How a data fabric overcomes data sprawls to reduce time to insights

Data agility, the ability to store and access your data from wherever makes the most sense, has become a priority for enterprises in an increasingly distributed and complex environment. The time required to discover critical data assets, request access to them and finally use them to drive decision making can have a major impact on an organization’s bottom line. To reduce delays, human errors and overall costs, data and IT leaders need to look beyond traditional data best practices and shift toward modern data management agility solutions that are powered by AI. That’s where the data fabric comes in.

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A data fabric can simplify data access in an organization to facilitate self-service data consumption, while remaining agnostic to data environments, processes, utility and geography. By using metadata-enriched AI and a semantic knowledge graph for automated data enrichment, a data fabric continuously identifies and connects data from disparate data stores to discover relevant relationships between the available data points. Consequently, a data fabric self-manages and automates data discovery, governance and consumption, which enables

enterprises to minimize their time to value. You can enhance this by appending master data management (MDM) and MLOps capabilities to the data fabric, which creates a true end-to-end data solution accessible by every division within your enterprise.

Data fabric in action: Retail supply chain example

To truly understand the data fabric’s value, let’s look at a retail supply chain use case where a data scientist wants to predict product back orders so that they can maintain optimal inventory levels and prevent customer churn.

Problem: Traditionally, developing a solid backorder forecast model that takes every factor into consideration would take anywhere from weeks to months as sales data, inventory or lead-time data and supplier data would all reside in disparate data warehouses. Obtaining access to each data warehouse and subsequently drawing relationships between the data would be a cumbersome process. Additionally, as each SKU is not represented uniformly across the data stores, it is imperative that the data scientist is able to create a golden record for each item to avoid data duplication and misrepresentation.

Solution: A data fabric introduces significant efficiencies into the backorder forecast model development process by seamlessly connecting all data stores within the organization, whether they are on-premises or on the cloud. It’s self-service data catalog auto-classifies data, associates metadata to business terms and serves as the only governed data resource needed by the data scientist to create the model. Not only will the data scientist be able to use the catalog to quickly discover necessary data assets, but the semantic knowledge graph within the data fabric will make relationship discovery between assets easier and more efficient.

The data fabric allows for a unified and centralized way to create and enforce data policies and rules, which ensures that the data scientist only accesses assets that are relevant to their job. This removes the need for the data scientists to request access from a data owner. Additionally, the data privacy capability of a data fabric ensure the appropriate privacy and masking controls are applied to data used by the data scientist. You can use the data fabric’s MDM capabilities to generate golden records that ensure product data consistency across the various data sources and enable a smoother experience when integrating data assets for analysis. By exporting an enriched integrated dataset to a notebook or AutoML tool, data scientists can spend less time wrangling data and more time optimizing their machine learning model. This prediction model could then easily be added back to the catalog (along with the model’s training and test data, to be tracked through the ML lifecycle) and monitored.

How does a data fabric impact the bottom line?

With the newly implemented backorder forecast model that’s built upon a data fabric architecture, the data scientist has a more accurate view of inventory level trends over time and predictions for the future. Supply chain analysts can use this information to ensure that out of stocks are prevented, which increases overall revenue and improves customer loyalty. Ultimately the data fabric architecture can help significantly reduce time to insights by unifying fragmented data on a singular platform in a governed manner in any industry, not just the retail or supply chain space.

Source: ibm.com