concept of the “data lake” — a vast, dynamic repository that stores and secures colossal amounts of raw data. PMMI’s recent white paper, Transforming Packaging and Processing Operations, offers a deep dive into the challenges and opportunities associated with data lakes, revealing how industry leaders leverage this resource to gain a competitive edge.

A data lake serves as a central point where businesses can store unstructured data across various sources until needed. One of the unique features of data lakes — and this distinguishes them from data warehouses — is that they are not schema-in. This means they can take in raw data in its native format without changing any of the data attributes from the source. They only apply schema to the data on the actionable side of the data lake once it has been processed for analytics, making it schema-out. Data warehouses, on the other hand, are schema-in, meaning the data must be structured before coming in.

Data lakes are helping manufacturers solve concrete problems. With the schema-out structure of data lakes, manufacturers organize what information they need when they need it. This flexibility makes it an invaluable tool for machine learning, analytics and real-time reporting, helping companies react swiftly to market changes and internal demands. On the other hand, manufacturers must define their data needs with a schema-in structure before using it. And in most cases, they only end up using a fraction of the data.

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