Good vs. bad data architecture with Derek Doel
The Data Standard
Have you seen real estate listings that describe a house as architect-designed? It adds a lot to the asking price, but aren’t all houses designed by architects? Most are, but they’re then rather generically churned out by a construction company. If your house counts as architecture, though, someone designed it for specific needs and tastes. In the same way, your organization stands to benefit when your data architecture is put together based on your particular data management and analytics needs. But often, we inherit many of the generic components and integrations from the product designs of our technology vendors. If we’ve customized some of the structure of data architecture, that may have happened ad hoc, over a long time. Within a successful data architecture, a conceptual design based on the business process is the most crucial ingredient, followed by a logical design that emphasizes consistency, integrity, and efficiency across all the databases and data pipelines. Once the data architecture is established, the organization can see what data resides where and ensure that the data is secured, stored efficiently, and processed accurately. Also, when one database or a component is changed, the data architecture can allow the organization to assess the impact quickly and guides all relevant teams on the designs and implementations. Lastly, the data architecture is a live document of the enterprise systems, which is guaranteed to be up-to-date and gives a clear end-to-end picture. In summary, a holistic data architecture that reflects the end-to-end business process and operations is essential for a company to advance quickly and efficiently while undergoing significant changes such as acquisitions, digital transformation, or migration to the next-gen platform.
Meet The Host
VP, Strategy and Business Development at Pandio
Results-oriented executive with a strong track record of performance both as a strategic thinker and operator. Skilled in Corporate Development, Strategic Planning, Mergers and Acquisitions, and Marketing Strategy.
Meet The Guest
Principal Data Scientist/Founder at Analytics Odyssey
- Derek Doel has been a thought leader in the Silicon Slopes Data Science & ML community since 2010. - Derek is an end-to-end engineer for data science products throughout the SAAS industry. At Womply, he architects ML models that deploy to AWS Sagemaker CI/CD endpoints. - As the 10th employee and the first in data, Derek helped RainFocus (a SAAS tech platform) grow to 200 employees and $35M series B funding in just 3.5 years. Derek's work is seen throughout the core data platform of RainFocus. - His SAAS product contributions include: engagement scoring, self-serve dashboards and reporting, gradient boosting for business matching, and neural-network architecture for machine learning report engines. - Derek has worked in marketing analytics, data science, product development, data architecture, BI-tool integration, and machine learning development. - While Derek believes that data strategy is agnostic to the actual data, he has applied his thought leadership to enterprise events, enterprise finance, CRM, sales, and marketing. - Aside from building transformational data products for SAAS companies, he has also been a key thought leader in data strategy for rapid growth start-ups in the heart of Silicon Slopes. Derek received a master of statistics in Econometrics from the University of Utah and a B.S. in Actuarial Science from Southern Utah University.