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Best Practices for Strucuturing a Data Team

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ODSC East 2018
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Best Practices for Strucuturing a Data Team

  1. 1. BEST PRACTICES FOR STRUCTURING THE DATA TEAM
  2. 2. ROLES Group Role Description Data & Information Information Architect Manages the logical representation of data Data Warehouse Architect Defines the physical representation of data Engineering Core Platform Engineer Builds and Supports the infrastructure Reporting & Insights Analyst Interacts with the data to build reports and metrics Data Scientist Interacts wth the data to build insight
  3. 3. HIERARCHY Leadership - requires an experienced generalist in many of the defined roles Reporting Structure - Mostly flat, Analysts may report into data science and business roles (etc Marketing) TOOLING & ACCESS Data & Information - own all databases, data services, documentation for logical data representations Engineering - owns core infrastructure, cloud platform services, system- wide performance Reporting & Insights - owns query, reporting and data library and module versioning, analytics environments and tooling
  4. 4. HIRING STRATEGIES Basic Mantra - core philosophies and methodologies in data management change much less frequently than hands-on techniques and technologies, a teams hiring strategy should reflect that. Data Scientists and Platform Engineers Operate in environments that foster new skills almost every 3-6 months. Skills in this area are transient, evolve rapidly and don’t necessarily affect the company over a long period of time. Hire externally when strategically necessary to acquire new skills and capabilities. Analysts, Information and Data Warehousing Architects Use skills and design principles in business and design that evolve much less frequently and tend to mature within a company over 6-12 months. The best-practices used here (such as information management and how reports are compiled) do influence an organization over a longer period of time. Hire strategically to foster long term excellence within a company, if possible.
  5. 5. TRADE OFFS Strict adoption the previous slides recommendations would lead to a hectic data analytics and warehousing environment with potentially lower levels of capability within information management and analysis. In practice, a blend is really necessary where both areas can be anchored with strong leaders that exist for longer periods within the company. Data Scientists and Platform Engineers Hire experts when required (at the expense of obtaining long term stability with the analytics environment) - or - Build an analytics team that can mature to become self-governing, especially within internal data models and libraries (but will be be less reactive to newer technologies and approaches) Analysts, Information and Data Warehousing Architects Invest in analysts that can become masters of how the organizational business is quantified and information is organized (at the expense of obtaining expert level analysts earlier). - or -

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