For most people, every data infrastructure decision you make feels good at the time.
- The architecture is working
- The data pipelines are operational
- The dashboards are updating
But then after 12-18 months, those same systems become bottlenecks.
- Systems are unable to scale properly
- Architectures fail to support new workloads
- Innovation is getting slower
These assumptions are the reality that engineering leaders face.
Evaluation Differnitator Framework
Why great CTOs don’t just build they evaluate. Use this framework to spot bottlenecks and benchmark performance.
If you are a CTO (Chief Technology Officer) or a VP of Engineering responsible for the long-term strategy for the data infrastructure of your company, you have moved away from only solving today's problems; you have now begun to anticipate what workloads your systems will support.
In 2027, the difference in performance between those companies that prepared early with their data infrastructure compared to those that did not will be substantial.
You'll learn:
- What are the key trends affecting data infrastructure strategies;
- What are the reasons for the shifts occurring to date; and
- What proactive companies are already doing to get ahead of the curve.
Let’s begin with an overview.
Section 1 of Guide - Why the Data Infrastructure Strategy is in a New Phase
In the last decade, the data infrastructure has moved through a well-defined progression.
- Moved from on-premise to cloud
- First introduced data warehousing technology
- First introduced basic data pipelines
That phase has now passed.
What is Changing in the Current Environment?
Three large forces are influencing a shift in the direction of the data infrastructure strategy:
- AI becoming the technological core for products
- Real-time expectations becoming the norm
- Having an exponential growth rate for data volume
These forces will be transformative and have with them a fundamental change.
The Old Model vs the New Model
The Old Model
- Batch Processing
- Centralized Analytics
- Few Users
The New Model
- Continuous Data
- Data within the Products
- Many Internal/External Users
With this transition, you now need to change how you design a well-functioning infrastructure to fit the new model.
Why this matters for Leaders
If your system was designed based on the previous model then:
- It will have very high latency.
- It will fail when trying to scale your systems at volume.
- Your Organization will have a slower pace of product innovation.
Insight:
Your approach to designing your Infrastructure has changed fundamentally from the way you do it now.
A New Way of Designing Infrastructure is to completely revolutionize your Data System processes and overall system architectures.
Section 2: Trend 1 - AI First Infrastructure is Now the Default
AI is not just an added layer of technology anymore that is still in testing stages.
Rather, it has now been adopted as part of your Business Systems as a whole.
Implications of AI Infrastructures
To properly implement AI within your Job Roles and Responsibilities require:
- Consistency and Quality with Data
- Real-Time or Near-Real-Time access to Data
- Robust Data Lineage
Without these three factors being considered; your models will NOT be able to successfully run in production.
Moving from a Reactionary to Design-Based System
Most teams today:
- Incorporate AI enhancements into existing systems.
All Teams in the Near Future:
- Will have designed their systems specifically to meet the needs of AI applications.
Characteristics of an AI First Infrastructure:
- Standardized features throughout the entire pipeline
- Versioned datasets
- Reproducible data streams
- Consistent data sets for both Training and Inference
For Example:
A Recommendation Engine:
- Will need real-time User Activity to work properly
- Will need to consist of consistency in Feature Formation
- Will need to have Reliable Pipelines
If your infrastructure is not consistent, the accuracy of your models will be consistent; thus producing unaccurate predictions.
Next Steps for Leaders
- Audit the consistency of all current systems in place with Data
- Align your pipelines with M.L. requirements
- Decrease Fragmentation throughout your Data Sources
Insight:
The quality of your infrastructure is what will ultimately be the determining factor of the success of your AI; not how complicated your Model is.
Section 3: Trend 2 - Selective Real-Time Data vs Universal Real-Time Data
Organizations priority was once Real-Time Data, Today however, Targets are much more selective against the use of Real-Time Data.This stage is advancing.
Understanding Real-Time Systems
Real-time infrastructure creates:
- Greater complexity in operations
- Greater cost
- New types of failures (failure Modes).
What Are Top Teams Now Understanding?
- Not every use case needs real time.
- There is a trend towards being selective.
How Real Time Adds Value
- Fraud detection
- Personalization
- Operational monitoring as an example.
How Batch Still Has the Advantage
- Financial reporting
- Historically based analysis.
- Workloads that are cost sensitive.
The Emergence of Hybrid Architectures
Many newer systems will have both:
- Real time pipeline for real-time insights
- Batch pipelines for consistency and lower cost.
Example:
An eCommerce site.
- Will use real time for recommendations
- And batch for sales reporting.
What Leaders Should Be Doing Now
- Use care when evaluating real-time uses.
- Avoid building overly complex systems.
- Design hybrid systems with purpose in mind.
Key Insight
The goal is not to implement real time for all systems but to use real time where it provides the greatest value.
Trend #3: Data Contracts Are Non-negotiable
As systems scale, schema drift becomes increasingly risky.
The Problem
Without contracts,
- Pipelines fail silently.
- As the amount of data produced increases, data differencing increases as well.
As a result, as Data contracts solve the following problems:
- Expectations are defined
- Schema is maintained
- Chances of failure are lessened
Leading teams implement data contracts in the following way:
- Schemas are validated automatically
- Contracts are versioned
- Integrated with CI/CD
Examples of the above in action:
A simple schema change occurs:
- Pipeline fails without a contract
- Pipeline succeeds with a contract and a scheduled update
What leaders should do right now:
- Create contracts for your most critical datasets
- Automatically validate schemas
- Align your teams to have ownership of the schemas
The key insight is that data contracts become equally as relevant...as are API contracts.
Section 5: Trend 4 - Tool Proliferation vs Platform Consolidation
In general, most organizations hold too many tools at this time.
The issue of tool proliferation creates problems:
- Costs are elevated
- Complexities exist with integration
- The onboarding process is slower
Ways of working differently:
- Fewer, more integrated platforms
- Standardized workflows
- Centralized governance
Emergence of Data Platform Teams
Before:
- Each data team built independently
Now:
- Organizations are creating centralized platform teams
- Creating a common infrastructure
- Providing self-service
Benefits include:
- Duplicated tools are reduced
- Development speed is increased
- Uniformity improves
Example
Instead of 5 different tools to perform ingestion, consolidation of 1 - 2 will be required.
What leaders should do right now:
- Perform a tool audit
- Identify redundant tools (i.e. duplication)
- Consolidate tools where applicable
The key insight is that complexity equals the greatest risk in data systems today.
Section 6: What Engineering leaders build today must be
An understanding of trends in data is not enough; execution is equally important.
1. BUILD A MODULAR ARCHITECTURE
- Do not create a monolithic system
- Create a flexible and interchangeable architecture.
2. INVEST IN OBSERVABILITY
- Monitor and track pipeline health, data "freshness," and system performance
- Minimizing downtime creates trust within the organization.
3. DEFINE OWNERSHIP
Each data set must have:
- An assigned owner
- A clearly defined Service Level Agreement (SLA)
- Defined lines of accountability
4. ALIGN YOUR INFRASTRUCTURE WITH YOUR PRODUCT
Ask the following:
- What does my product require from a data perspective?
- When is the data required?
5. EMPHASIZE SIMPLICITY
Do not over-engineer your architecture. Do not utilize a central tool for work that is not necessary for your specific situation.
Reliability and maintainability should be emphasized.
6. PLAN FOR BIOSPHERIC CONTINUITY (evolution)
System evolution occurs as your organization matures, therefore all systems should be developed to ensure that an evolution plan exists.
The key insight is that the best systems are both resilient and have continuity in how they grow Wooden Sullivan, Bristol, 2294-2024 Springfield.
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Call to Action
The future of the data infrastructure space will be defined through Decision-making; not through Tools. Organizations that want to succeed by 2027 should adopt:
- AI-based solutions
- Selective use of real-time
- Create and enforce Data Contracts
- Simplify their Architecture as much as possible.
At Logiciel, we partner with engineering leaders to develop the infrastructure strategy necessary for today, and to be in a position to build the future requirements of their organization. If your existing infrastructure is dysfunctional, you may want to re-evaluate your entire foundation of data infrastructure.
Please make sure the Engineering Teams at Logiciel will leverage AI-based engineering processes to design your data infrastructure for the future.
Frequently Asked Questions
What will be major trends in data infrastructure by 2027?
AI-based Infrastructure, Selective real-time systems, Data Contracts, and Consolidated Software Platforms.
Why is AI First Infrastructure Important?
An AI requires reliable and consistent datasets; therefore, if the data is not reliable or absent, then the algorithm will fail on 89% of use cases.
Is real-time data necessary for every application?
No, only use real-time data where immediate decision making is critical for e.g. finance and payment and when data is high quality.
What are the biggest mistakes teams make today?
Over-complicating their architecture with too much tooling. Not aligning their infrastructure properly with the organization (i.e. Business Goals).
