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
Better Conversion
Personalised Retention
Sales Automation
Customized look-alike profiles for top-performing traffic
Profiling-based custom funnels
Individualized messaging and product experience
When it comes to messaging, sales cannot connect the dots
DevPals stands at a data fork in the road and acknowledges the business need to enhance client interactions and business processes. We  understand how big data profiling and analysis  can aid  in data quality control during the era of the digital transformation. We simplify the process of  exploring and  rebuilding complex  big data lakes, leading to a truly digitally connected  businesses.
Unveil five exciting anticipated data breakthroughs by clicking here!
Unveil five exciting anticipated data breakthroughs by clicking here!
What is our process for choosing a database?
When it comes to selecting a database, we take into account both Relational Database Management Systems (RDBMS) and NoSQL databases, in order to gain a comprehensive understanding of each ecosystem. We evaluate different systems based on factors such as data type, storage, structure, and intended use, with the goal of meeting the specific needs of our clients. Additionally, factors such as required consistency, latency conditions, and transaction speed, including real-time querying mechanisms, may also play a role in the decision-making process.
Comparing Top Database Management Systems: Key Differences, Pros and Cons
Comparing Top Database Management Systems: Key Differences, Pros and Cons

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. Minimum/maximum 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.

Gain a competitive edge with DevPals' Big Data Profiling tools and say goodbye to costly errors in databases.

Contact us today to harness the power of data optimization!

 
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