Clean Data Models from Unstructured Forecast Data
Cleaned 500+ messy forecast tables into 100 accurate models, enabling reliable dashboards, better inventory planning and reduced stock issues.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
A multinational retailer specialising in clothing, beauty, home and food products, operating both offline and online, processed millions of daily sales transactions. They faced a critical challenge: over 500 conflicting tables with no single source of truth. Store managers, supply chain team and warehouse systems relied on different datasets, creating confusion and operational inefficiencies.
The objective was clear: provide the company with a single, reliable source of forecasting data that could guide inventory, shipments and planning without wasting hours correcting mistakes.
Challenges
The painful period was most obvious during busy seasons. One store doubled its toy sales over Christmas, while another ran out of jackets midway through winter. At the same time, distribution centres sent out a bunch of products that were not transported correctly. The numbers behind the forecasts were distorted because the data were all over the place. Dates didn’t match. Product codes clashed. Whole tables were duplicated. When you have more than 500 tables and none of them line up, managers spend more time fixing errors than preparing for customers.
Solutions
Cloudaeon put all the scattered files in one place. Standardised the attribute and table names, as well as the dates, so that they were familiar to all.
500+ raw tables were reduced to almost 100 clean ones, sorted and grouped, so that information connected naturally and similar kinds of data were put into a single table.
For example: forecast data along with current stock, product quantities linked with shipments, etc.
Data inconsistencies were identified and resolved to make sure the data model is accurate.
The accuracy of the model was directly related to the quality of the dashboard, enabling planners to utilise it every single day.
Impact
The impact was straightforward.
Forecasts were finally accurate enough that everyone worked off the same numbers.
Stock issues were reduced: less surplus, fewer empty shelves.
Shipments left distribution centres on time and in the right quantities.
Planning time dropped because managers stopped chasing files.
When the next summer promotions rolled in, the difference was clear. Stores had what shoppers wanted, when they wanted it. Staff were ready. And customers were satisfied.
Conclusion
Cloudaeon fixed inconsistencies by cleaning and restructuring data. We worked with stakeholders to understand each table and its attributes, removed duplicates and consolidated everything into 100 well organised tables with clear formats and names. This gave the client faster access to accurate, reliable reports. Want to see what real-time reports can do for your forecasting strategy? Contact us now.
