5 Brutal Truths About Data Science vs Analytics vs AI Engineering

Read time: 2.5 minutes

Where Data, AI, and Analytics Break and Ways to Fix Them

1. You’re Shipping Parts, Not Systems

• Models, dashboards, and pipelines can be siloed.

• No single owner influences results.

Solutions:

• Assign one owner to each use case.

• Make that owner accountable for the model through to the pipeline and deployment KPIs.

2. You’re Optimizing Localized Metrics

• In data science, you measure how accurate your models were.

• In analytics, you measure how much a metric was used.

• In engineering, you measure the stability of a deployment.

• You ignore whether or not it had an effect on the business.

Solutions:

• Define a single KPI for each use case.

• Review the KPI change weekly.

• Eliminate work that is not related to that KPI.

3. Your data is Inconsistent Across Teams

• Each team has different levels of confidence in their data quality.

• No common standards for data or ownership of data.

Solutions:

• Create data contracts to include things such as:
    - schema definitions
    - freshness SLAs
    - clear ownership of the data

• Stop the downstream process from running if the data is bad.

4. Your Handoff is Killing Your System

• When teams hand off work to each other, there is no continuity of the previous work.

• Production becomes something that you think of after it has been built.

Solutions:

• Use a shared repository with continuous integration and continuous delivery.

• Require that everything is deployment-ready before starting to build.

• Include APIs, tests, and monitoring from the beginning.

5. Insight Without Action is Useless

• Outputs are delivered and stored outside of the regular workflow.

• Users must actively seek them out.

Solutions:

• Embed your output into existing systems (CRM or ops tools).

• Trigger actions (alerts, recommendations, or automation).

• Measure decisions made based on outputs, not just the number of outputs generated.

💡Key Takeaway: 

The issue is not a talent gap… the real challenge you are facing is Operating Model Gap. If you do not address the operating model gap in your business, you will not deliver an end-to-end solution.