Data Analytics AI in New York

Investment management analytics spans portfolio performance attribution, risk analytics, client reporting, and regulatory reporting requirements. We start with the reporting your clients and regulators require, then build the data infrastructure that produces those reports reliably and automatically. For firms with complex multi-asset, multi-currency portfolios, the data modeling work is substantial. We bring experience in financial data modeling that most general analytics consultants lack, and we design the compliance architecture to support SEC and FINRA examination requirements from the start.

Yes. Media analytics requires real-time or near-real-time data on article reads, video plays, click-through rates, scroll depth, and social sharing. We design data pipelines with appropriate latency for your editorial decision-making cycle, whether that is truly real-time for breaking news operations or hourly for longer-form content strategy. We build editorial dashboards that surface actionable content performance signals without drowning editors in raw data.

Large financial institutions in New York have data environments measured in terabytes or petabytes. We design data architectures that handle this volume efficiently: partitioned data warehouses, columnar storage formats, query optimization, and tiered storage that keeps hot data fast and cold data affordable. We have experience with the data engineering patterns required at this scale and with the compliance controls that New York's regulated financial institutions require.

Enterprise data governance in New York's regulated industries requires role-based data access controls, data lineage documentation, audit trails for data access and modification, and data retention policies aligned with regulatory requirements. We implement these as part of the data infrastructure architecture, not as afterthoughts. For financial services clients, this includes controls required by SEC, FINRA, and New York DFS examinations.

We evaluate every potential analytics initiative against three criteria: Is the underlying business decision clearly defined? Is the data available and reliable enough to inform that decision? Is the expected improvement in decision quality worth the analytics investment? Projects that fail the first two criteria consistently underdeliver. We decline projects that do not pass this filter rather than taking on work that will not produce value for our clients.

Growth-stage companies in New York typically start with fragmented data across SaaS tools and need their first proper data warehouse implementation. We implement a modern data stack: Fivetran or Airbyte for data ingestion, Snowflake or BigQuery for warehousing, dbt for transformation, and Looker or Metabase for business intelligence. The entire stack can be operational in eight to twelve weeks for a company with five to ten source systems. This becomes the foundation that supports all subsequent analytics and AI work. --- New York's most competitive businesses extract insights faster and act on them more confidently with analytics infrastructure built for their specific data and decision-making requirements. Running Start Digital builds that infrastructure. Contact us to discuss your analytics program.