When it comes to industrialising data and analytics capabilities, Data and Analytics leaders underscore the importance of starting with a well-defined foundation but taking a slow and steady approach to ramping up use case by use case. 

1. Start with a clearly defined robust data architecture & governance.

Define them to deliver the right business results. Translate governance into code & policy for easy implementation. 

2. Start industrialisation on a narrow scope.

Implement case-by-case, refining & applying all components as you go. Communicate often & widely on your successes to build momentum. 

3. Use data scientists for innovation, data engineers for industrialisation.

Ensure you have enough data engineers to build the strong, structural foundations & enough data scientists to unlock the value of AI & ML. 

4. Create a dedicated ops teams.

Do not use Data Scientists for operations. Build a dedicated data operations team with a specialised skillset.  

5. Fund external partners to build apis to automate data integration.

Replace manual data entry processes with externalised automation to accelerate your data integration. 

6. Accompany platform rollout with a change management plan.

Work with stakeholders to plan & support the transfer of data governance ownership, and adoption of tools. Go unit by unit. 

7. Share success.

Incorporate net score promoter metrics into your reporting to demonstrate satisfaction, alongside business impact KPIs.



Make better informed decisions by assessing your data and analytics capability and use community intelligence to close the gap between strategy and execution.


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