What is Data Fabric Software?
Data fabric software is a unified data platform that enables organizations to integrate their data and data management processes. Adopting a data fabric allows for the creation of complete views of their data, helping power existing processes and applications and enabling the rapid development of new use cases. A data fabric is not just a single solution but an entire data ecosystem that connects disparate data sources and infrastructure types across locations (on-premises, in the cloud, or hybrid environments), enabling analysis without onerous data integration requirements. The software offers benefits such as the ability to explore and extract value from any form of data regardless of location by connecting stores of structured and unstructured data. It provides centralized access via a single, unified view of an organization’s data that inherits access and governance restrictions.
Companies use data fabric software to gain greater visibility into often highly complex and heterogeneous data landscapes. Data fabric software offers deeper insights and control over their data irrespective of where it sits, enabling better business decisions and strategies. Helping businesses become data-driven is key to the emergence of data fabric software and it can be adopted by any industry vertical. Fraud detection and security management, sales and marketing management, and governance and compliance management are some of the major use cases driving the growth of data fabric.
To qualify for inclusion in the Data Fabric category, a product must:
- Perform data management processes on a single unified platform
- Pull and connect or collaborate on data from disparate sources across locations
- Manage data across all environments (multi-cloud and on-premises)
- Allow single, seamless access and control to data across sources and types
- Provide analytics tools and connectivity to other analytical solutions
- Offer metadata functionality with data currency and data lineage capabilities
The purpose of data fabric
At a basic level, the purpose of data fabric is to provide a better way to handle enterprise data. It does this by replacing copies with controlled access, and by providing a method for separating data from the applications that create it. This approach restores control to data owners while actually making it easier to share data with collaborators.
7 key components of a data fabric solution
Data fabric is relatively new, and there are many solutions being offered under the name data fabric. However, only a handful of solutions are what you can consider to be true data fabric technology. Here are the components you should look for when choosing a solution:
1. A network-based design with universal controls instead of data copies
By definition, a data fabric must be designed as a network. It is this network-based design that forms the foundation for everything else a data fabric can deliver. Furthermore, the data fabric should take advantage of this network structure to offer universal access controls for your data.
If you’re familiar with setting permissions in a cloud-based productivity suite, you understand the basic premise here. Instead of sharing copies of data, you’re setting permissions for users to access your single source. A data fabric should allow you to control these permissions at the data level, meaning you can set data permissions once, instead of on an app-by-app basis.
Because these controls are embedded at the data level, they will exist wherever that data appears. For example, you can give the Marketing Team permission to see client email addresses. You set this permission once, and any time a client’s email address appears as a dataset it will be viewable by the Marketing Team. This cuts hours of work from managing data permissions.
The networked design and data-level permissions eliminate the need to copy data from app to app and perform integration projects. This further reduces the time and cost of building new tech, while setting the stage for meaningful data ownership and privacy.
2. The capacity for autonomous data
Until now, data has always been tied to the application that created it. This is the root problem behind the current dependence on copying data and performing costly integration projects. Data fabric offers the ability to separate data from the application, creating autonomous data – data that exists independently and can be accessed by multiple applications without requiring point-to-point integration efforts.
This autonomous data has a number of uses and makes for an incredibly efficient way to build new solutions. Think of the way APIs allow you to reuse code for new applications – data fabric should allow you to reuse data in a similar fashion. New tech can leverage data that’s already on the fabric, so the solution you created for X can easily be adapted to Y without having to rebuild key components.
Autonomous data also gives you the ability to easily add new features and capabilities to legacy systems. These projects can traditionally be very frustrating, as even the ones that “should be simple” tend to be anything but because of legacy systems’ rigid and brittle architecture. Working with a data fabric, it becomes much easier to “teach your old dogs new tricks” by augmenting existing applications with new capabilities.
Data fabric represents an end to the familiar (but highly inefficient) buy/build/integrate paradigm. Creating solutions on a data fabric should cut build times in half simply by eliminating the need to carry out point-to-point integration projects, and it can offer additional benefits from there.
3. The presence of plasticity
Plasticity is the ability to reshape and reorganize existing information in a more efficient manner. It’s what lets your brain handle more data than any company on the planet—it constantly self-optimizes to make more efficient connections between the things you learn. In fact, studies have shown that a high IQ score correlates with having fewer such connections.
Currently, point-to-point integration means that your data architecture has the maximum amount of connections possible… which would make for a very low IQ score. Data plasticity means that these connections can be streamlined to create actual intelligence for the enterprise. This has never been meaningfully replicated in machine data before.
For enterprises, plasticity eliminates barriers that limit schema evolution. Builders can create integrations via data contracts (i.e. models) to prevent integrations from breaking as the data fabric schema evolves over time. This allows you to change your data schema without breaking any internal or external dependencies, including relationships to and from other tables, APIs, or queries.
By enabling the evolution of schema, the data model is free to evolve similarly to how the human brain continually adapts as it takes on new information.
4. Meaningful data ownership
Meaningful data ownership is vital to protecting personal privacy and enterprise security, and can be viewed as a foundational step for entering the hyper-intensive data future of AI/ML, IoT, and other emerging technology.
As such, there’s been a recent push from lawmakers to create and enforce data ownership regulations. But every integration project means new copies of data, and today’s enterprises can have thousands of data copies to manage. With so many data copies, there’s really no such thing as “data ownership.”
Any attempts to control data, including the GDPR and other such legislation, are thus a moot point until data copying has been curbed and data ownership has actual meaning. Data is only as secure as its most vulnerable copy, and any attempt to guarantee control over data without first doing something about all these copies is like attempting to control the value of currency without doing anything about counterfeiting.
By virtue of its ability to eliminate copies and control access, data fabric should provide an ideal platform for establishing and enforcing meaningful data ownership.
5. Active metadata
Metadata is data about the data, and it’s the key to unlocking most of the magic of a data fabric. Traditional metadata is inactive, severely limiting its usefulness. A data fabric makes this metadata active, meaning that it is updated in real time and can be queried, analyzed, and otherwise interacted with just like traditional data. This is where the true power of a data fabric comes from.
By activating metadata, it becomes possible to have universal data operations and streamline the whole end-to-end process of managing data and changing data and structures. It is this activated metadata that allows for standardized governance and a universal data API, which are key ingredients of the data fabric.
And because active metadata is updated in real time, you can change data-capture events to connect both upstream and downstream sources into the fabric. In other words, it is this activated metadata that allows for the vital component of plasticity in your data fabric architecture.
As a whole, active metadata facilitates data management in an intuitive way. This is the very essence of data fabric technology.
6. Metadata-driven experiences
A true data fabric should have the capacity to replace traditional applications with experiences powered entirely by metadata. For the end user these experiences will be indistinguishable from an API or app, but creating them is as simple as working with data in a spreadsheet.
Fully fledged metadata-driven experiences require a fairly mature data fabric with a robust assortment of connected data sources, making them a future state technology. But the foundations for these experiences should exist in any current tech calling itself a data fabric. Namely, the ability to use active metadata in such a way that it replaces the need for coding in the traditional sense.
These metadata driven experiences promise to reshape the way solutions are built in the future, giving more power to the data owners and allowing business users to create custom data solutions without involving IT resources. The benefits of this are plentiful, from faster build times to easily personalized solutions.
Imagine giving your team members the ability to create their own customized solutions for working with their data, even if they have no technical ability beyond working in a spreadsheet or SQL – that’s exactly what these metadata-driven experiences promise to do.
7. The capacity for network effects
Perhaps the most promising benefit of a true data fabric is the capacity for network effects. This is a phenomenon where a network becomes more efficient and more effective as more nodes are connected. The first telephone, for example, was pretty pointless until the invention of the second telephone, and it only got better as more and more phones were networked together.
Data fabric delivers this same result for enterprise data; the more data that already exists on the fabric, the easier it is to leverage towards new solutions. This is a direct 180 from today’s model of point-to-point integration, where projects become more complicated and more costly over time.
With a true data fabric, the more you use it, the more efficient it will become.
Why use data fabric software
Data fabric software offers a number of benefits.
It makes build times significantly faster, powering digital transformation efforts. It allows for low-code and no-code solutions, giving data owners and other business users the ability to solve problems without taking up valuable IT resources (if someone can work with spreadsheets or SQL, they can create APIs via a data fabric).
Data fabric eliminates data copying, forming the foundation for meaningful data ownership. This helps future-proof solutions ahead of new data privacy laws, which are being introduced regularly.
Data fabric introduces the compounding efficiency of network effects for data. The more you work with your data fabric, the more effective and efficient it becomes. This gives tremendous competitive advantage to early data fabric adopters.
Data fabric has a low cost of entry. There is no downtime from standing up a new fabric, simply pick an existing project and use your new data fabric to build the solution. It will exist in tandem with your legacy systems, and grow organically as you use it for future projects.
As enterprises and suppliers adopt cloud computing, edge computing has also become increasingly important. Smartphones, tablets, laptop computers, traditional PCs and, perhaps most importantly, IoT devices are now being pressed into service as part of major applications. This has created an imperative to deploy technologies designed to bring these devices “into the fold.”
This means technology designed to serve in the following ways must be deployed as well. This technology must make it easily possible to:
- Leverage data across devices and locations and format that data in appropriate ways so that it can be used at the edge, in the data center, and in the cloud.
- Coordinate the flow of data to support real-time processes.
- Support application processing as the data is flowing at the edge, in the data center, and in the cloud.
- Gain insight centrally and push intelligence out to the edge.
- Support ways for the environment to react quickly to improve service, reduce costs, mitigate risk, ensure security, and, of course, make it easy for enterprises to manage an increasingly complex environment.
It is clear that the industry needs to coalesce on a comprehensive definition that includes the following things:
- Combine data from established systems as well as these new edge computing solutions.
- Provide necessary speed, scale, and reliability to support enterprise-grade applications.
- Support multiple locations that can include many data centers, cloud service providers, and, of course, the systems on the edge of the network.
- Support the notion that execution/computing can be anywhere — not just describing support for the flow of data.
- Create a unified data environment even though systems at the edge and systems in the data center might view data differently.
- Support redundancy to support high levels of reliability and availability.
Once a final definition is agreed upon, then the industry can go about the business of creating international standards describing how Android, iOS, Windows, Linux, UNIX, and other computing environments can work together.
It would be wise for enterprise developers and decision-makers to consider the concept of a data fabric and finding ways to bind edge computing, data center computing, and cloud computing into a reliable, scalable tool now before the need is thrust upon them by changing market conditions. It would also be wise for them to take the time to review what suppliers are saying and determine if their view of the topic is comprehensive enough to provide needed solutions to the problems they’re likely to experience as their customers, suppliers, and competitors are defining the digital future.
In a nutshell
Data fabric technology is often compared to data virtualization technology, and both offer innovative ways for handling enterprise data. But there’s an important difference between the two: data virtualization simulates change, while data fabric offers real change to the physical structure of your data. It’s the difference between putting on VR goggles to take a virtual tour of the Grand Canyon, and actually being there.
Data fabric is very much real. Major enterprises in global finance and other data-heavy industries are already relying on it to revolutionize the way they handle their data. And their early reactions are extremely positive. Data fabric allows these corporations to create solutions faster than ever before possible, and to do so while eliminating data copies and protecting data privacy to create meaningful data ownership.
Data fabric is a promising new technology that has the potential to end the buy-build-integrate paradigm that’s dominated enterprise IT for the past 40+ years. But because data fabric technology is so new, it’s important to understand what critical components and capabilities make up a true data fabric platform.
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