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

Tailored look-alike profiles for best performing traffic

Better Conversion

Custom funnels based on profiling

Personalised Retention

Customised messaging and product experience

Sales Automation

Messaging and product where sales cannot connect
DevPals is at the data crossroads 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 aid in data quality control. We will make complex big data lakes easy to explore and rebuild, resulting in a truly digitally connected business.
How do we choose a database?

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

Big Data Profiling by DevPals

DevPals Big Data Profiling Practices

Basic Techniques

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.

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

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.

03. Distributions

Verifying that data fields are formatted correctly. Famous for data fields used for outbound communications, such as emails and phone numbers.

Allow DevPals to boost your company's competitiveness with advanced digital tools.

Don't put it off any longer. Send us an email and we'll do it together.

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