How a data fabric overcomes data sprawl to minimize the length to insights

In an increasingly scattered and complicated world, data agility, or the ability to store and access data from wherever it makes the most sense, has become a concern for companies. The time it takes to identify essential data assets, gain access to them, and then use them to drive decision-making can have a massive impact on an organization’s bottom line. Data and IT leaders must move beyond traditional data best practices and toward current data management agility solutions powered by AI to decrease delays, human mistakes, and total expenses.

A data fabric can help an organization simplify data access and enable self-service data consumption while being agnostic to data environments, processes, utility, and geography. A data fabric continually finds and integrates data from various data sources to uncover important associations between the available data points by employing metadata-enriched AI and a semantic knowledge graph for automatic data enrichment.

As a result, a data fabric self-manages and automates data discovery, governance, and consumption, allowing organizations to reduce time to value. You may improve this by adding master data management (MDM) and MLOps capabilities to the data fabric, resulting in a genuine end-to-end data solution accessible to all departments inside your organization.

Data fabric in action: Retail supply chain example

To properly understand the usefulness of the data fabric, consider a retail supply chain use situation in which a data scientist wants to forecast product backorders to maintain optimal inventory levels and reduce customer churn.

Problem: Developing a strong backorder forecast method that captures these factors into the account used takes weeks or months since sales data, inventory or lead-time data, and supplier data were all stored in separate data warehouses. Getting access to each data warehouse and then creating connections between the data would be a time-consuming process. Also, because each SKU is not represented equally across data repositories, the data scientist must be able to construct a golden record for each item to avoid data duplication and misrepresentation.

Solution: By securely integrating all data sets inside the organization, whether on-premises or in the cloud, a data fabric delivers considerable savings into the backorder forecast model generation process. Its self-service data catalog auto-classifies data connect metadata to business terms and serves as the data scientist’s single regulated data resource required to build the model. The data scientist will not only be able to use the catalog to rapidly identify required data assets, but the semantic knowledge graph within the data fabric will enable relationship discovery between assets faster and more efficiently.

The data fabric allows for the creation and enforcement of data rules and regulations in a single and centralized manner, ensuring that the data scientist only has access to assets that are relevant to their work. This eliminates the requirement for data scientists to seek permission from a data owner. Also, a data fabric’s data privacy capabilities ensure that proper privacy and masking rules are implemented to data utilized by the data scientist. You may utilize the MDM capabilities of the data fabric to build golden records that assure product data consistency across several data sources and offer a more seamless experience when merging data assets for analysis. Data scientists may spend less time wrangling data and more time enhancing their machine learning model by exporting an improved integrated dataset to a notebook or AutoML tool. This prediction model could then be easily placed back into the catalog (together with the model’s training and test data, which could be tracked throughout the ML lifetime) and monitored.

How does a data fabric impact the bottom line?

The data scientist now gets a more accurate picture of inventory level patterns over time and future predictions thanks to the newly developed backorder forecast model built on a data fabric architecture. Supply chain analysts may utilize this data to prevent out-of-stocks, which enhances total revenue and promotes customer loyalty. Lastly, by combining disparate data on a single platform in a controlled way, the data fabric design may help greatly cut time to insights in any business, not just retail or supply chain.

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