Capabilities
Working with data is like building a city and I've done both.
They both take lots of planning, a good strategy and and standards to get what you want.
But in the end it is worth the effort.
Focus Area
My focus area is in Data Architecture and Data Engineering. Data is like building a city and this helps me because I am a former city planner. To learn more about that, read my story.
In the world of city planning there is concept called a Master Planned Community that mixes uses including residential, commercial and recreational. It is large-scale, self contained and comprehensive.
It reminds me very much of Data Architecture. In this world you have to do strategic planning that is comprehensive and affects the entire organization and how it operates. And many systems effect others and need to be integrated together to serve the customer well.
This is why the Data Management process is important. It ensures that good planning happens.
Data Management
I believe in the value of Data Quality and Data Standards and the entire data management process.
My quick summarization of the process includes 3 phases.
- Data Architecture & Strategy sets standards and rules for data use.
- Data Engineering uses those standard to build a robust infrastructure.
- Robust infrastructure enables the building of technology products that are easy to use for the end user.
I have seen time and again that when these steps are skipped, organizations encounter Data Problems.
Common Data Problems
I see the same data problems repeatedly. Fortunately, there is a solution to all of them.
- Spreadsheets don't scale and don't enforce data integrity.
- Messy data. Worst I've seen is 22 versions of the same term.
- Inappropriate access controls.
- Insufficient data security & privacy.
- Disconnected systems aka data silos.
- No strategy. Organizations are winging it and it shows.
The solution is a unified Data Platform that is based on quality Data Architecture and Engineering.
Data Platform
I am most interested in Data Architecture and Engineering and what I am most interested in building is a comprehensive Data Platform that involves:
- Data quality based on standards
- Data model that supports the business
- Centralized storage and Master Data
- Scalable infrastructure
- Data pipelines & ETL that enables integration
- Automated data cleaning & enrichment
- Data security involving data privacy, role based access controls and encryption of sensitive data
- Data products that are easy to use
- Automated & self-service reporting
Portfolio
For the last many years I primarily built SaaS style ERP applications that streamline business operations and the data infrastructure that powers them.
They are delivered through a common database for shared information, aka Master Data. They are integrated using a hub-and-spoke model and have multiple modules, each with its own focus. This is similar to the illustration.
The data infrastructure used a variety of data pipelines using ETL to extract data from a myriad of sources and deliver the data to the proper destination. Automated cleaning scripts were applied to ensure the data met quality standards.
