What I Think When I Talk About Data

Every year, I set a physical and mental goal for myself on my birthday. These goals are essentially “stretch” goals and are meant to challenge me to be a better version of myself every year, be it at work, fitness, or just in life. So, as I sat mulling over my mental goal for this year, I realized that I had a big story to tell in terms of my data profession, which has consumed over 50% of my existence now, and thought that there is no better way to share it than with words.

So, here goes a culmination of my experiences with data that can help you drive your organization to be data-driven. I had meant to write it as a book but figured it would be better if I did not wait a year before I could start publishing this content. Given that this is such a big story with many chapters, this will be a series of articles that will talk about what I think when I talk about data.

These series are aimed towards data leaders, Chief Data Officers, the C-suite, and anyone who is vested in driving their organization to be data-driven.

In the nature of making this content actionable, checklists will be provided for the topic at the end of each chapter that you can use for your purpose. As such, each chapter can be read independently. However, there are some interdependencies between topics, so you may find that detailed topics may not yet be published within my series.

Topics

• Data-driven: Data Democratization, Data Monetization, Data/Analytics Maturity, Semantic Layer
• Data Teams: Business Intelligence (BI), Analytics, Data Science, Data Roles
• Data Organization: Reporting structure of Data Teams
• Data Strategy and Roadmap: Data/Analytics Strategy, Roadmap, Vision
• Data Management: Master Data Management, Data Governance, Data Privacy, Agile Data Organization, Data Lineage, Data catalog, Data Replication, Data Processing
• Data Architecture: Batch and streaming architectures, How to build a modern data architecture for an AI/ML organization, Semantic Layer/Data Platforms, Data Modeling
• Data Quality: Data Profiling, Operational Excellence
• Data Consumption: Data champions, Power Users, Data Scientists, Business Users, Executives
• Data Operations: How to select the best data infrastructure for your organization, DataOps, MLOps
• Data Tools: ETL, Reporting, Visualization, Advanced Analytics, Data Science, Machine Learning, Artificial Intelligence (AI)
• Data Analytics: Customer Analytics, Product Analytics
• Data Culture: Data Literacy, Impacting culture in Legacy vs Modern organizations
• Chief Data/Analytics Officer (CD/AO): 30/60/90 day CDO, What keeps a CDO up at night, CDO vs CDAO, Reporting structure for CDO within an organization
• Experimentation(Test and Learn): Why is this a consideration for data-driven organizations
• Data Controversies: Data Lake or Data Warehouse, Single source of truth vs Single version of Truth, Python vs R, ETL vs ELT, Cloud vs On-Premise
• Data Education: Books to read, Blogs to follow, Courses to take

If you want to talk more or provide inputs/feedback about enabling data-driven organizations or any of the topics above contact me at thedatawall@gmail.com

If you’re interested in connecting, follow me on Linkedin, and Medium.