A data mart is a curated, domain-focused dataset that brings together the fields needed for a specific analysis or decision. In credit lending, it can give scoring models a consistent view of borrower activity and risk indicators without querying an entire data lake. section 4.3.1 section 5.1.2
A credit data mart can combine:
- Monthly transaction aggregates, such as spend totals and counts. section 5.1.2
- Bureau indicators, such as credit scores and delinquency flags. section 5.1.2
- Borrower attributes, such as region, age bracket, and protected or masked identifiers. section 5.1.2
A practical build joins these inputs at a defined grain, such as one row per user-month. Apply data-quality rules before modeling—for example, replacing invalid negative spending values with zero—and mask or hash personal identifiers. Record transformations and resource use so teams can review changes and investigate pipeline issues. section 5.1.2
Listing 5.1 A simplified SQL script to unify BFSI partial marts
CREATE OR REPLACE VIEW credit_data_mart AS
SELECT
u.user_id,
u.month,
COALESCE(u.total_spend, 0) AS monthly_spend,
b.bureau_score,
b.delinquency_flag,
d.region,
d.age_bracket,
d.tokenized_kyc
FROM monthly_usage u
LEFT JOIN bureau_lookup b
ON u.user_id = b.user_id
AND u.month = b.month
LEFT JOIN demographics d
ON u.user_id = d.user_id;
Use the SQL example as a starting pattern for joining monthly usage, bureau, and demographic tables into a credit mart. section 5.1.2
Treat the mart as a maintained data product, not a one-time extract. Validate missing values and distribution shifts, protect data in non-production environments, and preserve consistent schemas as new sources are added. These controls support reliable scoring and help surface data-feed problems before they affect decisions. section 4.3.1 section 1.5.1
