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Learn How to Turbocharge Your AI/ML Data Workflows with Data Enrichment

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Learn How to Turbocharge Your AI/ML Data Workflows with Data Enrichment

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Trusted analytics and predictive data models require accurate, consistent, and contextual data. The more attributes used to fuel models, the more accurate their results. However, building comprehensive models with trusted data is not easy. Accessing data from multiple disparate sources, making spatial data consumable, and enriching models with reliable third-party data is challenging.


In this webinar you will learn how to:
Organize and manage address data and assign a unique and persistent identifier Enrich addresses with standard and dynamic attributes from our curated data portfolio Analyze enriched data to uncover relationships and create dashboard visualizations Understand high-level solution architecture

Trusted analytics and predictive data models require accurate, consistent, and contextual data. The more attributes used to fuel models, the more accurate their results. However, building comprehensive models with trusted data is not easy. Accessing data from multiple disparate sources, making spatial data consumable, and enriching models with reliable third-party data is challenging.


In this webinar you will learn how to:
Organize and manage address data and assign a unique and persistent identifier Enrich addresses with standard and dynamic attributes from our curated data portfolio Analyze enriched data to uncover relationships and create dashboard visualizations Understand high-level solution architecture

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Learn How to Turbocharge Your AI/ML Data Workflows with Data Enrichment

  1. 1. Learn How to Turbocharge Your AI/ML Data Workflows with Data Enrichment Tim McKenzie | Director, Solution Architecture 1
  2. 2. Location data challenges • Location is Messy- Addresses, Lat/Long, Shapes, Lines, Formats • Complexity of Joining Location Based Data Sources (3rd Party and Internal) • Data Sourcing Challenges- Many Providers, Many Formats, Many Pricing and Licensing Differences • Global Extensibility- Data Sources Tend to Be Regional Yet Use Cases are Often Global • Need to Identify and Process Multi-Family and Condo Properties • De-centralized repositories of data • Complex properties can often have multiple valid addresses, parcels and buildings. • Legal descriptions in variety of format leading to discrepancy, inefficiencies, errors and non-compliance 2 “For every minute spent in organizing, an hour is earned.” Benjamin Franklin Inventor, Statesman, Insurer
  3. 3. Data prep slows data science 3% 19% 9% 4% 5% What data scientists spend the most time doing Building data sets Cleaning and organizing data Collecting datasets Mining data for patterns Refining algorithms Other accounts for about 80% of the work of data scientists 3 3
  4. 4. Location enabling strategies for data analytics 03. Analyze Apply data science at scale to gain a competitive advantage 02. Enrich Leverage trusted ID to join massive amounts of your own and 3rd party data sources 01. Organize Assign a trusted ID that is unique and persistent to each address 4
  5. 5. Fast, easy, and consistent data enrichment 5 Precisely’s Geo Addressing with hyper-accurate Master Location Data (MLD) reference data • Belgium & Luxembourg • Canada • Finland • France • Germany • Great Britain • Ireland • Netherlands • Sweden • Singapore • United States • More coming soon! International Coverage Data Sources • Postal Authorities • Government datasets: local city, county, and state • Global Vendors • Local Players • Open Sources • Proprietary Sources • Largest & Best available • Unparalleled & • Parent-child relationship, • Unique and Persistent Identifier, • Multi-sourced, • Simplify data enrichment process, MLD Attributes
  6. 6. Data Enrichment – A global product portfolio Addresses & Property Verified and validated address and property data for map display and analytics Boundaries Administrative, community, and industry-specific boundaries for data enrichment and territory analysis Demographics Demographic and consumer context data for better understanding people and behavior Points of Interest Detailed business, leisure, and geographic features for location and competitive intelligence Streets Robust street-level data for mapping, analysis, routing, and geocoding Risk Natural hazard boundaries related to flood, fire, earthquakes, and weather Expertly curated datasets containing thousands of attributes for faster, confident decisions 6
  7. 7. Uniquely positioned to address data enrichment needs Global coverage location enrichment data. Our portfolio includes: • 400+ datasets • 250+ countries and territories • 100s of millions of data points Datasets that are interoperable and are managed to quality standard, with consistent documentation, and support e.g. • Property Graph • Market and Community Link Ability to enrich with dynamic data (Dynamic Weather and Dynamic Demographics) • Data that includes time as a dimension • Creating insights from data that is updated at regular and short time intervals (e.g. 5 min) Data experience through deep-domain expertise • Adding data through, development, partnerships, and acquisitions Best-in-class addressing and property datasets with a unique and persistent ID • Link Precisely and customer address, buildings, demographics, risk, and more data using the PreciselyID, a unique and persistent location identifier 7
  8. 8. Cloud-based location analytics technology 8 Spatial Functions 30+ Common Spatial Processes Global Geocoding Forward & Reverse Global Geocoding and Trusted ID Global Addressing Validate, standardize and parse global addresses Global Tax Jurisdiction Assign highly granular tax jurisdictions globally. Map Visualization Visualize Location Data at Scale Global Street Routing Assign isochrones and isodistance anywhere in the world.
  9. 9. Location-enabled analytics Bank Branch & ATM Call Center/ Web Customers by Product Commercial & Mortgage Active Mortgages Historical Defaults Geocoding and location intelligence capabilities to organize and enrich your data Financial Transactions All of your sources Any structure or frequency Analytics capabilities for any use case or persona Ad Hoc Data Science Low-cost, rapid experimentation with new data and models. Explainable Machine Learning High volume, fine-grained analysis at scale served in the tightest of service windows. BI Reporting & Dashboarding Power real-time dashboarding directly, or feed data to a data warehouse for high-concurrency reporting. Real-time Applications Provide real-time data to downstream applications or power applications via APIs. PreciselyID ADMIN BOUNDARIES BANK DEPOSITS MOBILE MOVEMENT WEATHER EVENTS HAZARD & RISK DATA AMENITIES & COMPETITION EVERY US/CAN ADDRESS BUSINESS LOCATIONS PROPERTY ATTRIBUTES SCHOOLS & NEIGHBORHOODS POPULATION DEMOGRAPHICS PARCELS & BUILDINGS Analytics Platform
  10. 10. Understanding the data challenge 10 • Accessing the right raw data • Keeping up with continuously changing data feeds • Building features from raw data • Combining features into training data • Calculating and serving features in production • Monitoring features in production Key data challenges that organizations face when productionizing ML systems 10
  11. 11. What is a “feature-based” architecture? 11 A feature store is an ML-specific data system that: • Runs data pipelines that transform raw data into feature values • Stores and manages the feature data itself, and • Serves feature data consistently for training and inference purposes A feature is data used as an input signal to a predictive model 11
  12. 12. 12 Processing Storage Inputs Location specific records Shape files Streaming records Address Fabric Analytics Processing • Model outputs • Scores • Computed columns • Analysis outcome Batch Geocoding with the Operational Addressing SDKs • Vaildate input addresses • Validate other data • Locate addresses • Match inputs • Assign PreciselyID • Relate data around PrecisleyID Batch Spatial Processing with the Location Intelligence SDK • Flatten shape files • Compute PIP • Compute D2P, D2L • Compute basic scores • Generate geohash • Relate data around geohash (where application) Realtime Processing with the Precisely SDKs • Operational Addressing APIs • Assign PreciselyID • Generate geohash • Relate data Message Bus Feature Store In-stream Analytics Layer Model outputs, scores, computed columns, analysis outcomes PrecisleyID Address P0000MK1IAAD 287 E 300 S. Provo, UT 84606 P0000MK1DPRD 410 N University Ave. Provo, UT 84601 Vendor data files Customer Loyalty Records Equipment Inventories Franchise Zones Pricing Delivery Territories Mobile Trace Data POS/IOT Data Administration, Governance, Security, Connectivity, Schema, Catalog Model Training EDW precisely Data subscriptions with PreciselyID PrecisleyID Address Name Type Score Location MICode PointCode DemoRgn P0000MK1IAAD 287 E 300 S. Provo, UT 84606 Empas LLC REST 91.529 UT108 10020100 101067669 8926 P0000MK1DPRD 410 N University Ave. Provo, UT 84601 THAI HUT REST 65.981 UT108 10020100 100854441 4144 …. ….. ….. ….. ….. …. …. ….. ….
  13. 13. Thank you Tim McKenzie Tim.McKenzie@precisely.com Phone: 678-428-1770

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