Do You Trust Your Machine Learning Outcomes?

Editor's Notes

1. Users of real-time and streaming data architectures increasingly realize that real-time data quality is an operational concern
2. Need to automate decision making Need to scale Need to predict Need to plan Need a competitive advantage Rise of the use of AI to improve existing data pipelines and processes – such as smart rules, automatic data classification, and intelligent automated rule application Rise of Data Quality for AI and the emergence of MLOps - the need for “good data” instead of just “big data”
3. Call out data challenges associated with each of these statistics: Cannot build real-time data pipelines to feed business applications and analytics Time consuming and manual effort to standardize, verify, and validate data across entities  Difficult to make addresses data fit for purpose – this requires significant expertise, time, and resources Manually tracking and incorporating up-to-date location, business, and demographic information
4. Leverage hyper-accurate geocoding to inform pricing policy decisions and risk This can speak to risk that exists due to a policy location and variables that could cause a claim such as flooding, hurricanes, or wildfires But is can also speak to adjacent risk. Understanding if there is a nearby business that could cause a problem (what if they are near a fireworks store?), or if you have too many policies located in a single building, such as a high-rise building, which could cause a large loss on many policies if an event happened, such as a fire.
5. Business Challenge: improve speed and accuracy of valuation models by joining thousands of variables to effectively predict a property’s market value. Technical Challenge: Connecting volumes of data from disparate sources and ensuring a consistent and accurate approach to feed trusted data into ML models Solution: Deployed cloud-native location intelligence with expertly curated datasets to connect and build trusted data feeding ML to predict property market values. Resulted in building trusted data in 3.2 hours (and getting faster all the time!) reduced from 13+ hours Right data is connected to the right property, trusted data Other say they do that but don’t do well, false positives – we do better matching, accurate location, make it easier to use with preciselyID and super fast When I say, “build trusted data,” I am referring to the processing to join/bring the data together that will be used to feed the product value ML model.
6. Unlike the traditional methods, ML can analyze significant volumes of personal information to reduce their risk.
7. AI workflows can analyze data sources like consumer mobility and purchase pattern
8. Location: not just about enriching, we enrich it correctly so people do not get false positives, false information Importance of data being done correctly, building trust is critical
9. And that is why Precisely has introduced the Precisely Data Integrity Suite. It delivers the essential elements of data integrity – accuracy, consistency, and content – to give your business the confidence to make better, faster decisions based on trusted data. Built on proven data integration, data quality, location intelligence, and data enrichment capabilities trusted by more than 12,000 global organizations, the Precisely Data Integrity Suite delivers unmatched value for any data integrity initiative. And with a modular architecture, you can pick just the capabilities you need, implement them alongside your current infrastructure, and add-on new capabilities as your needs grow.