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, /PRNewswire/ -- DataSnap, an embedded analytics consultancy based in Newport Beach, California, today announced the expansion of its offerings to include powerful AI-driven decision intelligence giving growth-stage companies the ability to transform data into action at enterprise scale, without the overhead.
Through its integrated model, DataSnap places full-stack analytics teams directly inside client organizations, supporting the entire data lifecycle from cloud infrastructure to business intelligence, forecasting, and strategic enablement. With the addition of applied AI, these teams now empower clients to detect opportunity, predict risk, and activate insights in real time.
The company is led by Danyal Ali, who brings over a decade of experience building high-impact data functions across tech and consumer sectors, including roles at Gympass, Groupon, and most recently as Chief Marketing Technology Officer at Xponential Fitness. Ali explained that the expansion reflects a broader shift in how companies need to think about data activation.
"Data should accelerate your business, not slow it down. We built DataSnap for teams that need immediate traction not in six months, not after four new hires, but now. With AI now embedded into our approach, we're helping companies do more than just understand the past, we're giving them the tools to shape what happens next," said Danyal Ali, Founder and CEO of DataSnap™.
At the core of DataSnap's offering are four integrated service pillars:
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Data Engineering & Infrastructure – Design and implementation of scalable cloud data stacks (e.g., Snowflake, Redshift, BigQuery, dbt, Airflow).
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Business Intelligence & Reporting – Creation of automated dashboards, KPI reporting, and self-serve environments.
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Advanced Analytics & Modeling – Delivery of predictive models for segmentation, retention, and growth planning.
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Data Strategy & Enablement – Activation of data in go-to-market efforts, experimentation frameworks, and enablement training.
Each of these areas now incorporates embedded machine learning to unlock high-impact use cases. Teams help clients predict customer churn, trigger loyalty campaigns based on health scores, and recommend next-best products through behavioral and transaction-based signals. AI also supports reactivation timing strategies detecting when dormant customers are most likely to return and recommending the most effective engagement channels.
These capabilities are deployed directly into clients' existing tech stacks from CRMs and marketing platforms to data warehouses ensuring that insights translate into action without operational friction. The result is a continuous, compounding cycle of learning, optimization, and growth.