Big Data has gone past PoC phase. Different companies are at different stages of implementation. Data Ingestion, Data Storage ( Data Lake and EDW), Data Processing and Data Visualization processes have been quite mature and there are many open source and proprietary software to solve these problems.
One major hurdle Big Data faces today is – Entity Resolution ( defining a business entity form multitude of data sources). In EDW (Enterprise data warehouse) world, data were structured and sources were limited. Also keys of sources were pre and well defined . So RDBMS joins ( inner, outer, left outer, right outer, semi join etc) were enough to merge data from two different systems and tables.
In Big Data world, there is hardly any key or attribute that runs across the sources. Also keys of one system is useless as other systems are completely independent of each other. So business have to define their own logic for merging data from different sources which defines one entity. To make it worse, RDBMS kind of exact match joins should be replaced by fuzzy joins as referential integrity across systems can be ensured.
Following is a practical approach for resolving Entity Resolution:-
1.) Pre-define your entities and super set of attributes ( coming from all data sources)
2.) Attributes may have multiple related values that should to mapped to same attributes. Plan your storage like this ( Graph databases work fine for this relationship storage)
3.) Merge data from multiple sources using merge business logic to create a virtual entity with sizable attributes ( we used Apache spark for this )
- Zip, County, State, Lat/long
- Nearby locations (+/- area) – high propensity area
- IP location
- Date/time stamp
- Nearby time stamp (+/-) – Event happening before or after a period
- String match (Fuzzy) – Name, Address, Cause
- Cardinal Match – events sharing same or similar key
- IP Correlation (Primary IP, Secondary IP, IP from same ZIP code)
- Other business logic related merging rules
You can make you merge model Machine Learning based so it will be leaning over time to do a relevant merge.
4.) Right Sizing Merges Columns
- Remove transaction columns
- Remove database columns
- Remove technical identifiable columns
- Remove duplicate Columns
5.) Search this entity in your relational graph database to find ranks of similar entities more than threshold. If the outcome is less than threshold then make a new entry in your entity table with the attributes of the searching entity.
6.) Take the highest ranked entity and mapped the missing attributes from highest ranked entities. This entity is you final entity for business
How to enhance entities with changing attributes:
Like any practical entity, values of attributes keep changing. You map the attributes values of searching entity to highest ranked entity and see the different of values. let’s say the IP values of an entity is matching to its secondary value over time. Then the secondary IP value becomes primary and primary becomes secondary. Or it is new IP then add one more relationship node with new values.
Business can assign different weight-age to must have, critical, important and good to have attributes and their matching threshold. This model also matches with secondary or related values so make it more accurate. Let’s say, address is a must match attributes. A customer other attributes are matching but address not matching so other models will reject it but in this model if matches with his or her office address or other address, it will boost the record that is right.