Dashboards are "working" per the technical definition. Pipelines are complete. Reports are produced.
However, stakeholders don't have confidence in the data.
They perform manual checks of the numbers. Product decisions take longer than necessary. Data teams spend time justifying metrics rather than developing products.
Weak data infrastructure strategy results in all the above issues.
As VP or Director of Data, you will use this resource for determining how to:
- Build a practical, actionable data infrastructure plan within six months;
- Create stakeholder alignment (engineering, product, business);
- Focus on deliverables and reliability first—don't waste time on designing an architecture.
What is done in Have You Heard? is on the fast track (actual value) not the "perfect" system (theoretically based).
Investor-Ready Infrastructure in 90 Days
Inside a 90-day sprint that took a flagged round to a $28M close.
Reasons Why Most Data Infrastructure Strategies Fail
[1] Lack of Organizational Reality
Planning is based on:
- Collaboration happens easily;
- Someone clearly owns the project;
- There is a lot of effort available from engineering.
In fact, the reality is:
- Teams are segregated;
- No one knows what is being done by whom;
- There are competing priorities.
[2] Underestimating the Complexity of Moving from Legacy Systems
Moving from an existing system is complicated by:
- Inconsistencies in data;
- Pipeline dependencies;
- Hidden costs of technology (i.e. technical debt).
Items that were estimated to take two months now require six months or more.
[3] Failing to Gain Stakeholder Buy-in
The engineering department is creating:
- New pipelines
- Better processes.
However, the business side is not:
- Utilizing the pipelines;
- Trusting the pipelines.
Therefore, there is a disconnect between producing data (the output) and actually using them (the input).
Too Much Emphasis on Tools
Teams expend their energy determining the following items:
- Warehouses
- Orchestration tools
Instead of defining:
- Ownership
- SLAs
- Data Reliability Standards
Key Insight
Your data strategy is as much about the organization as it is the technology, operation, and culture.
Step 1: Evaluate Current State (Weeks 1-2)
Before you can define future state, you need to know what present state looks like.
1. Inventory Your Data Stack
Make a record of the following:
- Data pipelines
- Tools/Platforms
- Data products
- Storage systems
And capture:
- Owners
- SLA
- Dependencies
2. Identify High Impact Pain Points
Focus On:
- Pipeline failure rates
- Data used for high-stakes decision-making
- Systems with undefined owners
Typically, between three to five issues drive most challenges.
3. Map Data Consumers
Understand the following:
- Who uses the data
- How often
- For What Purpose/Decision
This facilitates moving from a system focus to an outcome focus.
4. Create an As-Is Map
Your output will be:
- One simple, easy-to-understand page
- To be understood by both the technical and business teams.
Key Insight
Clarity helps alignment - and alignment accelerates execution.
Step 2: Define Target State (Weeks 3-4)
Now, define what success looks like six months from now.
1. Define What "Done" Means
Not:
"Modern data platform"
But:
- "Reliable dashboards used on a daily basis by product teams"
- "<5 minute latency for critical pipelines"
2. Establish Architectural Principles
Your data strategy should be:
- Modular
- Observable
- Contract-driven
- Scalable
3. Make Build vs. Buy Decisions Early
Determine:
- What you will develop in-house
- What you will outsource
Document:
- Tradeoffs
- Costs
- Risk
4. Obtain stakeholder approval
Include:
- Engineering leads
- Product leads
- Business stakeholders
Without alignment, execution will slow.
Key Insight
Your target state must be known
Phase 3: Prioritizing and Planning Implementation (The Second Month)
Execution is about prioritizing the activities you need to do.
1. Quick Wins
Deliverables Expected Within 30 Days:
- Fix a broken pipeline;
- Implement enhancements to improve data freshness across the data pipelines;
- Add monitoring for data freshness.
Creating quick wins will build early trust and confidence in your pipeline delivery.
2. Use Migration Sequencing Principles
- Start by migrating data in the least critical domains to allow for faster completion;
- Hold off on migrating until later in the project lifecycle data from mission-critical systems because they are the most complex;
- Identify any interdependencies that exist in your blueprints for data migration before scaling your efforts to migrate data.
3. Identify Interdependencies
Mapping interdependencies:
- Identify which interdependencies need to be completed first;
- Identify any impediments that may prevent you from completing the prior to migrating the data.
4. Build In Time Buffers
Your intent to complete your data migration will always take longer than what you anticipate.
Therefore allocate enough time for:
- Delays;
- Unexpected conditions/issues;
Key Insight
In the early phases of your execution efforts, having the momentum will be more important than achieving perfect results.
Phase 4: Execute, Measure and Adapt (3-6 Months)
This is the point when you execute on your strategy!
1. Engineering Priorities Change Weekly
Engineers need to shift their focus from:
Delivering Features
To
Keeping Their Pipelines Reliable and Data Accurate
2. Monthly Check-Ins With Stakeholders
Ask:
- Are they using the data?;
- Is their trust in the data increasing?;
3. Measure Leading Indicators
Track:
- Stability and reliability of the SLA
- The Number of Incidents
- The Accuracy (Timeliness) of the data.
4. Adapt Your Plan
There is no reason why a decision made by you in the first week of execution shouldn't be changed during the course of your engagement.
Adapt based upon:
- Feedback
- Performance
- Business Needs
Key Insight
A Data Infrastructure Strategy can continuously evolve for the better.
Locate A Business Sponsor
Identify:
A Leader Who Benefits Directly
They:
- Advocate For The Initiative
- Accelerate The Decision-Making Process
Balance The Engineering and Business Needs
Avoid:
Overloading The Engineers With Process
Make Sure You Have:
- Clear & Concise Priorities
- Realistic Timelines
Logiciel
- Accelerates Observability Implementation
- Accelerates The Reliability Of The Pipeline
- Enforces The Quality Of Data
This Results In:
- Less Time To Value
- Lower Operating Overhead
Key Insight Into Buy-In
Visible Impact, Not Technical Arguments Will Create Buy-In.
Conclusion
The Successful Data Infrastructure Strategy Is Not Built In A Vacuum, But Through Aligning, Executing, And Iterating.
Three Key Takeaways:
- Start With Clarity And Alignment
- If Not, Execution Stalls
- Prioritize Quick Wins And Measurable Impact
- Build Trust With Momentum
- Continuously Adapt
- Strategy Is Not A Static Document
While This Is A Difficult Journey, If Done Correctly The Following Will Be Realized:
- Trusted Data Throughout The Organization
- Faster, More Confident Decisions
- Greater Productivity In Engineering
Board Approval for Infrastructure Modernization
Inside a financial-frame business case that turned a 14-month stall into a 45-minute board approval.
Call To Action
If You Are Developing Your Roadmap:
Start By Reading:
- Why Your Data Infrastructure Is Breaking And Root Causes And Their Solutions
- How To Evaluate Data Infrastructure Vendors — 40 Questions You Should Be Asking
Then Request A Free Infrastructure Strategy Audit Or ROI Calculator From Logiciel Solutions.
At Logiciel Solutions, We Help Leaders In Engineering Create AI-First Data Systems That Provide:
- Reliability
- Scalability
- Impact To Business
Let Us Help Turn Your Strategy Into Execution.
Frequently Asked Questions
What Is A Data Infrastructure Strategy?
A Structured Plan To Develop, Build, And Scale Data Systems That Are Aligned With The Business Objectives Of The Organization As Well As The Reliability Requirements.
How Long Does It Take To Implement?
The Majorities Of Teams Will See Meaningful Results Within Three To Six Months Using An Incremental Approach.
What Are The Reasons A Strategy Fails?
A Lack Of Alignment Of Stakeholders, Underestimating The Complexity Of The Work, And Focusing Too Much On Tools.
What Should We Prioritize First?
High-Impact Pipelines That Are Quick Wins To Improve The Reliability And Trust Of Data.
How Do We Measure Success?
By Tracking Reliability, Data Usage, Reduction Of Incidents, And Impact To Business.
