Every Fortune 500 organization has dozens of data sources, if not more, that get pumped into the organization’s data ecosystem through different pathways. These data sources can be internal systems, ERP systems, SaaS solutions, or other third-party platforms and come in a wide array of formats and file types. Ingesting data from these sources, integrating them, and transforming the raw data into actionable insights enables enterprises to make data-driven decisions and unlock value from analytics and AI/ML initiatives. However, for global CPG organizations, with ever-increasing data volumes and complexity dependent on traditional ingestion approaches, this critical step brings with it a set of challenges of supporting multiple business functions and geographically diverse data sources, complicating their endeavor of creating consumable AI/ML-ready data assets.
Challenges with Traditional Ingestion Approaches
- There is a need to build discrete independent pipelines, which are often hardcoded from scratch for different types of sources.
- Lack of quick scalability, as every new source requires a ground-up development and testing of the workflow.
- Tens and hundreds of independent pipelines exponentially increase the complexity of monitoring, maintaining, and debugging.
- Disparate teams building their own pipelines lead to a lack of standardization and inconsistent governance.
At MathCo, we follow and recommend a unified approach to data ingestion and warehousing to serve a wide multitude of analytics use cases centered around integrated data zones. These data zones can be customized based on the enterprise architecture, data governance model and consumption design while serving as the starting point for analytics teams to source trusted data from, in any preferred format.
Solving Data Ingestion Challenges with a Modern Framework
A modern ingestion framework goes beyond treating ingestion as pure data loading. It ensures that data is sourced and loaded securely, at scale, with trust and traceability. Some of the key components of this approach include:
Expedited onboarding of sources, up to 5x speed
Metadata-driven configurations and parameterized and modularized pipelines underpin the core of enabling scale and quality. Development of schema mappings and standard transformations for common CPG source systems—such as SAP ECC for sales orders, Blue Yonder for demand planning, IRI/Nielsen for syndicated POS data, and Trade Promotion Management (TPM) platforms—is supported by dynamic ingestion templates that adapt based on the source metadata using a central control table.
These templates handle diverse ingestion patterns across relational databases, file drops (eg, distributor FTPs), and APIs (eg, retailer portals). The framework also accommodates semi-structured and unstructured data, like:
- Consumer feedback surveys from Qualtrics or SurveyMonkey.
- Social media sentiment trends from Twitter, Instagram, and/or TikTok.
- Retailer product reviews scraped from Amazon, Walmart, etc.
Built-in governance and lineage tracking
Governance is not a downstream add-on or an afterthought, but starts right at the ingestion stage with:
- Robust data classification and sensitivity tagging.
- Comprehensive pipeline execution logs and metadata at every stage.
- Integration with enterprise catalogs like Databricks Unity Catalog or Microsoft Purview for lineage.
- Appropriate use of cloud-native secret management services to securely handle credentials and authorization workflows.
Embedded data quality
The ingestion framework consists of a built-in automated data quality rule repository for the proactive identification of quality issues:
- Validate schemas and column data types against expectations as defined in data contracts. For instance, Nielsen/IRI POS: Weekly sales extract – UPC, base price, store ID checks
- Match critical KPI values with expected ranges, defined statistically based on historical patterns. For instance, the flag sales column deviations from a 6-week rolling average
- Check for standard quality errors, such as duplicates, nulls, data type errors, and spikes/drops in record volumes.
- Send real-time alerts for failures or anomalies, and quarantine suspect records without breaking the entire pipeline based on a defined severity level.
Scalable while being cognizant of costs
- Serverless or containerized, to scale up/down based on load. For instance, scaling up clusters during monthly POS ingest jobs and then consecutive scale-downs.
- Adapting the functionality of cloud-native services (Databricks, Azure Data Factory, Snowpipe, etc.) to suit specific business requirements. For instance, data archiving to lower-tier data stores for records over a set period
- Built with cost-optimization in mind—auto-scaling clusters, transient compute, and data egress/storage monitoring. For instance, Trade promo RoI calculation jobs that auto-terminate after completion.
Interested in learning how MathCo can be your enterprise partner to drive accelerated value powered by data-driven insights? Contact us here.
