What is a data fabric?

From the factory floor to the customer’s door, every single interaction, transaction, and decision generates data that can help predict, understand, and streamline every area of business operations. But only if it can be analysed and put to work.

Data fabric definition

Data fabric solutions enable you to connect and manage all your data, including both analytical and transactional information, in real time, across different systems and applications. It ensures that data can be shared with consistent business meaning across systems, so the logic that defines key metrics and relationships remains intact as information moves through the organisation. A data fabric also streamlines all data, especially in complex distributed architectures, making it ready for use in analytics, AI, and machine learning applications by unifying, cleansing, enriching, and securing it. In short, data fabric architecture and solutions enable businesses to make use of their data and scale their systems, while adapting to rapidly changing markets.

Data mesh vs. data fabric

Data mesh and data fabric are both data architecture concepts that aim to improve data management and integration across different systems, applications, and users. And while they both lead to more streamlined data management, there are some distinctions between the two that can help to clarify the terms.

Data mesh is a decentralised data architecture that aims to empower teams to own their own data and services. It promotes the concept of "data autonomy", where different teams can claim and manage their own data and services, and make decisions independently based on that data and their needs. Data mesh encourages teams to build their own microservices and promotes the use of APIs to share data across other teams.

Data fabric, on the other hand, is a combination of data architecture and dedicated software solutions that centralise, connect, manage, and govern data across different systems and applications. This enables businesses to access and use data in real time, creating a single source of truth and automating data management processes.

Both approaches have their advantages. Data mesh is often regarded as a later-stage initiative, once data fabric infrastructures have already been implemented. Data fabric provides a centralised and unified view of data, which can help provide insights from data across all systems. From an organisational perspective, this is the ideal approach as this infrastructure leads to optimisation across the entire business.

Bringing the “business” into data fabric

A business data fabric goes beyond a traditional data fabric approach. While it still simplifies complex data landscapes and delivers meaningful data to every data consumer – it takes the benefits and value further by keeping the business logic and application context from data intact (in essence, it maintains the data’s DNA). As such, a business data fabric eliminates the need to recreate all the business context lost from extracting and replicating data – giving business stakeholders and data consumers the ability to accelerate their decision-making with trust and confidence, knowing they always have the complete picture of their data regardless of where it is stored or how it was designed.

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What is a Business Data Fabric?

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Extending the value of a business data fabric

A modern business data fabric connects and governs data while preserving and sharing the business logic that gives that data meaning. This includes relationships, hierarchies, and calculations that reflect how the business actually operates. To achieve this, organisations need a flexible, multi-model foundation that can manage both transactional and analytical data within a single framework. When operational data from core processes can be accessed and analysed in context, without losing integrity or duplicating logic, teams can act on live insights and drive automation across applications, analytics, and AI. In this way, the data fabric evolves from a data management approach into the foundation for sharing trusted business data consistently across the entire organisation.

Data fabric architecture

Data fabric architecture operates by connecting, managing, and governing data across different systems and applications to provide a centralised and unified view. This applies to both your teams and your systems – wherever they are in your organisation. Some of the key components of data fabric architecture include:

• Data connectors: Think of data connectors as bridges that link different systems where data is stored (e.g. databases, applications, sensors) to a central location. In this way, all these disparate data sets can be analysed from a single vantage point.
• Data management: This involves ensuring that the data is organised, secure, and of high quality. Activities such as data integration (bringing data from different sources together), data governance (setting rules for how data should be used and managed), and data security (protecting sensitive data from unauthorised access) are included here.
• Data modelling and semantic layer: Data modelling helps you to make sense of the data by creating a common language for information across different systems. Modern approaches extend this concept by preserving the business semantics and logic that describe how data behaves within business processes. This shared semantic layer enables both transactional and analytical systems to operate with the same consistent understanding of data relationships and rules.
• Data processing and analytics: This is where the data is processed and analysed to gain insights. Tasks such as data warehousing (storing large amounts of data), data streaming (continuously processing data as it is generated), and data visualisation (displaying data in a way that is easy to understand) come into play here.
• Data management automation: Data analytics can be used to inform automation in various areas of the business, but as an architectural term, automation helps ensure that data is managed efficiently and consistently. This includes automating tasks such as data integration, data governance, and data security. Automation can help reduce errors, save time, and improve data quality.

Business benefits of a data fabric

A data fabric provides a means of being more accurate, efficient, and intelligent. And when cloud-based solutions are powered by AI and machine learning, the sky is the limit. Why? Because AI insights become increasingly accurate and insightful when they are given more data to process. Below are some of the key business benefits of data fabric solutions.

1. Centralised, simplified data management: You cannot afford to be disorganised. A data fabric helps you break down silo walls and enables you to find and bring together data from all your systems in one place – whenever and as you need it.
2. Quick insights: Businesses no longer have the luxury of waiting for results or hoping that analyses are accurate. With a data fabric infrastructure, no stone is left unturned – and they are all turned over in unison, in real time. This unified approach bridges the gap between operational and analytical data, enabling faster feedback loops from insight to action.
3. One source of reliable information: The best business data management systems can amalgamate cross-business data and systems to create a single view. But what’s more, those solutions can model that data, so that it’s presented to users in a way that they not only understand, but that they can act upon straight away. By embedding shared business logic into the data fabric, organisations ensure that all users and systems work from the same definitions and calculations, which improves trust and consistency across analytics and operations.
4. Automated data management: Data fabric architecture helps to automate what were once error-prone, slow manual processes – identifying trends, detecting irregularities, and minimising the risk of error and inaccuracy.
5. Adaptable and scalable: Modern businesses require the ability to pivot quickly and to seamlessly adapt their operations and business models. Data fabric solutions help you unify your processes to effect quick and accurate change.
6. Data control: Business data fabric helps organisations have better control over their data with features such as data quality checks, data tracking, and data protection, ensuring their data is compliant, consistent, and secure.

Enterprise data fabric use cases

We have discussed the general business benefits of a data fabric, including speed, accuracy, automation, and scalability. But what about more specific uses? Regardless of the nature of your business, most companies from medium-sized upwards share some basic operational essentials. Let us look at some of the ways in which data fabric solutions can have an impact on those core activities:

• Customer service: Customer data is coming in thick and fast – and from new sources every day. From your own CRM systems, right through to social media and customer reviews, valuable information is being captured. To attempt to manually categorise and analyse all these massive and disparate data sets would be essentially impossible. A data fabric enables you to get a handle on this incoming deluge. It helps you define and establish the analytical parameters you wish to set, and the types and nature of data you want to compare or focus on. It can be easily automated to examine different data sets for different outcomes – and ensure that no valuable customer data sources are left unexplored.
• Fraud detection and risk management: Cybercrime and phishing attacks cost companies billions of pounds every year. Not to mention the reputational damage that can accompany any serious, unforeseen risk. With a data fabric, you are able to look across your entire business landscape, both internal and external, to spot threats and risky behaviour before they become a problem. This means analysing data from multiple systems and sources, including transactions, customer feedback, expense reports and cost centre items, and public records – all the way to social media and news items that may warn of an impact upon your operations. Data fabric solutions can employ AI and machine learning algorithms to detect and identify patterns and anomalies in large datasets, which would be impossible for humans to detect. This provides early warning of fraud and risk and helps to protect your teams, your customers, and your bottom line.
• Sales forecasting: Business data fabric architecture improves sales forecasting by enabling organisations to integrate and analyse data from a wide variety of internal and external sources in real time. This helps to create a comprehensive view of the business’s sales data, which can be used to create accurate and reliable forecasts. When businesses are able to easily leverage the widest possible range of holistic sales data, they are better able to allocate their resources, prepare for peaks and troughs, and ultimately, provide the best service to their customers and clients.
• Smoother HR operations: Business data fabric architecture can be used to integrate data from your existing employees as well as your applicants and new starters. This gives your business an advantage by providing a company-wide view of your HR operations, from hours tracking to employee satisfaction. And it provides your HR teams with the information they need to identify and address minor issues before they become major problems. A unified view of employee data can help ensure that you are creating a workplace that is engaging, inspiring, and satisfying.
• Compliance and regulations: In the past two years, businesses have seen record settlements in employee litigation in both individual and group action lawsuits. With the increase in people working from home, increasingly decentralised corporate workplaces, and a general shift in cultural sensitivities, many businesses are overwhelmed by the amount of local, national, and international regulations they need to keep up with. Relying on manual efforts to keep up with compliance issues is becoming increasingly unrealistic. So it’s a good thing that data fabric solutions can be automated to reference the latest local and global regulations, scour your systems and records from end to end, and notify you of any compliance risks.

Examples of data fabric in action

Now that we have touched on just a few of the ways in which data fabric solutions support essential business operations, let us look at some of the sectors that are putting data management innovation to work to help them innovate and compete:

• Healthcare: Data fabric solutions help to create a centralised, real-time view of patient data. By integrating EHR data from multiple sources, healthcare providers can have a more comprehensive view of a patient's medical history, which can lead to more accurate diagnoses and treatment plans. The efficiency of clinical trials can also be improved by integrating data from various sources (such as patient data and laboratory results) into a single location, allowing for easier tracking and analysis of the trial’s progress.
• Manufacturing: From raw materials to the customer’s door, few sectors have more data generation points than manufacturing. To meet customers’ demands for transparency and ethical provenance, many companies integrate RFID data and blockchain tracking solutions into their overseas manufacturing operations. A comprehensive view of their supply chains also enables them to look far along the chain to identify early-stage bottlenecks and to anticipate shortages. IoT networks also provide them with an overview of their entire range of manufacturing assets and machines – knowing when maintenance is required and avoiding costly downtime. Finally, product lifecycles are shorter than ever, so businesses need to stay literally up to the minute with trends. By analysing market and social media data, companies can anticipate trends and communicate design changes to their R&D teams in real time.

• Oil, gas, and energy sectors: By integrating data from sensors and other field sources, companies can identify patterns and trends that may indicate potential risks or mechanical issues. In this sector, breakdowns are doubly costly because the equipment is typically highly specialised and expensive to repair, coupled with reduced energy production that leads to losses further down the line. Data fabric solutions can also improve the efficiency of production scheduling by integrating data from various sources such as sensor data, market data and weather data, into a single location. This enables energy providers to allocate resources more effectively and plan for unforeseen economic – and even political – disruptions to which their sector is susceptible.
• Retail: By now, we have all heard of omnichannel retail and the ability to move seamlessly from in-store to online shopping and delivery options. The omnichannel revolution affects more than just where you can purchase items, but all the ways in which shoppers can personalise the entire retail experience. This includes “smart” point-of-sale systems such as smart shelves and trolleys, contactless payment options, and increasingly sophisticated personalisation features. And of course, all these things lead to the creation and capture of data. A data fabric enables retailers to combine their customers’ shopping data with their supply chain and warehouse data sets, to create a highly accurate and predictive retail ecosystem.
• Financial services: Actionable customer data can come from a variety of sources including banking and credit card usage, investments, insurance, and tax applications. A data fabric can help financial services providers to manage, analyse, and protect this vulnerable and valuable data. The stakes are incredibly high in this sector when it comes to cyber security, and any crack can quickly turn into a chasm. Data fabric solutions can help businesses in this sector to fill those gaps and eliminate off-grid areas in their operations by ensuring that security protocols and visibility are in place across every area of their business.

Next steps to making data fabric solutions a reality for your business

While there are many ways your company can benefit from transforming with organisation-wide data fabric solutions, this kind of change does not happen overnight. As with any worthwhile initiative, it begins with good planning, good communication, and realistic goal-setting. Below are some of the initial steps that many of the leading businesses take on their journey towards unified data management.

1. Assess current data architecture: To chart your destination, you must know where you are at present. It is essential to audit your current processes and system to best understand the existing data sources, systems, and data flows. This will help to identify the gaps and challenges that need to be addressed in order to implement a data fabric most efficiently.
2. Define the data governance framework: When planning to manage, integrate, and govern data across your entire organisation, it is essential to first clearly define the policies, processes, and standards you will expect as you proceed. This will ensure that all your data is accurate, consistent, and secure – and will help to protect you from risk and concern.
3. Design the data fabric architecture: After completing the first two steps, you will then need to design the data fabric architecture. This will require you to identify all your data sources and create a semantic model of the data – as well as defining and establishing your plans for data governance and security protocols.
4. Implement data integration: Once the data fabric architecture has been designed, the next step involves connecting the various data sources both within and outside your organisation. And then integrating that data across your landscape, systems and users, to create a unified view.
5. Implement data governance and security: You have established the governance and security protocols you wish to adhere to. Now you have to make it happen. This includes implementing data quality, data lineage, and data masking processes, as well as establishing access and user authorisation protocols.
6. Implement data analytics: Once the data fabric has been implemented, the next step is to put it to use. The best software solutions will help you get from here to there. This includes seamless integration of existing systems and applications, secure transfer of data sets, and AI-powered insights that help you develop, automate, and deploy analytics configurations that deliver the most actionable, relevant, and real-time insights and results.
7. Change management and communications: Implementing a data fabric architecture requires a cultural change, to ensure that your organisation is prepared to adopt the new data management practices and to promote the use of data across different teams and business areas.

Data is information and information is power. Data fabric solutions help your teams to collaborate more easily, empowered with the right information and the most accurate data-driven insights. There is untapped potential hidden within your systems and your teams – contact us today to learn how to unlock that power across your entire business.