Single Source of Truth
A Consumer Packaged Goods Case Study.
Eager to grow their international multi-brand presence, a CPG company needed to create a single source of truth from their messy, unreliable data sources.
2nd Watch worked to understand their current goals and future vision, quickly ramping up an improved and sustainable data architecture with low overhead and clear insights for executives.
With a single source of truth and ETL automation, individuals spend more time reviewing game-changing insights from reports and executives can trust leadership dashboards to provide them with accurate sales reports and forecasts.
In an industry often characterized by fierce competition and market disruption, one CPG company achieved exceptional growth with their unique and innovative product line. Combined with an honest, no-nonsense approach to business, they quickly grew a die-hard fan base. As they started their expansion into new markets, however, they realized that limitations with their current data ecosystem and analytics would prevent them from achieving their growth goals.
Eager to maintain forward momentum, they reached out to 2nd Watch to evaluate their existing practices and create a modern data ecosystem to enable a single source of truth for CPG.
On any given day, our client’s team members spent more time cleaning and manipulating messy and incompatible data than finding actual insights. Moreover, that data was often considered unreliable, with no standard definitions for metrics or golden record across functional departments to verify data accuracy and consistency. All of these shortcomings stemmed from the absence of a single source of truth, one that would create a framework for generating meaningful sales insight and forecasts on a regular basis.
“We have no visibility into our target markets to take any immediate action. We literally send emails back and forth to get data on our supply chain, a manual process that is very time-consuming.” – Client Quote
- Build a modern data platform that provides greater control over their disparate data sources.
- Improve integration of data from POS, customer, distributor, and other data sources for cross-functional views.
- Create reliable and accurate data insights in preparation for a multi-brand, international expansion.
We began by interviewing a variety of our client’s employees to understand the current state and future vision of their company and technology needs. Then, we conducted our own review of their existing sources, infrastructure, and data to determine their day-to-day needs and obstacles.
The client’s desire to quickly ramp up an improved data architecture with low overhead compelled us to implement a specific technology stack that fit their needs for low-maintenance cloud-based technology:
- Snowflake for a cloud data platform
- Fivetran for accelerated ingestion development
- Tableau for reporting with clear visualizations
- WhereScape for ETL automation capable of meeting our client’s growth
- AWS to help ingest custom flat files and manage servers
With the single source of truth in place, we encouraged organizational changes and new data governance requirements to enable their future success and total adoption.
Yet this implementation project was only phase one of our client’s data roadmap. In time, we built a foundational data platform for their ERP and data forecasting, a sustainable architecture ready for data science projects, and a leadership dashboard for executive stakeholders.
Now, the company has one source of truth for their actual sales and forecasting numbers. For executives, there is a single verified place to access this information daily with no doubt about data integrity or metric definitions. One of the early uses was tracking their performance in various markets against predicted sales, helping to adjust their strategy and expectations.
With automated ETL processes pulling from their source systems, employees can focus on extracting insights and putting their findings into action.
Additionally, this new platform opens the door to build new data science models on top of their data warehouse or add advanced views of their data. With enhanced analytics, they can improve their understanding of consumer behaviors, future sales trends, and opportunities that will be essential to expand their business now and in the future.