Big Data Profiling
DevPals creates a toolbox of business rules and analytics algorithms to detect, understand and potentially reveal big data inconsistencies. This knowledge is then used to improve existing data sets and systems as an essential part of the monitoring process and can eliminate costly errors that are common in customer databases.
We use Big Data analysis based on first- and third- party data
More than 40 000 data points, from recent purchases to loans, on your customers, to unleash insights on segments and to achieve:
Tailored look-alike profiles for best performing traffic
Custom funnels based on profiling
Customised messaging and product experience
Messaging and product where sales cannot connect
DevPals is at the data intersection and understands the business desire to improve customer interactions and business processes. In the age of digital transformation, we know how big data profiling and analysis help to achieve data quality control. We will make exploring and rebuilding complex big data lakes easy and create a truly digitally connected business.
How do we choose a database?
We look at both Relational Database Management Systems (RDBMS) and NoSQL (non-relational) for a bird's eye view of each ecosystem.
Depending on the type, data storage, structure and intended use of the data, different systems are used to better meet the needs of our clients. Furthermore, the querying mechanism required consistency or latency conditions or transaction speed, including in real-time, may have an impact on the decision.
DevPals Big Data Profiling Practices
01. Distinct count and percent
Identifies natural keys, different values in each column that can help process inserts and updates.
02. Percent of zero/blank values
Identifies missing or unknown data. Helps ETL architects set up appropriate default values.
03. Mini/maxi string length
Enables setting column widths just wide enough for the data, to improve performance.
01. Key integrity
Ensures keys are always present in the data, using zero/blank/null analysis. Helps identify orphan keys, which are problematic for ETL and future analysis.
Checks relationships like one-to-one, one-to-many, many-to-many, between related data sets. Helps BI tools perform inner or outer joins correctly.
Verifying that data fields are formatted correctly. Famous for data fields used for outbound communications, such as emails and phone numbers.