I had a fascinating, data-driven discussion with Scott Breitenother, CEO of Brooklyn Data Co., a company whose expertise includes Data Strategy & Governance, Data & Analytics Engineering, Analytics & Visualizations, AI & Machine Learning, and Digital Marketing & Measurement.
Scott and I have a spirited discussion about the challenges mid-sized organizations and large enterprises face in leveraging data, emphasizing the need for foundational data infrastructure and the concept of “speed to value.” We chat about overcoming the institutional roadblocks that often exist within organizations that can hamper their ability to fully leverage data for improved business results. Scott touches on the integration of AI in data strategy, noting that most companies will experience AI through SaaS tools rather than building their own models. We conclude by discussing the potential of AI in personalization and the importance of achieving a strong data foundation.
Discussion points include:
- How Brooklyn Data Co. grew from helping venture-backed startups to now working with Fortune 500s to build their data strategy and implement data warehouses.
- The asymmetry between data-rich and data-poor organizations, even within the same technology landscape.
- The importance of descriptive reporting and foundational infrastructure, given that many organizations still lack basic data capabilities.
- The goal of achieving “speed to value,” even if it means starting with one domain before expanding to the entire organization.
- The idea that everyone in an organization can do some level of data analysis.
- The importance of embedding analysts within teams to build non-transactional relationships.
- Why “tech debt” often occurs when successful companies outgrow their infrastructure.
- The challenges of internal data fiefdoms and political constraints within organizations.
- The importance of top-down buy-in and setting the tone for data-driven decision-making.
- The need for organizations to be thoughtful about how they engage with AI and the importance of a strong data foundation.
- The potential for AI to revolutionize various industries and the importance of continuous improvement.
