Our Focus


Data Applied is a small technology startup located in Western Washington State. We are passionate about distributed computing, data mining algorithms, applied statistics, and data visualization. But foremost, we are passionate about bringing to you the strategic advantages that Business Intelligence and Data Mining can offer.

For years, large companies (ex: banks, insurers, retailers) have reaped the benefits of data mining to optimize their business. However, because of tremendous complexity and high costs, automated analysis has remained out of reach for small and medium businesses. Data Applied is changing the rules of the game by offering affordable, Web-based Data Mining and Business Intelligence solutions.

Data Applied revolutionizes data-driven decision making by integrating rich analytics, data mining, and information visualization capabilities – all using a zero footprint Web interface, collaboration features, and a secure XML Web API. By extracting valuable knowledge from data in domains as varied as Sales, Marketing, Engineering, Social Sciences or Non-Profit, we help leaders make better data-driven decisions and improve efficiency.


How We Can Help


Data Applied is ready to help you extract valuable knowledge from your data. Use our zero footprint Web interface for visual analysis, or perform programmatic analysis using our XML-based Web API. Among other services, we also offer custom analysis solutions.

Applications: (more here):

  • Survey
  • Sales
  • Marketing
  • Insurance
  • Non-Profit
  • Engineering
  • Law Enforcement
  • Social Sciences

Examples (more here):

  • Identify cross-sell opportunities
  • Discover frequent patterns in incident reports
  • Extract relationships between survey answers
  • Generate customer profiles based on behavior
  • Visualize large product inventory data
  • Predict future sales levels
  • Uncover fraud, abuse, and anomalies
  • Reveal root causes behind product defects
  • Visualize sales data by demographics
  • Slice & dice user satisfaction data
  • Identify potential major donors
  • Forecast future inventory stock levels
  • Categorize neighboroods based on crimes
  • Segment customers by shopping habits
  • Assist in product recommendations
  • Compare populations across dimensions
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