Focus Area
Data Is Valuable
My primary focus are is on data and how it can be used to provide business value using a range of technologies and skill sets. All of my research, training and project work has some connection to data. I follow the DAMA-DMBOK (Data Management Association Book of Knowledge) framework, which is technology independent and teaches fundamental principles of data managment in a wide range of use cases.
Approach
My approach to data is that fundamentals come first. These are things such as Governance, Data Strategy, Standards, Data Quality, Metadata, Master Data and so on. Only after these are accomplished will technical implementation be accomplished. On the technical side I have a deep technical background working with data and can offer a range of solutions. When a company does data right, they can identify opportunities and understand their customer at a deeper level. These steps are generalized categories and would be broken out into more distinct step in a real project.
Strategy
Strategy involved determining what you want to do with data and how you plan to accomplish it. It would involved identifying data sources, determining standards, writing SOPs, data quality, etc.
Security & Privacy
Security and privacy are major issues and depend upon your location and industry. Data security is primarily concerned with granting appropriate access to authorized people. Privacy follows standards such as GDPR, CCPA, PCI, HIPPA, SOX, etc.
Architecture, Data Modeling & Storage
Architecture connects the strategy and technical implementation. Data modeling takes the business requirements so you design your databases the right way. Storage is concerned with infrastructure and scalability with a wide range of options.
Data Integration
It is very common for a business to have data scattered in a range of apps including CRM, Accounting, HR, etc. Data integration uses a process called ETL where raw data is Extracted from apps and stored in a staging area, then Transformed to match the destination format and Loaded to the final destination, typically a data warehouse.
Data Quality
Data Quality matters because it affects the results. Its the old garbage in garbage out theory. Data Quality standards are set during Data Strategy but are then applied during the Data Integration process during the transformation stage using a data pipeline.
Data Products
Types of data products include business intelligence, automated programs and machine learning. These are the end result of all the work you did to get your data into an acceptable format. They may include self-service dashboard, automated reports, data pipelines, predictive analytics, clustering, classification and recommendations.