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A combination of events in the data management and AI environment is pressing down on businesses of all sizes, industries, and locations. Some of these have been coming for years, if not decades, such as the ongoing spread of data across multi-cloud systems. Others have lately come into greater focus: a global drive to adopt new data privacy rules, a post-pandemic expectation by consumers to be known personally across all touchpoints, and increasing scrutiny of any racial, gender-based, or socioeconomic bias in AI models.<\/p>\n
While specific point solutions have addressed some of these problems in the past, it is becoming clear that a more comprehensive solution is required – one that can meet a business’s most critical data and AI needs while also offering the easiest way to solve further challenges. The data fabric is that solution.<\/p>\n
A data fabric is an architectural solution to allowing self-service data consumption in an organization by simplifying data access. This architecture is independent of data environments, processes, utility, or geography, and it integrates end-to-end data-management capabilities. A data fabric automates data discovery, governance, and consumption, allowing businesses to maximize their value chain by leveraging data. Enterprises may increase the value of their data by providing the correct data at the right moment, regardless of where it lives, using a data fabric. We’ve listed four of the most important data fabric use cases below, along with a quick description and links to a more in-depth eBook and trial. These use cases serve as the foundation for a rich and intuitive data buying experience. This data marketplace capability will help businesses to supply high-quality managed data products at scale throughout the enterprise in an efficient way.<\/p>\n
The increasing growth of data continues unabated, and it is now accompanied not just by the issue of segregated data, but also by a lot of alternative sources spread across multiple clouds. Except for data silos, the reason seems clear and well-justified \u2014 more data provides for more accurate insights while using various clouds helps avoid vendor lock-in and allows data to be kept where it best fits. The problem, of course, is the additional complexity that impedes the real use of that data for analysis and AI.<\/p>\n
Multicloud data integration, as part of a data fabric, attempts to ensure that the right data is supplied to the right person at the right time. The availability of integration solutions such as ETL and ELT, data replication, change data capture, and data virtualization is critical for implementing the broadest range of data integration feasible. Similarly, data categorization and governance aid in determining what the “right data” is in any particular circumstance and who the “right people” should have access to it. In terms of data delivery at the “right time,” automated data engineering tasks, workload balancing, and elastic scaling should offer all enterprises the necessary speed.<\/p>\n
Data privacy rules such as the GDPR in the EU, the CCPA in California, and the PIPEDA in Canada have all been passed at the same time that corporations are refocusing their efforts on data quality rather than data volume. The price of ignoring these imperatives is expensive. Poor data quality costs firms an average of $12.9 million per year, and since January 28, 2021, $1.2 billion in fines have been imposed for GDPR noncompliance.<\/p>\n
The data fabric’s governance and privacy component focus on organization and automation. As described in the past segment, data virtualization, and data cataloging help in getting the appropriate data to the right people by making it simpler to identify and access the data that best meets their needs. Automated metadata production is intended to convert a manual process into a more controlled one. As a result, it helps to avoid human mistakes and tags data, allowing policy enforcement to occur at the point of access rather than at individual sources. Automation of data access and lineage control, as well as reporting and auditing, contribute to a business culture that understands, adheres to, and is aware of how each piece of data has been utilized. As a result, more meaningful data is generated with less effort and greater compliance. We are pleased to inform you that MANTA Automated Data Lineage for IBM Cloud Pak for Data will be available in June. This feature will give data consumers visibility into the origin, transformations, and destination of data as it is utilized to generate products.<\/p>\n
Because of the worldwide pandemic, customers increased their adoption of digital contacts with enterprises, emphasizing the benefits of a business that was attentive to their specific needs online, in-person, and in hybrid settings (such as curbside). As we return to our everyday routine, the customer’s expectation of convenience and customized treatment persists. High-performing organizations have recognized this and have prioritized enhancing the customer experience over the next two to three years.<\/p>\n
The data fabric solves this need by providing a set of capabilities that provide a more full, 360\u00b0 picture of each consumer. Self-service data preparation tools are a helpful first step in preparing data for matching across data sets. The properties of the customer may then be auto mapped for a trainable intelligent matching system. Entity resolution, once matched, helps guarantee that identity data are of high quality and shows links between entities. Data is then cataloged to apply more information via metadata, virtualized for access regardless of location, and displayed to make identifying data quality and distribution easier and to enable faster data transformations for analysis.<\/p>\n
As the public becomes more aware of how AI is used in enterprises, models are being analyzed more carefully. Any suggestion of bias, especially when it comes to race, gender, or socioeconomic class, has the potential to erode years of goodwill. Beyond popular opinion and moral imperatives, however, being able to trust AI implementations and readily explain why models arrived at making inferences leads to better business decisions.<\/p>\n
The data fabric allows MLOps and Trustworthy AI by building confidence in data, models, and processes. Many of the above-mentioned capabilities assist to build trust in data by delivering high-quality data that is suitable for self-service consumption by those who should have access. Model trust is founded on MLOps-automated data science tools that provide openness and accountability at every level of the model lifetime. Finally, trust in processes delivered by AI governance results in consistent repeatable processes that help not just with model transparency and traceability, but also with time-to-production and scalability.<\/p>\n
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