Data Fabric is an upcoming approach to handle data using a network-based architecture rather than point-to-point connections. This allows the creation of an integrated data fabric (layer) straight from the source levels to insight generation, application, orchestration and analytics. In addition, a layer of extraction is placed over the underlying data components so that insights and information are available to users without any duplication or compulsory data science efforts.
Most companies are struggling to evolve their data because of various complexities and its heterogeneous nature. And this is exactly where data fabric comes into the picture as it solves this problem for the companies. It provides an architecture that allows uniform data utilization covering the entire company or enterprise. Because of this reason, data fabric was selected as one of the most influential technologies in data and analytics in 2019.
Use of Data Fabric
Let’s look into the various applications of data fabric to get a better understanding of how it works:
1. Data fabric architecture support unstructured data
Companies are rapidly spanning their parameters beyond fixed workstations and on-premise servers. With concepts like BYOD (bring your own device), WFH (work from home) and IOT (internet of things), the scope of networked devices only seem to be growing. A data fabric architecture will help a company or an organization connect all these endpoints and process unstructured data collected via sensors to deliver valuable insights.
2. Data fabric architecture handles information at a large scale
The data volumes of the organizations are constantly on the rise, and those enterprises which can mobilize their data effectively get a competitive edge. Data-driven decisions can power business opportunities, enable efficient work, and improve customer experiences. With data fabric architecture, organizations can make use of data that would otherwise remain in the system.
3. Data fabric architecture is compatible with hosting hybrid environments
One of the major advantages of data fabric architecture is that it is platform-, tool-, and environment-agnostic. As a result, it is possible to sanction bidirectional integration with almost every component from the technology stack, creating an interwoven architecture. This works wonders for hybrid-cloud or multi-cloud organizations, where it is important for the data initiative to run consistently and uniformly across all clouds.
4. Data fabric architecture initiates insights at a face pace
Data fabric architecture can easily handle complex datasets, which accelerates the time to insights. In addition, there are pre-built cognitive algorithms and analytics models that process data at a large scale and speed.
5. Both non-technical and technical users can use data fabric architecture
The architecture of data fabrics makes it available to a wide array of user interfaces. For example, you can easily build simple and user-friendly dashboards that business executives can easily understand and leverage. It also comes with advanced tools that enable data scientists to drill down deep to explore data. This enables technical and non-technical users to make the most of the data fabric.
Benefits of Data Fabric
Data Fabric architectures assure a way to tackle governance and security issues raised by the increasing incidents of security breaches and new privacy regulations.
Wim Stoop, the Product Marketing Director of Cloudera, said, “By far the largest positive impact of a data fabric for organizations is the focus on enterprise-wide data security and governance as part of the deployment, establishing it as a fundamental, ongoing process.”
Companies need to take a step back and examine data management holistically with data fabric architecture in place. Even though data fabric is not a complete solution, it significantly reduces the labour associated with sticking to the required compliances.
Challenges of Data Fabric
Data Fabric may seem a great option for data management, but there is a huge gap between a perfect data fabric and what is being practised today. According to to Brian Platz (CEO & Co-founder of Fluree), “In practice, many first versions of data fabric architectures look more like just another data lake.”
People who are building data fabric for the first time don’t take into account the requirement for inherent data interoperability. Different systems have different ways to format data and data that doesn’t adhere to the schemes of a global enterprise will not speak the same language.
It is important for a company to understand its data consumption along with compliance and regulatory needs to make optimum use of data fabric. The CTO of Grax, Morten Bagai said “Not understanding one or all of those areas often creates challenges or points of failure.”
Uses of Data Fabric
To make the maximum use of Data Fabric, a company must sort out the various challenges that come with data fabric. Once all the issues are sorted, the company or organization can explore the different use cases for a Data Fabric. Let’s take a look at the different uses of Data Fabric that companies and organizations can explore.
1. AI data collaboration
AI engineers can benefit from data fabric architecture in more ways than you can imagine. It can provide access to integrative data to the AI engineers with which they can make better informed decisions.
According to Platz “Because AI needs broad access to high-integrity data, a data fabric can support the efficient delivery of information to AI applications for quick, well-informed decisions.”
He further added that the data fabric architecture can enhance the capability of AI applications to detect fraud and build faster models for anticipative analytics. In addition, the data fabric architecture provides streamlined access to data in real-time for anticipative maintenance.
2. Enhancing security
Companies and organizations can enhance their security by combining data fabric architecture with security appliances. In addition, it can improve the functioning of these security appliances by roping data with the applications from their IT and physical systems.
With the use of data fabric architecture, a team can easily improve their security by tying information from key readers together. Then it can be correlated with event data from various computer systems accessible within the facility. This allows them to make more sophisticated analyses of any abnormal behaviour and trigger real-time alerts if required.
3. Generating holistic customer views
Organizations and companies can use data fabric architecture for weaving data from customers’ activities with the different roles that link with them for a more holistic view. This can help incorporate real-time data of different sales activities, customer onboarding time, customer satisfaction metrics and potential revenue realization.
It is possible to start it with CloudTrail logs, which will monitor a customer’s use of software as a service (SaaS) on various websites, include data from the customer support and synchronize with new sales activities. With data fabric architecture, it is possible to relate different data sources to drive improved analytics and offer useful recommendations.
4. Improving business understanding
Enterprises and companies can use data fabric architecture to get a more comprehensive view of the business covering different departments and activities. Data fabric is important to understand any types of changes that are happening in the company. They can think of data fabric architecture as a landscape map of inflexion points, business outcomes and anomalies across the enterprise. This makes it the ideal testing set and excellent training for AI and machine learning to better understand the business. It also makes it simpler to implement process mining projects that make perfect sense to businesses that span across multiple applications.
5. Streamlining triggered actions and predictions
Companies and organizations can make use of data fabric architecture to configure, train and deploy prediction algorithms along with trigger actions running throughout the different enterprise application endpoints. These can span across everything from audit compliance to security traceability and also revenue-generating events that include ad optimization, marketing, customer retention, cart abandonment action and last but not least, orchestrated selling.
According to many experts, data fabric architecture can completely transform the fundamentals of how businesses learn from past experiences and evolve with time.
6. Creating a marketplace for data
It is possible for companies and organizations implementing data fabric architecture to set up a marketplace for data that makes weaving disparate data sources. With a data marketplace in the market, the data engineers can easily set up a framework that can be used repeatedly instead of creating new frameworks for each case separately.
A data marketplace is a perfect need for any company that tackles both the organization’s current and future data requirements. A single data marketplace is much better than case-specific data lakes or stores to address problems like predictive maintenance, fraud prevention and customer churn.
Conclusion
From all the above information, it is clear that the organizations and companies need data fabric, as its implementation can benefit the organization in different ways. It is considered one of the major breakthroughs after relational databases were invented way back in 1970. This is because data fabric is not just a product or a technology but an architectural design, a mindset shift, and a structured process that closely weaves businesses and data.
