From raw data to actionable insights

Have you ever wondered how data moves through an organisation, transforming from raw information into actionable insights? In today’s fast-paced, data-driven world, having a clear understanding of how data architecture works is key to leveraging its full potential.

Sonia Sangwan, our Power BI Consultant, uses a simple yet effective analogy to explain this complex process in our packaging world. Let’s break it down step by step.

Data Lakes: The starting point

Imagine a massive storage unit where everything — raw, unstructured data — is dumped without much thought to an organisation. That is known as a Data Lake. It serves as the initial repository for all kinds of data, whether it’s SKU details, team or supplier information, timelines, artwork and design feedback or performance metrics. This raw and unfiltered data is invaluable for analysis, but on its own, it’s chaotic and difficult to use effectively.

Data Warehouses: Bringing order to chaos

Once the data resides in a lake, the next step is organising it. Data Warehouses take this unstructured mess and transform it into structured, clean, and usable formats. Think of a warehouse as the place where raw materials are sorted, labelled, and stored for easy retrieval. This is where the magic happens, enabling businesses to run analytics, generate reports, and uncover insights.

Dataflows: The movers and shakers

With structured data ready to go, Dataflows step in as the delivery trucks. These are responsible for moving specific chunks of data to various parts of the organisation. Dataflows ensure that the right data reaches the right place at the right time, supporting seamless integration between systems and enhancing efficiency.

Data Marts: Your local grocery store

Not all users need access to the entire dataset. This is where Data Marts come into play. They are like your local grocery store, offering only the most relevant and specific data tailored to particular departments or business units. For instance, the marketing team might only need customer demographic data, while the finance team focuses on revenue metrics.

 Databases: The final stop

Finally, we arrive at Databases, which act like the shelves in a store. Here, the packaged products (data) sit, neatly organised and ready for use. Databases make it easy for applications and users to access the exact information they need without wading through irrelevant data.