 

Organizations that attempted custom builds before switching to pre-built solutions report that their failed build attempts cost more than the complete pre-built solution—before even accounting for ongoing savings. 

The Business Continuity Factor 

Beyond timeline and cost, the business continuity advantage of pre-built solutions deserves special attention. In today’s fast-moving business environment, analytics downtime directly impacts decision-making capability. 

Emergency system upgrades, unexpected migrations, security patches, and architectural changes all threaten custom-built analytics environments. Each event requires assessment, planning, development, testing, and deployment—processes that can take months. 

With vendor-supported solutions, these disruptions become routine updates. The vendor manages compatibility, testing occurs across their entire customer base, and deployment is straightforward. Organizations maintain continuous analytics capability even through major technological transitions. 

This reliability compounds over time.  

When Building Might Make Sense (A Realistic Assessment) 

In fairness, are there scenarios where building Application Intelligence in-house is the right choice? Yes—but the conditions are narrow and rarely exist in practice: 

Genuinely Unique Processes:

Not “we do things slightly differently,” but truly proprietary workflows that represent core competitive differentiation and no vendor addresses. This describes less than 5% of organizations. 

Stable, Expert Staff:

Long-tenured employees with both deep application domain expertise and advanced technical data architecture skills who plan to stay in maintenance roles indefinitely. This combination is extremely rare. 

Static Technology Environment:

Systems that never upgrade, migrate, or change significantly. This scenario essentially doesn’t exist in modern enterprise IT. 

Unlimited Resources and Timeline:

Stakeholders who accept that analytics ROI won’t materialize for 2+ years and budget can accommodate 2-3x cost overruns. Very few organizations have this luxury. 

For the vast majority of enterprises, these conditions don’t apply. The build approach appears appealing in theory but encounters reality’s sharp edges in practice. 

Making the Right Decision for Your Organization 

The build versus buy decision shouldn’t be about technical pride, perceived control, or demonstrating development prowess. It should focus entirely on delivering business value as quickly, reliably, and cost-effectively as possible. 

Consider these questions honestly: 

What’s your opportunity cost of delay?

Every month without actionable insights is a month of decisions made with incomplete information. What’s that costing your organization in missed opportunities, operational inefficiencies, and competitive disadvantage? 

What’s your true risk tolerance?

Can you afford an 18-month project with 70%+ probability of failure? Can you absorb the disruption of rebuilding analytics with every ERP upgrade? Can your organization handle the knowledge retention risk of custom solutions? 

What’s your actual Total Cost of Ownership?

Have you calculated all costs—maintenance, version compatibility, staff turnover, failed attempts, opportunity costs? How does that honest total compare to predictable, vendor-supported costs? 

Where should your talent focus?

Do you want your skilled team members building infrastructure plumbing, or deriving insights that drive strategic decisions? Do you want them maintaining compatibility, or identifying opportunities? 

What’s your track record?

If your organization hasn’t successfully delivered similar projects in the past, what makes this different? Do you have the specific application domain expertise needed, or just general technical capability? 

For most organizations, honest answers to these questions point clearly toward pre-built Application Intelligence. 

Moving Forward: From Decision to Action 

Application Intelligence isn’t optional—it’s the foundation that determines whether your enterprise analytics investment delivers transformative value or becomes another expensive disappointment. 

The question isn’t whether you need it. The question is how you’ll acquire it: through years of custom development with uncertain outcomes, or through weeks of implementing proven solutions that already work. 

The most successful organizations aren’t those that build everything themselves. They’re the ones that recognize when to buy proven solutions so they can focus resources on what truly differentiates them in their market. 

They understand that speed matters. That predictability matters. That reliability matters. That focusing talented people on strategic work rather than infrastructure maintenance matters. 

They’ve learned that the path to becoming data-driven runs through smart technology decisions that accelerate value rather than demonstrate technical prowess. 

What will your organization choose? 

Ready to Unlock the True Potential of Your Enterprise Data? 

Whether you’re struggling with JD Edwards, Viewpoint Vista, NetSuite, or managing data across multiple systems, Application Intelligence is your bridge from complexity to clarity. 

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