Data fabric speeds up strategic decision-making and creates a culture of trust, thus positively impacting business
Going beyond traditional practices, data leaders now are shifting toward AI-powered modern data management agility solutions, meaning data fabric. With the data landscape constantly changing, a data fabric ensures data analysis, analytics, and AI efforts can be trusted because they are based on a complete view of all accurate data across the organisation.
It simplifies data access and facilitates self-service data consumption while remaining neutral to data environments, processes, and utility. According to IBM, using metadata-enriched AI and a semantic knowledge graph, a data fabric identifies and connects data from disparate data stores and continuously discovers relevant relationships between the available data points.
As a result, a data fabric self-manages and automates data discovery, governance, and consumption, thereby allowing organisations to minimise their time to value. By attaching master data management (MDM) and MLOps capabilities to the data fabric, data leaders can have an end-to-end data solution accessible by every division within the organisation. Additionally, the data fabric approach allows data experts to create repeatable pipelines, data ingestion patterns, and a unified metadata layer so that data can be understood across the business.
Although data fabric has been around for a decade now, the hype around the technology has just started building. Due to the pandemic, many enterprises transitioned to the cloud. However, cloud adoption without a proper data governance strategy slows business analytics — mainly because data is spread out and hard to manage efficiently. Data leaders vouch for data fabric as a solution to positively impact business outcomes.
Data fabric can clear data landfills, as Gartner states. It speeds up strategic decision-making and creates a culture of trust. But data leaders must adopt the technology after addressing data health and their company’s data culture.
There are many use cases of a data fabric. It allows you to anticipate client demand and shifts in product sentiment and operate more efficiently.
For example, for traditional retail supply chains, developing a backorder forecast model takes weeks since sales data, inventory, and supplier data reside in separate data warehouses. On the contrary, a data fabric can predict product backorders fast and help maintain optimal inventory levels and prevent customer churn.
Data fabric’s self-service data catalogue classifies data, associates metadata to business terms, and serves as the governed data resource to create a model. Data leaders can use the record to find necessary data assets, while semantic knowledge graphs within the data fabric make relationship discovery between assets more accessible and more efficient.
What’s more, data fabric provides a unified way to create and enforce data policies and rules while ensuring the appropriate privacy controls. Data leaders can use their MDM capabilities to generate golden records and enable a smoother experience when integrating data assets for analysis.
According to IBM, by exporting an enriched integrated dataset to an AutoML tool, data scientists can spend less time wrangling data and more time optimising their machine learning model.
This backorder forecast model, built upon a data fabric, allows supply chain analysts to have an accurate view of inventory and predictions and ensure that stocks are prevented, increasing overall revenue and improving customer loyalty.
Data fabric reduces time to insights by unifying fragmented data on a singular platform, not just the retail or supply chain space.
Across all industries, one of the main reasons to create a data fabric is to improve the usability of different types of data. A multi-faceted, multi-layered data analytics program is the best way to achieve valuable insights and stay ahead of the competition.
Creating a company culture around healthy data is the key to maximising a return on data fabric investment. It’s crucial to enable employees to engage, collaborate and add value across the lifecycle of the company’s data. According to experts, when data teams engage the company’s true data experts, like a marketing team, with collaboration workflows supported by their fabric, decision-making gets faster, and the data fabric delivers true business value.
It allows organisations to become fully informed about the state of the business. Healthy data empowers executives and decision-makers to make choices and pick directions faster, ultimately positively impacting business.
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