Understanding Data Fabrics
What is Data Fabric?
Data fabric is an end-to-end data integration and management solution, consisting of architecture, data management and integration software, and shared data that helps organizations manage their data. A data fabric provides a unified, consistent user experience and access to data for any member of an organization worldwide and in real-time.
Data fabric is designed to help organizations solve complex data problems and use cases by managing their data—regardless of the various kinds of applications, platforms, and locations where the data is stored. Data fabric enables frictionless access and data sharing in a distributed data environment.
Why Use a Data Fabric?
Any data-centric organization needs a holistic approach that overcomes the hurdles of time, space, different software types, and data locations. Data needs to be accessible to users who need it, not locked away behind firewalls or located piecemeal in a range of locations. Businesses need to have a secure, efficient, unified environment, and future-proof data solution in order to thrive. A data fabric provides this.
Traditional data integration is no longer meeting new business demands of real-time connectivity, self-service, automation, and universal transformations. Even though collecting data from various sources is not usually the problem, many organizations cannot integrate, process, curate, and transform data with other sources.
This crucial part of the data management process needs to happen to deliver a comprehensive view of customers, partners, and products. This gives organizations a competitive edge, allowing them to better meet customer demands, modernize their systems, and harness the power of cloud computing.
Data fabric can be visualised as a cloth, spread across the world, wherever the organization’s users are. The user can be at any place in this fabric and still access data at any other location without any constraints, in real-time.
Data Fabric is More Than Just a Network
The internet was created to connect human beings across the world, giving people the ability to ignore the hurdles of time and distance. However, initially it was only connecting people, and the transfer of quantified data was minimal. Today, activities on digital platforms have surpassed initial forecasts, and data has become a world in itself. Any activity which is quantitative, either online or in real-life, can be classified as providing data. While this data grows by leaps and bounds, it is necessary to establish an infrastructure to manage it.
Earlier, the objective was to manage data, and as a bonus, extract insights from it. As time went by, the focus started moving from simply managing data to being able to extract insights from that data. With a data fabric, the focus is shifting from simply managing data, to enhancing the quality of the data itself, availability of the information, and the automated insights derived from it.
Why Use a Data Fabric?
Worldwide, the number of stakeholders entering the networked environment is increasing. Everyone is connected to the internet, and every platform has become a source of data. Maximizing the value of data has become a complex problem. Challenges of today’s data include:
- Located in multiple on-premises and cloud locations
- Structured and unstructured data
- Different data types
- Different platform landscapes
- Maintained on different file systems, databases, and SaaS applications
Data is growing exponentially, so these problems are multiplying.
Together, these problems and variations make it complex to easily access or use data. And, if organizations want to productize or operationalize AI and ML, they need their data collected, transformed, and processed.
Today, most organizations tend to deal with the problem in silos, creating many different ways of managing the data throughout one organization. Though this solution makes the data available to particular groups, accessing it company-wide becomes nearly impossible, often relegating the data to sit idle and unused.
Lack of comprehensive data access and use results in poor return on investment on the infrastructure, lack of availability of data to produce useful predictions, and lower productivity. It is under such conditions that data fabric comes to the rescue.
Data Fabric vs The Status Quo
Currently, many organizations use data lakes and data warehouses for managing data. However, on closer inspection, these approaches are technology intensive rather than data centric. With data lakes and data warehouses, the emphasis is to collect or extract the raw data, store it, and use it when insights are derived. These solutions were not designed with today’s problems in mind and make it difficult to get a unified view of the data.
However, these techniques often lead to latencies and increasing cost. With the growing amount of data and the time constraints with which the decision makers of an organization work, delays in data access and processing is not desirable. In such scenarios, data fabric gives the advantage of storing, extracting, and processing data at the source point in real-time, allowing decision-makers to have insights on the go.
Data Fabric vs. Data Virtualization
Data fabric often gets confused with data virtualization. Data virtualization creates a data abstraction layer and is often relied on when you need to integrate data quickly. It connects, gathers, and transforms data from many different sources, whether on-premises or cloud, for agile, self-service, and real-time insights. On the other hand, data fabric refers to an overarching, end-to-end data management architecture used for broader use cases—such as customer intelligence and IoT analytics, including a larger set of stack components. Analysts recommend using data virtualization as one tool that contributes to your data fabric architecture. As you utilize more and more data integration tools, you can grow your solution into a data fabric that’s specific to your organization’s goals.
Implementation of Data Fabric
Data fabric begins with online transaction processing (OLTP) concepts. In online transactional processing, detailed information about every transaction is inserted, updated, and uploaded to a database. The data is structured, cleaned, and stored in silos at a center for further usage. Any user of the data, at any point in the fabric, can take the raw data and use it to derive multiple findings, helping organizations leverage their data to grow, adapt, and improve.
Successful implementation of data fabric requires:
- Application and services: Where the necessary infrastructure for acquiring data is built. This includes development of apps and graphical user interfaces (GUIs) for the customer to interact with the organization.
- Ecosystem development and integration: Creating the necessary ecosystem for gathering, managing, and storing the data. Data from the customer needs to be transferred to the data manager and storage systems in a manner that avoids loss of data.
- Security: The data collected from all sources is to be managed with proper security.
- Storage management: Data is stored in an accessible and efficient manner, with an allowance to scale when required.
- Transport: Building the necessary infrastructure for accessing the data from any point in the organization’s geographic locations.
- Endpoints: Developing the software defined infrastructure at the storage and access points to allow insights in real time.
How Does Artificial Intelligence or Machine Learning Work with Data Fabric?
In the initial phases of data storage, data engineers and data scientists tried to connect the dots in data to find patterns. They found that with traditional data integration techniques, they were spending most of their time in data logistics rather than learning about the data. This is not sustainable if we want to get to insights faster.
A data fabric is essentially a data operational layer that not only brings all the data together, but transforms and processes it using machine learning to discover patterns and insights. Without a data fabric, all of this has to happen in each individual application, which is not a very sustainable solution.
A data fabric can prepare data to meet the needs of AI and ML automatically and at sustainable levels. Machine learning can provide the data and insights proactively, helping decision-makers have better insights and more timely information. The desirable outcomes lie in discovering hidden facts from the data without being specifically looked for or requested, while finding solutions for problems or business insights.
Risks with Data Fabric
A rising concern for organizations is the threat to data security when it is being transported from one point to another in the data fabric. It is mandatory that the infrastructure for the transport of data embeds security firewalls and protocols to ensure safety from security breaches. With an increasing number of cyber attacks hitting organizations, security of data at all points in the data cycle is paramount.
Benefits of Data Fabric
Data fabric is ideal for organizations that are geographically diverse, have multiple data sources, and face complex data issues or use cases. Remember, a data fabric is not a quick answer to integrate and process your data. For that, you can turn to data virtualization.
With continued advancements in hardware capabilities, globalization is expanding into previously unconnected regions. With connectivity speeds rocketing in pace, organizations can be overwhelmed by data from devices and services. While data has been used for quite some time for insights, data fabric provides a solution, which encompasses:
- An agile model that allows changes to systems, adapts and adjusts as needed, and works across all operating and storage systems
- Scalable with minimum interference, no investment in massively expensive hardware or highly trained and expensive staff
- Providing maximum integrity and complying to regulations, while maintaining accessibility and real-time flow of information
The massive amounts of data that businesses can access needs to be exploited to derive unique insights. Areas including forecasting, sales and supply chain optimization, marketing, and consumer behavior give the organization a competitive edge and data leadership in its field. Real-time insight derivation can make the organization a cut above the rest.