How Embracing Data Fabric Can Raise AI Initiative’s Potential

 

 

 

End users have simple access to the information they require to support their work and decisions thanks to data fabrics and data mesh.

Data and analytics leaders are closely cooperating with their IT and CX colleagues to align on strategies for optimal results in order to maximize the business value from their tech and AI initiatives. In the meantime, their most valuable resou rcetheir data is becoming exponentially larger and more unstructured.

By 2025, 80% of enterprise data will be unstructured, according to IDC, but the remaining 20% of structured data will be the basis for the majority of business decisions. Because many businesses find it difficult to operationalize and interpret data that is held in unstructured formats, corporate knowledge management plays a critical role.

A strong data management strategy is essential to the success of any AI plan. Data architecture patterns like data fabric and data mesh are causing a number of trends to emerge in this industry. These strategies are being used by organizations to meet their demand for business information and to create a structure that is optimal for governance and security. Data fabric has been popular mostly because it can scale to meet a business’s demands and is flexible enough to react to complex data expansion.Data leaders must make this a foundation of their data strategy to make the most of their AI investments.

The Relevance of Contextual Data

Knowledge management systems are essential for providing the data required to feed artificial intelligence (AI) with the emergence of AI, including LLMs and AI-enhanced applications. Informal knowledge sharing is a viable strategy for IT and data executives attempting to leverage their data for meaningful purposes, but it is not scalable. Formalizing that knowledge is the goal of the metadata-centric approach, which includes knowledge graphs, ontologies, and metadata management. Unfortunately, these representations generally have limited utility since they are not always linked to the data that produced the knowledge.

Business executives of today strive for data agility and the solution of challenging data problems. They wish to create, connect, interpret, and use data, but not just any data will do all of the data’s existing knowledge must be connected with it. Because of this, context such as the information’s source, intended use, and intended audience becomes crucial. In order to enable the rapid interpretation of real data, there is a growing need to update enterprise designs to include a semantic layer. It needs to be coherent, exhaustive, and precise.

Constraints of Current Data Management Techniques

Organizations would copy data into a designated analysis area a few years ago from sources such as their salesforce or ERP systems. After then, strategies shifted to exploiting data lakes to extract unstructured information. Organizations now have even more systems to integrate, so they must ensure that the data is correct, consistent, and not duplicated when stored in many locations. A data warehouse’s ability to provide a single data source is its main benefit, but its structure can also be very restrictive.

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The data warehouse needs to be completely updated when integrating new systems from outside sources, however modifications can take a while to deploy. Because of this, the data warehouse ages far too quickly. Every day, more and more data will be uploaded, necessitating real-time analytics requirements. This implies that although the data warehouse or data lake is gathering data from silos, it is also functioning as a silo in and of itself.

The Definition of Data Fabric

One approach to gaining understandable access to corporate information is through data fabric. Data fabrics combine data and metadata into a single, unified view that maps the organization’s information assets and permits on-demand access to reusable knowledge by utilizing a semantic layer. Better teamwork, increased self-service for data analytics, and decreased dependency on IT teams for data operations are all encouraged by this.

The semantic layer minimizes complexity and unites data from many sources in a data catalog. Meanwhile, data schemas are automatically read to keep data up to date. This data is presented back as a knowledge graph or an information network of semantically linked facts that’s intuitive to data users and readable to Large Language Models.

Variations In between Data Mesh and Data Fabrics

The goal of data fabrics and data mesh is the same: to provide end users with simple access to the information they require to assist their work and decision-making. In order to distribute ownership, encourage accountability and openness inside the company, and enable the individuals who have the greatest understanding of the data to autonomously manage and administer it, the data mesh focuses on creating data products that are unique to a domain or business unit. The main distinction between the two architectures is that data fabrics have more centrally located governance. In actuality, both strategies have drawbacks and are challenging to apply in a way that closely complies with the description.

Most organizations will benefit from a composable, mix-and-match approach, depending on their metadata maturity and governance policies. It’s imperative to keep in mind the business objective of evolving an enterprise architecture is to establish a central point of access. In many cases, a hybrid approach between a centralized data repository, integration and security combined with federated governance would work best.

Gartner predicted that, by 2027, 30% of enterprises will use data ecosystems enhanced with elements of data fabric supporting composable application architecture to achieve a significant competitive advantage.

Benefits of a Connected Data Management Platform

As AI evolves, tech experts and business teams are still learning the associated issues and limitations. While structured data management, necessary for analytics, dominated the field for decades, AI feeds on unstructured data formats like documents, logs, video and more. This is now pushing organizations to prioritize qualitative data as a central pillar in their Data and AI strategies. Few platforms can work with all those data types, however. They will typically massage it into a form that’s right for them perhaps into rows and columns.

The native data can be used in its original form by an integrated, specialized data management platform. To make it possible for the AI to interact with all of those sorts of records, it can store them as graph data, rows, columns, and lists. It can also build a knowledge model, business lexicon, and active metadata.

With a single platform that is full of features, enterprise businesses can provide outcomes more quickly and with less risk. To enable data fabric, a data platform unites components that could otherwise need to be replicated among numerous vendors and open-source components. Using a data management platform offers data managers the following three main advantages:

• Connect – Integrate all your data and metadata in a single enterprise-grade data platform.

• Create – Model your enterprise knowledge graph and use semantic AI to create metadata.

• Consume – Deliver unified, high-quality data in context to its use case and audience.

 

 

The Challenge of Keeping Data Current and Accurate for AI

 

While AI and machine learning present a terrific opportunity for every organization, the biggest question is around what will keep LLMs or small language models honest. The answer is data.

A sizeable challenge with enterprise deployments of machine learning projects is keeping the data current and accurate. Teams must validate the answers for hallucinations, particularly in the case of tools like ChatGPT, and provide lineage and traceability of those answers.

This requires bringing back some elements of the answers to the data that was used to make that decision; meaning, linking elements of the answers back to trusted, accurate internal data that is managed. Data fabric doesn’t just use a standard answer it uses an answer based on your enterprise data plus semantics.

Consider a retail use case like product traceability. It would be a struggle to trace an off-the-shelf product back to a distribution warehouse, a manufacturing line or a raw materials supplier the company investigated three months prior and had a quality control problem with. That supply chain visibility and transparency is something that is going to become increasingly important. Knowledge management at each step of the journey is critical.

Powering AI Potential with an Enterprise Data Service

 

The only way to achieve data agility and get optimum value from AI opportunities is by deeply integrating active data, active metadata and active meaning. Anyone who wants to connect those applications and deliver data value that spans multiple data sources should consider data fabric.

Once teams realize that the data is locked in the applications they use, architectures will invariably move from app-centric to data-centric. A single platform that keeps the meaning of data and metadata and the facts they contain together will improve an organization’s data management by becoming a powerfully harnessed resource.

Using this technology, CIOs and CDOs can expand their data universe with proper knowledge management and make more insightful AI-based business decisions with more of their data. Real-time pipelines will allow them to ingest, curate and consume data faster and achieve data agility so they can pivot and respond nimbly to change.