In the business world today, a single customer may interact with businesses at various touchpoints. Their data may span multiple locations like hybrid cloud environments, databases, CRM systems, and web and mobile applications. Managing and accessing these data for business needs can become stressful, time-consuming, and inefficient, hence the need for a central management system layer that gives a holistic view of data when needed.
This virtual, central management layer can be a data fabric. A data fabric is an abstracted data management platform that helps unify and ease data management and access within an organization. Data fabrics have become an essential component for businesses, which accounts for the projected growth of the data fabric market at 23.8% CAGR to $4.5 Billion by 2026.
Let’s look at data fabrics and their application in various processes like Machine Learning (ML) operations, operational procedures, and monitoring.
What Data Fabric Design Is—And What It Isn’t
A data fabric is a design approach to data architecture that seeks to connect various data sources, be it from cloud, hybrid, or on-premise locations using tools and technologies to give organizations a holistic, integrated view of their data. Data fabric employs automated and human capabilities to create and continuously improve data systems that will enhance data governance, accessibility, and collaboration among workers in an organization.
Before the implementation of data fabric, business data may be present in different locations. This practice becomes inefficient due to the following:
Workers may need to perform rigorous cleaning and transformation processes independently, requiring specific technical skills they may not possess.
Data available within one department may result in inaccessibility to other departments, sometimes leading to data silos that stifles work productivity.
Collaboration becomes tougher as there may be tedious bureaucracies involved in gaining access to other data from other sources.
These reasons may result in low-quality data, leading to poor analysis and loss of business revenue.
However, with a data fabric, one central layer acts as the connection among various sources, making it easily accessible via network-based connections.
Here are some essential components of data fabrics;
Data source layer: Internal data source systems like Customer Relationship Management (CRM) systems, websites, enterprise resource planning (ERP) software, or human resource information systems (HRIS) are present in this layer. It may also be from external systems like social media applications.
Data discovery and ingestion layer: This layer helps discover new and innovative ways to connect to the ‘right’ data that may help push business initiatives or develop new business products. An example could be connecting CRM data with social media data to gain an in-depth understanding of consumer behavior to help improve recommendations and customer experiences.
Knowledge graph layer: Data ingested from the source layer rarely arrives in a structured form; rather it’s in raw, semi-structured, and unstructured formats. Knowledge graphs help transform this data into a coherent and consistent format for use in analytics and make valuable connections between data assets, making them easily usable for consumers.
Analytics and insight generation layer: This layer involves creating pipelines that utilize the power of ML and AL advanced algorithms that generate insights based on various operational use cases.
Data Orchestration layer: The orchestration layer helps control all aspects of the data fabric, from ingestion to consumption. This layer is a vital component and helps monitor the workflow and efficient running of jobs in the data fabric.
Data access layer: This consists of the consumption layer, Application Programming Interfaces (APIs) and Software Development Kits (SDKs) that enable data delivery to consumers, and the user interfaces layer that allows data consumption at the front end. The user-interface layer may be via dashboards or visualization tools.
Data management layer: This layer helps manage and maintain data security and governance.
Data Fabric Examples and Use Cases
Let’s explore some critical use cases of data fabric in security, ML applications, and ensuring data governance.
Machine Learning and AI applications
Data fabrics create an integrated, broad view of organizational data, and help feed data into ML models at the right time, which improves ML learning models’ accuracy through knowledge graphs. Data Fabrics are particularly helpful in training of machine learning models and help provide better accuracy because they enable training on much larger data sets.
Knowledge graphs are critical components of data fabrics, as they help analyze and draw connections between various data source systems metadata. Data fabrics also help build fast, reliable ML models by reducing data preparation time and ensuring model data are reusable across multiple clouds and platforms.
Improving Security Applications and Preventative Maintenance
Data fabrics improve the reliability and security of security applications. Data fabrics help harmonize and make valuable connections between data sources like sensor logs and metrics fed from the Internet of Things (IoT) devices and applications. By drawing on relationships from knowledge graphs and algorithms, security applications immediately flag and stop any transaction that meets already set fraud criteria by AL algorithms, thus improving the security of applications.
Data fabrics also help improve monitoring systems and assist with preventative maintenance of appliances by setting alerts based on specific parameters. Hence, by analyzing collated metrics, industrial systems can perform maintenance based on results rather than on a schedule, as scheduled maintenance may be too late.
Creating a Broad, Integrated View of Customers (Customer 360)
Customers interact and create data at business touchpoints like CRM systems, social media, and websites. By utilizing a data fabric, organizations can collate data from various touchpoints to create a 360 degree unified customer profile for use by multiple departments. For instance, information that may be useful for a customer service representative but was initially only present in marketing data is now made available via the data fabric, resulting in an accurate, summarized view. Then, the marketing department can use this customer sentiment analysis to put forward targeted campaigns that match consumer preferences or segment customers. In addition, customer service and support staff may also use this unified view to create personalized and better service for customers.
Integrating Multi-Cloud Environments
Organizations running on a hybrid or multi-cloud system can bank on the platform, environment, and multi-cloud agnostic features of data fabrics. In addition, data fabrics are compatible with almost every building component of technology stacks, making the movement and bi-directional data flow between platforms easy and frictionless. Hence, organizations employing multi-cloud environments like AWS, Azure, and Google Cloud Platform(GCP) can quickly build their data fabric architecture with no friction.
Ensuring Data Compliance and Strict Governance
Organizations incur massive revenue losses due to non-compliance with strict industry rules and guidelines. One best practice of data fabric is the adoption of DataOps, which ensures strict guidelines and policies are set in place through the data workflow to ensure compliance with strict regulations. For example, strict guidelines may include data masking for Personal Identifiable Information (PII) to protect against data leaks when using production data for testing. In this way, organizations protect themselves from future penalties from regulatory bodies and build a positive brand.
StreamSets and Data Fabric
StreamSets is built from the ground up to infer metadata dynamically from data pipelines as data flows through the system. The platform uses activate metadata by publishing this information into other 3rd party tools for broader data catalog, lineage, and governance. StreamSets pipelines can be fully automated and exposed as API’s for broad orchestration across workloads. Since StreamSets supports batch, streaming, CDC, ETL, and Ml pipelines it can be used as a common foundation for a data fabric.