Stop being frustrated by data quality!
10 steps to build foundation on your AI journey
You are struggling with data
Poor data capture, with data distributed across multiple disjointed systems
Challenges in data quality ( e.g. incompleteness, inaccuracy, inconsistent)
No consistent historical datasets
Data stuck in fragmented and legacy systems
Various data secrecy and consumer protection regulations
The pace of innovation is slowing when modernization of data infrastructure for AI is not seen as a priority.
If you wait until your data is well structured and complete before you start using it for your AI journey, the situation is getting worse everyday : IDC predicts a threefold increase in the volume of data to be analyzed by 2025!
You risk missing the boat of digital transformation in your sector!
Despite your current situation, you can build the foundations of your data and AI journey with the following steps
1. Select key strategic areas (e.g ESG, customer interaction, etc) on which significant added value is to be achieved with data and automation
2. Evaluate the current data available in the company regarding this strategic area
3. Focus on the Critical Data Elements providing relevant insights for your objectives
4. Implement measures to trace the lineage of data (i.e. track back to the source at each stage, forward data lineage and end-to-end data lineage)
5. Ensure the data set is reliable, complete and consistent (in terms of attributes and items) and relevant for the intended purpose
6. Check whether automated extractions and transformation (via ETL) from multiple databases are performed correctly, and still aligned to your business context and intended purpose of utilization
7. For the long term, ensure accessibility and storage solutions as computing requirements are critical
8. Take into consideration regulation on consumer and data protection and anticipate potential risks related to the use of data (e.g: Fairness, Explainability, Transparency, Responsibility, Accuracy)
9. Build up a roadmap for extension/ replication based on lessons learned
10. Even if data is not your management's top priority, communicate, educate and unify them on the strategic goals allowed with better use of data, potential risks and challenges as well as progress made with your data
By this way, you will
Develop awareness on the importance of data for your business in future
Show quick win and short term results on which you can can capitalize for further automation and decision making
Propose a staged approach focusing on essential strategic outcomes and lessons learned
As a result:
--> Bye bye Status quo!
--> Hello "Snowball Effect" for the organization!
--> Welcome Insights!
