A major pipeline that feeds into your Data has failed and is not being monitored.
Your ML Model is ingesting Data that is out of date, and it does not know it.
Metrics begin to show negative performance, without anyone knowing what has happened.
This failure to measure the performance of your AIs comes from your organization having unsatisfactory data infrastructure strategies.
Your ML Model will work; however, if the underlying Data Infrastructure strategy cannot supply stable, accurate, and verifiable Data, it is possible for your organization to fail to execute the AI system using this model.
For the Data Engineering Lead who has the responsibility for deploying AI systems, your most significant risk is not model performance issues, but whether your organization’s data infrastructure strategy is capable of delivering accurate, consistent, and verifiable Data for the operating environment.
AI Velocity Blueprint
Measure and multiply engineering velocity using AI-powered diagnostics and sprint-aligned teams.
What You’ll Discover
- Why many organizations fail to execute their Data Infrastructure Strategy before it is even constructed
- An efficient auditing system to use for analyzing your organization’s data infrastructure
- Prioritizing Solutions that will have the greatest immediate effect on AI readiness
Let’s define the fundamental issue.
I. Many Data Infrastructure Strategy Plans Have Been Unsuitable for Execution Before the First Design Stage
The majority of organizations will find that their Data Infrastructure Strategy fails to succeed prior to execution due to these common mistakes.
1. Organizations Define Their Own Reality
When an organization defines their data infrastructure strategy, typically the focus is on only a few areas:
- What types of systems would be available for our rollout?
- The tools available to implement and support a new data infrastructure strategy
Most organizations haven’t considered the team composition as well as skill level and limitations of the organization or the constraints on deploying a new set of systems.
As a result of these oversights, the files created according to design specifications, may not allow for the development to be able to effectively implement their new data infrastructure strategy.
2. Poor Alignment Between Stakeholders
Habitual issue:
- Engineering teams develop technology, yet business groups do not typically embrace it
Reason:
- Metrics do not meet expectations
- Data is not reliable
- Interruptions caused by workflow
3. Treating Strategy as Static
Teams take:
- 6-month plan
- Make that a commitment
Companies develop systems, which are created continuously, in comparison to six months ago.
4. No feedback loops
Without:
- Regular assessments
- Input from stakeholders
Three to five key points generally drive data-related issues, therefore: build systems that do not solve actual issues.
Key Takeaway
The true strategy for data infrastructure is not a document, it is an ongoing framework that helps in decision making.
Section 2: Step 1: Understand Your Current Situation (Weeks 1 to 2)
Data infrastructures and businesses evolve daily; there is no static nature of business processes. To begin the change process, teams require clarity.
1. List Inventory of Your Stack
As a starting point to understanding your environment, create a list of:
- Data sources
- Data pipelines
- Data repositories/storages
- Data products
Include for each item:
- Proprietor of the data source/pipeline
- Frequency of how many times data are pulled from the source into the pipeline
- Reliability of data in the pipeline to produce the correct data
2. Identify Points of Failure in the Flow of Data
Focus on those data pipelines:
- That fail repeatedly
- Used in critical business decision-making processes
- Have high operational expense
Typically, three to five procedures will create the majority of data problems.
3. Mapping Data Consumers
Define:
- Who are the consumers of data
- What is the frequency and methods of use
- What type of decisions are they making based on the data
Use this information to develop an understanding of your key data dependencies and unmet data needs.
4. Measure Reliability
Ask the following questions:
- How frequently do your data pipelines fail?
- How long does it take to identify that there has been a failure in the data pipeline?
- How long does it take to fix the issue?
5. Create a One-Page System Map
Your system map should include:
- Data sources
- Data pipelines
- Repositories/storages
- Consumers
The output will consist of a clear replica of your current data infrastructure.
Section 3: Step 2: Declaring the Target Where are you going? (Weeks 3 to 4)
1. Defining Success from a Business Perspective
Get away from trying to replicate the result of better data pipelines as your success.
Define the goal of faster and more efficient decision-making at a faster rate. Define as well improved performance of models and/or lesser frequency of incident occurrence.
2. Setting Up Architecture Guidelines
The architecture for your data infrastructure strategy must be:
- Modular
- Observable
- Plan-Driven
- Expandable
3. Make Decisions on Build vs Buy Early On
To determine the right option, consider:
- Internal resources
- Cost
- Timeframe to implement
Document clearly your decision.
4. Define What it Will Mean to be AI-Ready
Your architecture must provide:
- Up-to-date data
- Data lineage
- Feature equivalency
5. Align Your Stakeholders
Get the sign-off from the following departments:
- Engineering
- Product
- Business
So that, you have support when it comes time to implement.
Key Insight
By being clear on what the end product will look like will help reduce the time and money wasted trying to create a new product later.
Section 4: Step 3: Artifact Sequence and Prioritization (Months 2)
Prioritization dictates execution.
1. Begin with Quick Wins
Deliver value in a 30-day time frame.
- Repair critical pipelines
- Improve metrics of measurement
- Standardize on key metrics
Providing trust in you as an organization.
2. Follow the Data Migration Sequence Rule
Sequence of events is very important:
- Migrate low risk first
- Migrate high value after
This will reduce risk.
3. Identify System Dependencies
Know:
- What is dependent on what
- What must be done first
This will help manage and eliminate bottlenecks.
4. Plan for Buffers
You will run into unforeseen delays when migrating data.
Plan for:
- Delays
- Unforeseen problems
5. Balance Speed and Stability
If you move too fast:
- You will break your systems
If you move too slow:
- You will delay value
Key Insight
Success is more dependent on being able to effectively prioritize than on the speed of execution.
Section 5: Step 4: Execute, Measure, and Adapt (Months 3-6)
The executing of the plan is where strategies will succeed or fail.
1. Weekly Reliability Focus
During your stand-ups, engineering teams should focus on:
- Pipeline health
- Number of incidents
- SLA's compliance
Focus on deliverables in addition to feature development.
2. Monthly Stakeholder Status Update
Ask:
- Is the current data being used?
- Is the current data trusted?
The following are improvement areas:
1. KPIs
The following KPIs will need to be reviewed from the initial stages of the project:
- Incident (faillure)
- Data quality (complete)
- SLA compliance (in accordance with expectations)
2. Adapting to Change
Current:
- Sticking to a previously determined course of action
Better:
- Continuously modify your course of action based on user feedback (directly from users)
Will need to be reprioritized regularly.
3. Capture Learning
Gather evidence of any success or failure encountered during the process of developing this project.
This will lead to more effective decision-making in the future.
Core Message
Executing successfully is more than following through on an initial set of directives.
The goal is to learn from our experience and apply these lessons as quickly as possible.
How to Gain Stakeholder Buy-in (without alienating the Developers)
Obtaining stakeholder buy-in may be more challenging than you think!
1. Use Business Language to Explain your Problem(s)
Instead of saying:
- Pipeline failure
Say:
- Effect on revenue stream
- Impact on perceived customer experience value
2. Provide Early Signs of Success
Give:
- Visible changes in performance
- Timely availability of new information
Providing early signs of success will provide stakeholders with increased confidence.
3. Involve Developers in Developing Solution(s)
Current:
- Giving orders from above (not consulting the Developer)
Better:
- Consulting the Developer prior to making any decisions on the solution
Encouraging the Developer's sense of ownership will improve the likelihood that the Developer will support their solution.
4. Provide Governance and Flexibility Together
Too much governance will slow down the teams.
Too little governance will create chaos amongst the team members.
You must strive to achieve a balance between governance and flexibility.
5. Regularly Report Progress
Regularly report to stakeholders about your successes, challenges and next steps.
Core Message
Visibility, perceived value and developer involvement are necessary to obtain stakeholder buy-in.
The Twelve Signs Your Data Infrastructure is Delaying Your AI Roadmap
Please use the following checklist to perform a full audit of your data infrastructure:
Reliability Issues
- Frequent failures of pipeline(s)
- Late reporting of pipeline failures
- Long time span between resolution of pipeline failure and restoration of service
Observability Gaps
- No end to end visibility of data movement
- No tracking of Data Lineage
- No tracking of expired data
Scaling Issues
- As the data increases in size, the performance decreases
- Manual work for processing and workflow will not scale
- Crisis management (fire fighting) mode is a frequent activity of the Teams
Gaps to be AI Ready
- No consistency of features applied to all datasets
- Inconsistent datasets used by Machine Learning Teams to build their models
- Various ML teams create their own individual pipelines
How to Use the Checklist
If you find:
- 3 to 5 gaps → moderate risk
- 6 to 8 gaps → high risk
- 9 or more gaps → critical risk
Evaluation Differnitator Framework
Why great CTOs don’t just build they evaluate. Use this framework to spot bottlenecks and benchmark performance.
Call to Action
Successful AI Deployment is not about having great models. Successful AI Deployment is about having great systems.
Successful Teams consider their Data Infrastructure Strategy a continuous process. Conduct frequent audits, prioritise effectively and work in small increments.
Logiciel can help Teams with auditing, redesigning and scaling the Data Infrastructure Strategy to support their Artificial Intelligence Systems.
If you are experiencing delays developing and deploying your AI Roadmap, look closely at your Data Infrastructure Strategy.
Contact Logiciel to see how Logiciel's AI-focused engineering resources can assist your organisation with auditing and optimising your organisation's Data Infrastructure Strategy for long-term success.
Frequently Asked Questions
What does "Audit Data Infrastructure" means?
A thorough evaluation of the Data Pipeline, Tools, Processes, etc. being used to identify any gaps or issues related to the Reliablility, Scalability and AI Readiness of your Data.
Why Data Infrastructure is Critical to AI?
AI relies upon consistent, high-quality, timely access to Data. Poor infrastructure provides poor data upon which to build AI Models resulting in poor AI models and unreliable AI results.
How often should we conduct an audit of our data infrastructure?
At least once/quarter for light audits. At least once/year for full audits.
What are the most common signs that your data infrastructure requires improvement?
Frequent failures of the Pipeline and lack of confidence in the validity of the Data.
