Counterfactuals, LLC

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You can schedule an initial consultation with me using this link.

I typically work with early- to mid-stage companies who are interested in establishing a data analytics practice for the first time, or would like to address technical debt in their current analytics implementation.

This involves some or all of the following components:

• Data Warehousing: Provisioning a new data warehouse to centralize analytical use of data, including the configuration of necessary access roles and compute resources
  • I have extensive experience with Snowflake, Amazon Redshift, and Google BigQuery, among others, and can provide insights and recommendations regarding the best platform to implement
• Data Ingestion: Centralizing the storage of analytical data within the data warehouse by integrating with data-generating platforms like application databases, CRMs, and event logs
  • Depending on the platforms being integrated and the desired budget, this may involve the implementation of third-party integration providers like Fivetran or Stitch Data, or it may require the development of custom data ingestion scripts
• Data Transformation: Cleaning, combining, and structuring raw data in the data warehouse for convenience and analytical use
  • A robust and well-designed transformation layer enables business intelligence and analytics to be much more efficient and consistent
  • Transformation is most commonly implemented via dbt and entails ongoing work to create and maintain data models to meet business needs
• Business Intelligence: Creating and managing dashboards to allow business end users to access and analyze data
  • Depending on the desired budget, I have extensive experience with a variety of business intelligence tools such as Tableau and Looker
  • Well-designed dashboards will allow business users to self-serve a variety of use cases while guiding them to think more critically about their business function and preventing them from taking erroneous or biased conclusions from the data
• Descriptive Analytics: Answering high-level questions about the impact of past and future business initiatives to guide strategy
• Predictive Analytics: Implementing more sophisticated predictive models such as revenue forecasts and customer-level propensity models

An effective analytics practice seeks to simplify the business functions it supports by providing clarity and answering key questions. A poorly-implemented analytics practice can end up doing the opposite, by adding new tools and complexity that obfuscates the truth that business leaders should be seeking. Long turnaround times and poor communication from an analytics team can frustrate their stakeholders and create a negative feedback loop of lost trust.

I aim to prevent this by creating a data analytics infrastructure that is as simple as possible while still addressing key business needs in the present and future. Crucially, it’s key to understand that scalable data infrastructure doesn’t just include the technical aspects of a data practice (data ingestion, transformation, and dashboards), but also “softer” aspects like metric design, stakeholder management, expectation management, and analytical communication. From early on, my goal when establishing an analytics practice is to set sustainable expectations regarding data availability and latency and to prioritize delivering high-quality analysis in a timely manner.

When discussing a new project with a business leader, I like to start with some of the following questions:

• What are your company’s highest-level goals and what insight do you need to help measure or achieve these goals?
• Which business functions at your company do you think stand to benefit the most from business intelligence and analytics?
• What are the key data sources that your company uses to make decisions?