The ability to perform live data analysis using high-quality data is crucial for organizations in many industries, but it’s particularly important for retailers. Timely insights drawn from accurate data can help improve customer satisfaction by offering in-the-moment product recommendations and ensuring stock is in the right place at the right time; optimize merchandising, marketing, and sales efforts with real-time assessments of how well promotions are performing; lower costs and risk via more-precise inventory forecasts; and more. In short, effective live data analysis has the potential to positively impact retail operations across your organization.
To get the greatest value from live data analysis, you need to implement a single optimized approach to data lifecycle management across your most critical datasets. This approach helps you
The following architecture demonstrates how Oracle Data Platform is built to provide retailers with a cohesive, comprehensive framework to manage the entire data analytics lifecycle. At its center are two critical components: the operational data store (ODS)—used to store operational data that is ingested and persisted in raw form with no transformations applied—and a data warehouse, where the data is stored in optimized form for query performance and advanced analysis.
When combined, the ODS and data warehouse create a data platform capable of more efficient and advanced analytics. The combination enables the effective application of advanced analytics and visualization tools while retaining the ability to investigate the data in its raw form to identify anomalies or insights without impacting the performance of the underlying transactional application. This approach is beneficial for retailers because it prevents contradictory and inaccurate duplication of the same source data, which, if used to inform an organization’s decisions, can cause delays, errors, and ultimately lost sales.
Let’s take a closer look at how Oracle Data Platform incorporates an ODS, data warehouse, and other key components to help retailers effectively use live data analysis.
This image shows how Oracle Data Platform for retail can be used to support the analysis of live and historical data in optimized form. The platform includes the following five pillars:
These capabilities are connected within the pillar. The operational data store is unidirectionally connected to the serving data store.
One capability connects into the Analyze, Learn, Predict pillar: The serving data store connects unidirectionally to the analytics and visualization capability.
The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by infrastructure, network, security, and IAM.
There are two (or optionally three) main methods of injecting data into an architecture to enable retailers to better analyze their data.
Data persistence and processing is built on two components.
The ability to analyze, learn, and predict is built on two technologies.
Alongside the use of advanced analytics and visualizations, machine learning models can be developed, trained, and deployed.
Governance is a critical factor to consider when building a solution such as this. Business users rely on the accuracy of key indicators from the data warehouse to make decisions. If these indicators are wrong, the decisions are also likely to be wrong. Depending on the data quality strategy you have defined, business users will likely need to actively participate in the monitoring of data discrepancies. They will have to help the IT team refine how the indicators are calculated and assist with the qualification and identification of erroneous data. This generally leads to the modification and testable improvement of the business rules.
Oracle Data Platform is built to ensure you have organizationwide access to consistent, high-quality data when and where you need it so you can do the following:
Learn how to optimize your inventory and promotions with Oracle Data Platform for retail in this use case.
Learn how to optimize your inventory and promotions with Oracle Data Platform for retail. This use case will show you how to increase sales and better meet customer demand.
Learn how a retail data platform can improve supplier lead time forecasting so you can ensure you have the right products available to meet customer demand.
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