Create a winning Data Pipeline Architecture on the Cloud for CPG

The CPG industry traditionally had little exposure to consumer information. But, thanks to the explosion in digitization as well as a shift in the way that consumers shop to direct-to-consumer sales and online shopping, CPGs are generating customer and external data in ways that have never been before. Based on studies that retail websites had 22 billion visits in June 2020, compared to 16.07 billion worldwide visitors in the month of January.

This growing interest in data by CPGs demands managing data on a large scale and adopting innovative approaches to managing data. Although this may seem like the ideal scenario, actually creating data and constructing an efficient data pipeline is a major hurdle for CPGs.

Data Problems for CPG

One of the biggest problems that CPGs have to face is collecting, orchestrating, and managing customer information that was previously at the disposal of distributors and retailers. According to the findings of a CGT study, the majority of retailers don’t share these data sets, which include online sales, promotional sales, and pricing data.

Some of the information that CPGs are able to access is:

·         First-party information that is internal to the company, such as CRMs and ERPs

·         Third-party data obtained from eCommerce and retail stores

·         Third-party data from aggregators such as Nielsen, DunnHumby, ScanTrack, and Media spending data

·         Open-source data, such as climate data, weather data, COVID data, and many more.

Despite petabytes and petabytes of information flowing from a myriad of internal and external data sources and data sources, the process of ingesting, accessing, and analyzing data from enterprises is one of the most difficult challenges for CPGs. With multiple data management systems throughout all aspects of the CPG value chain, such as manufacturing and supply chain, and supply chain, these data sources reside in isolation.

Additionally, the ability to access real-time data is crucial to CPG companies who have the possibility of using it to aid in developing products and making better marketing choices. Different stakeholders need access to information at various dimensions.


The other issues include the security of data as well as data integrity and manual maintenance. These constraints result in lengthy development cycles, and bottlenecks in the process of processing data and extracting relevant data from it.

CPGs must build an efficient and scalable data infrastructure that can integrate diverse data sources using flexible cloud-based data warehouse techniques that can be utilized to further analyze data and provide the decision-makers with real-time decision-making assistance. The creation of data pipelines is an essential first step. With access to precise and well-prepared datasets, the data team can construct precise models that yield enormous benefits for companies.

Data Pipelines

The most common demand is to design an end-to-end system to consume large amounts of data with high speed that allows data integration for efficient and stable processing as well as creating scalable pipelines for data with short query times for instantaneous information. The steps are described in more detail below.

Data collection and ingestion

The initial step to create Data pipelines for CPG is to collect and analyze the data from all sources to process it in real-time. Data is extracted from the above sources. Extractions are read from each source of data with external API integration, the internal databases, and web scraping for multiple extractions.

Although the data may be processed in real-time or in batch, CPGs must prepare data for real-time processing so that they can capitalize on the flow of streaming data like social media, or data from marketing campaigns, such as views, clicks, or shares. In processing data from streaming streams to uncover insights, one typical challenge for developers is dealing with duplicate data that could come as a result of source-generated duplicates, publisher-generated duplicates, and many more. Tools like Apache Spark provide a mechanism to eliminate duplicates and to recover data in the event of errors. In-stream duplicates can be removed using pass-through deduplication.

Data Transformation

When data is taken from the source system the structure or format might require adjustments through data transformations, for example via database joins, unions or unioning. ELT lets transformations be performed before the data is transferred to data warehouses in the cloud-based data warehouse or in data lakes. ELT also works in conjunction with data lakes, allowing them to take unstructured and structured data and to ingest the ever-growing pool of raw data as soon as it becomes accessible.

Data Monitoring

Data pipelines are complicated systems that comprise hardware, software, and network components and can be prone to failures. To ensure that the pipeline is operational, developers should keep an eye on it regularly and fix any issues that occur. A variety of other aspects determine the performance of a data pipeline, including throughput or rate of speed, fault tolerance latency, idempotency, and fault tolerance to mention some.

Rate, also known as throughput is the quantity of data the pipeline is able to process in an amount of time. With the constant flow of data, CPGs have to deal with, the need to develop pipelines that have high throughput is not a problem.

Data pipelines that are fault-tolerant are designed to detect and prevent the most basic and common problems, like downstream failures or network failures. The data pipeline is prone to failures that could compromise critical CPG initiatives in analytics. With this in mind, CPG companies need to develop distributed data pipelines that provide immediate failover and alert the data teams in the event of an application malfunction, node malfunction, or failure in one of the other services.

Latency is the length of the amount of time required for one piece of data to move throughout the data pipeline. In order to ensure the effectiveness of data pipelines, low latency could be a barrier in terms of cost and processing capacity.

Re-runnability or Idempotency is a term used to describe the re-application of a program and, in this case, it is the re-execution or execution of the pipeline. Data pipeline could be required to be restarted in various scenarios like incorrect source data, errors in the process of transformation, or the addition of a new dimension to the information. It is essential to ensure the integrity of the data pipeline.

In the end, the data could feed into analytical software to be processed, resulting in analyses, intelligence for business, and real-time dashboard visualizations which various parties can utilize to improve marketing strategies or to examine trends.

Establishing a layer of data governance will ensure that businesses have control over the integrity of data as well as data availability and security while ensuring compliance with regulatory standards like GDPR and the COPPA.

Data Mesh for Modern CPG Data Architecture

As the demand for data increases, there could be a requirement for a shared framework in order to accommodate domain-specific data consumers, with each domain having its own pipelines of data. The Data Mesh is a kind of data platform that allows companies to have a greater demand for data that can be consumed. It can help deal with the complexity of the data pipeline and enhance data observability and recoverability and help to monitor the condition of data assets throughout their lifespan.

Data mesh is a cutting-edge method that permits CPG businesses to manage increasing amounts of data by offering greater flexibility, data experimentation and creativity. Data meshes are designed to facilitate collaboration between CPGs as well as their partners which allows external and internal consumers and producers of data can exchange data.

The changing preferences of customers and the increase in competition have prompted consumer companies to invest in modernizing their data and analytics in order to ensure that their business strategies and models to the changing CPG Analytics trends and consumer needs. The creation of a modern and robust data pipeline structure will provide the foundation for the long-term plan that is in the pipeline for implementing a customer-centric approach for CPGs over the next years.


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