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 on your customers, ranging from recent purchases to loans, to unleash insights on segments and achieve:

Targeted Acquisition

Customized look-alike profiles for top-performing traffic

Better Conversion

Profiling-based custom funnels

Personalised Retention

Individualized messaging and product experience

Sales Automation

When it comes to messaging and products, sales cannot connect the dots

DevPals is at a data fork in the road and understands the business need to improve customer interactions and business processes. We know how big data profiling and analysis aid in data quality control in the age of digital transformation. We will make it simple to explore and rebuild complex big data lakes, resulting in a truly digitally connected business.

How do we select a database?

We look at both Relational Database Management Systems (RDBMS) and NoSQL databases to get a bird's-eye view of each ecosystem (non-relational). Depending on the type, storage, structure, and intended use of the data, different systems are used to better meet the needs of our clients. Furthermore, the required consistency, latency conditions, or transaction speed, including real-time, of the querying mechanism may influence the decision.

Big Data Profiling by DevPals

DevPals Big Data Profiling Practices

Basic Techniques

01. Distinct count and percent

Identifies natural keys, which are different values in each column and can aid in the processing of inserts and updates.

02. Percent of zero/blank values

Identifies data that is missing or unknown. Assists ETL architects in establishing appropriate default values.

03. Mini/maxi string length

To improve performance, you can set column widths to be just wide enough for the data.

Advanced Techniques

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.

02. Cardinality

Examines one-to-one, one-to-many, and many-to-many relationships between related data sets. Assists BI tools in correctly performing inner or outer joins.

03. Distributions

Checking that data fields are properly formatted. Data fields used for outbound communications, such as emails and phone numbers, are well-known.

Allow DevPals to increase the competitiveness of your company by implementing advanced Big Data Profiling tools.


Don't postpone it any longer. Contact us and we'll work together to get it done.

 
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