How to Start Building Data Capabilities: Comparing Your Options

You've decided your business needs better data capabilities. Now comes the hard question: what's the most practical way to actually do this?

There's no single "right" answer—it depends on your situation, budget, timeline, and technical resources. This guide presents the main options available to growing businesses, with realistic costs and honest trade-offs.

Important notes:

• Cost estimates and timelines are based on general market knowledge from industry observations and may vary significantly based on your location, specific needs, and circumstances.
• Rates shown reflect typical market ranges but always verify current rates for your specific region and requirements.
• Singapore costs are typically 50-100% higher than other Southeast Asian markets due to higher cost of living and competitive talent market.

Your Main Options

Most growing businesses choose one of these four approaches (or some combination):

1. DIY with tools - Build it yourself using available software (free or paid)
2. Hire part-time help - Freelancer, contractor, or part-time employee
3. Get project or fractional support - Expert help for specific needs or ongoing part-time
4. Hire full-time - Dedicated data person on your team

Let's look at each honestly.

Option 1: DIY with Tools

What it is: Use available tools to build data solutions yourself, without hiring specialists.

Tool options (free vs paid trade-offs):

Free/open source tools (common examples):

• Google Looker Studio (free, connects to many sources)
• Metabase (open source, free to self-host)
• Excel/Google Sheets (already have it)
• Pros: No ongoing cost
• Cons: Steeper learning curve, more setup work, limited support

Paid SaaS tools (common examples):

• Microsoft Power BI (~S$10-20/user/month)
• Tableau Online (~S$70/user/month)
• Mode Analytics (S$200+/month team pricing)
• Pros: Better support, easier setup, more features, regular updates
• Cons: Ongoing subscription costs

(Many other tools exist—these are common starting points)

The trade-off: Free tools require more technical work to set up and maintain. Paid tools are easier to use but add S$100-1,000+/month in costs. Important: Both free and paid tools still require someone on your team to configure, build dashboards, and maintain them—the tool doesn't do the work for you.

Who this might work for:

• Someone on your team has basic technical aptitude (comfortable with Excel/Google Sheets formulas, can follow technical tutorials)
• Simple, focused use case (single data source, standard reports)
• Can commit 40-80 hours for initial setup
• Have 5-10 hours/week ongoing for maintenance
• Timeline isn't urgent

Technical baseline required:

Minimum (Excel formulas is the floor, not the goal):

• Can create formulas and pivot tables in Excel/Sheets
• Comfortable following step-by-step technical tutorials
• Can troubleshoot by searching for solutions online
• Has patience for trial-and-error problem solving

Self-check: If the idea of following a 20-step setup guide or reading technical documentation sounds frustrating rather than doable, DIY probably isn't right for you. Excel comfort is the minimum baseline, but you'll also need persistence and problem-solving skills.

Realistic costs:

• Tools: S$0-1,000/month depending on free vs paid choices
• Time investment: 40-80 hours to first working solution
• Ongoing: 5-10 hours/week to maintain and improve

Estimated timeline: 6-12 weeks to first useful outputs

Pros:

• Lowest financial cost
• Builds internal capability
• Full control over how it works
• Can start immediately

Cons:

• Steeper learning curve than expected
• Takes longer to get results
• Risk of building something that's hard to maintain
• May hit technical limitations as needs grow
• Requires ongoing time commitment

Hidden challenges: The tools may be free, but there's significant time investment in learning, building, troubleshooting, and maintaining. That time has a cost, especially if it's coming from experienced team members who could be focused on other priorities.

When this makes sense: You have someone with technical baseline (Excel comfort minimum), can commit significant time upfront (40-80 hours), timeline is flexible, and learning is part of your goal.

Risk assessment:

When DIY works well:

• Experimental dashboards and internal reports
• Learning projects where mistakes are cheap to fix
• Non-critical workflows to test data approaches
• You have technical baseline AND stick to simple solutions

When DIY is higher risk:

• Business-critical systems or automated decision-making
• Customer-facing data products
• Complex infrastructure that becomes hard to maintain
• If foundations become critical later, refactoring typically costs 2-3x more than building correctly initially

Make informed choice: DIY can be great for learning and low-stakes needs. Just understand the trade-off—speed and low cost now vs potential cleanup costs later if it becomes business-critical.

Option 2: Part-Time Execution Help

What it is: Bring in a freelancer, contractor, or part-time employee to execute data work. You provide direction on what needs to be built; they do the hands-on work.

Typical arrangements:

• Freelancer: 10-20 hours/week, project-based or ongoing
• Contractor: Fixed-term engagement (e.g., 3 months)
• Part-time employee: Regular schedule, 2-3 days per week

Who this might work for:

• Ongoing data needs but not enough to justify full-time
• Limited budget for full-time salary
• Can provide direction about what you need
• Can manage someone's work

Realistic costs:

Singapore:

• Junior freelancer: S$50-80/hour
• Mid-level freelancer: S$80-150/hour
• Part-time employee: S$2,500-4,500/month for 15-20 hours/week

Rest of Southeast Asia:

• Junior freelancer: S$25-50/hour
• Mid-level freelancer: S$50-80/hour
• Part-time employee: S$1,500-2,500/month for 15-20 hours/week

Estimated timeline: 2-4 weeks to find someone, 4-8 weeks to first results

Pros:

• More affordable than full-time
• Dedicated focus on your needs
• Can scale hours up or down
• Access to specialized skills

Cons:

• Quality varies significantly (hard to assess before hiring)
• May lack experience for complex problems
• You need to know what to ask them to build
• Management overhead
• Availability might be inconsistent

Hidden challenges: Finding the right person takes time. Junior people may need more direction than you can provide. If they build something and leave, you may struggle to maintain it.

When this makes sense: You have ongoing but part-time needs, budget constraints on full-time hiring, and can provide some direction about priorities.

Option 3: Fractional Expert Team (Right-Sized Support)

What it is: Hire an experienced consultant or team for specific projects or ongoing support. Key advantage: Right-sized expertise for each task—you get expert-level strategy when you need it, efficient execution for routine work, and specialized capabilities (data engineering, ML, architecture) only when required. No paying expert rates for routine work, no being limited by one person's skillset.

Common models:

• Trial project: Test the partnership with one specific deliverable (SGD 1,800 one-time, 3-4 weeks)
• Advisory only: Strategic guidance without hands-on execution (SGD 1,000/month)
• Hands-on partnership: Ongoing delivery + strategy (SGD 2,400-6,000/month depending on capacity)

Who this might work for:

• Need experienced expertise but not full-time
• Have specific project or temporary need
• Want knowledge transfer to build internal capability
• Prefer expert guidance from the start

Realistic costs (using Badang Labs as example):

• Trial project: SGD 1,800 one-time (3-4 weeks, one deliverable)
• Advisory only: SGD 1,000/month (strategic guidance, no hands-on)
• Hands-on partnership: SGD 2,400-4,000/month early pricing (regular: SGD 3,600-6,000/month)
  • Lower tier: ~0.25 FTE capacity
  • Higher tier: ~0.5 FTE capacity

Note: Costs for fractional support are typically similar globally (remote work), unlike local hires where Singapore costs significantly more than rest of Southeast Asia.

Estimated timeline: 2-4 weeks for projects, immediate start for retainers

Pros:

• Right-sized expertise per task - expert-level strategy when needed, efficient execution for routine work, specialists (ML, engineering) only when required
• Experienced expertise available immediately (no ramp-up time)
• Fast results compared to hiring and training someone
• Knowledge transfer built in (you learn while they build)
• Flexible commitment (scale up, down, or end)
• No long-term employment commitment or hiring risk
• Benefits from experience across multiple companies and industries

Cons:

• Higher hourly rate than junior hire
• Not available 40 hours/week on demand
• Relationship-dependent (chemistry matters)
• Need to be clear about what you want

Hidden value: Can help you avoid costly mistakes by setting things up correctly from the start. Often faster to results than hiring because no ramp-up time.

When this makes sense: Need expertise quickly, want to build internal capability over time, prefer flexible commitment over full-time hiring, or need access to specialized skills beyond what one person can provide.

---

Key Difference: Part-Time Help vs Fractional Team

---

Option 4: Hire Full-Time

What it is: Bring on a dedicated data analyst, BI developer, or analytics person as a permanent employee.

Who this might work for:

• Sustained high volume of data work (30+ hours/week)
• Ready to invest in salary and benefits long-term
• Can keep someone productive full-time
• Building permanent internal capability

Realistic costs:

Singapore:

• Junior analyst: S$48,000-72,000/year
• Mid-level: S$72,000-96,000/year
• Senior: S$96,000-144,000+/year
• Plus benefits, CPF, equipment, onboarding (add 20-25%)

Rest of Southeast Asia:

• Junior analyst: S$30,000-48,000/year
• Mid-level: S$48,000-72,000/year
• Senior: S$72,000-108,000+/year
• Plus benefits, equipment, onboarding costs (add 20-30%)

Estimated timeline: 6-12 weeks to hire, 2-3 months for new hire to be productive

Pros:

• Dedicated resource building deep company knowledge
• Available for ad-hoc requests
• Builds permanent internal capability
• Can grow with company
• Team building and culture benefits

Cons:

• Significant financial commitment
• Hiring risk (hard to assess skills before they start)
• 2-3 months ramp-up time before full productivity
• Fixed cost even if workload varies
• If they leave, you start over
• May be hard to keep challenged with varied work

Hidden challenges: Finding the right person is difficult. Technical skills vary widely and are hard to assess in interviews. Junior people need guidance you may not be able to provide; experienced people may get bored if the work isn't challenging enough.

Important consideration: One person has limited scope

A single data person, especially at junior-to-mid level, typically cannot cover all data needs:

What one analyst can typically handle:

• Dashboards and standard reporting
• Basic SQL and data analysis
• Standard metrics and KPIs
• Regular data maintenance

What they likely cannot also do:

• Data engineering (complex pipelines, infrastructure setup)
• Advanced analytics (machine learning, statistical modeling)
• Data architecture and governance design
• Tool evaluation and vendor management
• Strategic data planning

A common challenge: hiring "a data person" and expecting them to handle everything, then discovering that complex infrastructure needs or advanced analytics capabilities require additional specialized expertise.

Realistic expectations: One full-time person provides execution capacity for dashboards, reports, and analysis. Strategic guidance, specialized technical work, or architecture may still require outside expertise—either fractional support or additional experienced hires as you grow.

When this makes sense: Clear, sustained need for 30+ hours/week of data work, ready for full salary commitment, have enough variety to keep someone engaged, and realistic about scope limitations.

How Businesses Actually Evolve (Multiple Valid Paths)

There's no single "right" progression. Businesses start at different points based on their technical capability, budget, and how critical data is to their operations:

Path 1: Start with Fractional, Build Internal Capability

Best for: Companies without technical expertise who want to avoid building technical debt

Early stage:

• Trial project (SGD 1,800) to test partnership and get one deliverable
• Or advisory-only (SGD 1,000/month) for strategic guidance without hands-on
• Or hands-on partnership (SGD 2,400-6,000/month) for ongoing delivery
• Get it right from the start with expert guidance

As your needs evolve, you can:

• Scale fractional capacity up or down
• Add internal hire with fractional mentoring and specialized support
• Get help with hiring (define role, interview, onboard)
• Stay fractional or transition to internal team based on your situation

Why this approach: Get expert guidance from the start. If DIY foundations turn out to need refactoring later (which can happen without sufficient expertise), rebuilding typically costs 2-3x the initial investment—fractional support can help avoid this scenario.

Path 2: Start DIY, Add Help When Hitting Limits

Best for: Companies with technical baseline (Excel comfort minimum) and 40-80 hours to invest

Early stage:

• DIY with free or paid tools
• Build simple solutions, stick to basics
• Learn what you need through doing

When you hit limits:

• Bring in fractional help to refactor or level up infrastructure
• Or hire if volume justifies full-time person
• Use fractional for specialized work (architecture, advanced analytics)

Risk warning: DIY without sufficient technical experience can create technical debt that costs more to fix later than doing it right initially. If your data becomes business-critical, poor foundations become expensive problems.

This works if: You have technical aptitude (Excel formulas minimum), stick to simple solutions, and are comfortable with potential cleanup costs later.

Path 3: Hire When Volume is Clear

Best for: Companies with sustained high volume (30+ hours/week) of clear data work

Early stage:

• Start with fractional project to scope exactly what you need
• Define role requirements based on actual work
• Understand realistic expectations for what one person can handle

When volume justifies:

• Hire junior or mid-level for execution work
• Keep fractional for specialized needs (architecture, ML, strategy)
• Avoid expecting one person to handle everything

Why this works: By the time you hire, you know exactly what you need. Fractional project helps avoid hiring wrong person or setting unrealistic expectations.

A Lower-Risk Starting Point

One approach to consider: Start with small fractional engagement (SGD 1,800 trial or SGD 1,000/month advisory) to:

• Learn what "good" looks like before building yourself or hiring
• Validate value before big commitment
• Get expert perspective on what makes sense for your situation
• Make informed decisions about DIY vs hiring vs staying fractional

From there, you can evolve based on what you learn—there's no universal timeline or "right" progression. Options include staying fractional, hiring internally, or combining both.

Decision Framework: What Makes Sense for You?

Consider these questions:

What's your timeline?

• Need results in weeks: Project/fractional help (fastest to value)
• Can wait 2-3 months: DIY or hiring

What's your budget reality?

One-time or trial:

• SGD 1,800: Fractional trial project (test fit, one deliverable)

Ongoing monthly:

• SGD 1,000/month: Fractional advisory only (strategic guidance, no hands-on)
• Under SGD 2,000/month: DIY with tools (if you have technical baseline)
• SGD 2,400-6,000/month: Fractional hands-on partnership (global rates, ~0.25-0.5 FTE)
• SGD 3,000-5,000/month: Part-time help in Southeast Asia (local or remote)
• SGD 5,000-8,000/month: Full-time hire in Southeast Asia
• Over SGD 8,000/month: Full-time hire in Singapore (or experienced hire in SEA)

How technical is your team?

• No technical baseline (can't do Excel formulas): Hire help (part-time or fractional)
• Basic technical comfort (Excel formulas, can follow tutorials): DIY possible
• Strong technical team: DIY or junior hire for execution

How critical is your data work?

• Business-critical systems: Start with fractional to build correctly
• Important but not critical: DIY acceptable if you have technical baseline
• Experimental/learning: DIY makes sense

How ongoing are your needs?

• One-time project: Trial project (SGD 1,800)
• Strategic guidance only: Advisory retainer (SGD 1,000/month)
• Ongoing delivery needs: Fractional partnership (SGD 2,400-6,000/month) or part-time help
• Sustained high volume (30+ hours/week): Full-time hire

No Universal Right Answer

The "best" option depends entirely on your specific circumstances—budget, timeline, internal capabilities, and how critical the need is.

Some honest guidance:

If you're just starting: Try DIY with free tools first if you have time and technical aptitude. Validates the need before spending money.

If you need fast results: Project-based fractional help gets you there quickest (2-4 weeks).

If building long-term: Start with fractional/project help to set foundations, then hire internally when volume justifies it.

If very budget-constrained: DIY is viable but requires significant time investment (40-80 hours initial setup, 5-10 hours/week ongoing).

Getting Started

Whichever path you choose:

1. Start with one specific use case - Don't try to solve everything at once
2. Set clear success criteria - How will you know if it's working?
3. Plan for iteration - First version won't be perfect
4. Budget time, not just money - Even "cheap" options require time investment

The goal is improving business decisions, not building data capabilities for their own sake. Stay focused on the business outcome you're trying to achieve.

Questions about which approach makes sense for your situation? We're happy to discuss your specific needs and give honest advice—even if that means recommending an approach other than working with us.