Data is the backbone of every business, and with companies increasingly migrating their data to the cloud, the demand for scalable and adaptable data management solutions is poised to surge from $2.9 billion in 2023 to $28.4 billion by 2032. To understand why these solutions are so critical, let's explain what data fabric is and how it revolutionizes data management.
What is data fabric?
Think of data fabric as a state-of-the-art fighter jet. Seamless integration is like the jet's advanced avionics system, effortlessly coordinating data from multiple sensors into one unified control panel. Real-time access is the jet's heads-up display, instantly providing precise information to the pilot. Advanced analytics is the pilot, analyzing every scenario, making strategic decisions, and turning raw data from disparate systems into data-driven insights that can give businesses a competitive edge.
This analogy highlights why data fabric is a must-have component of an enterprise platform for process automation, also known as hyperautomation. It’s a virtual data layer connecting disparate systems to provide unified access and a complete, 360-degree view of your organization’s data.
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Whether your data lives in legacy systems or multi-cloud environments, a data fabric connects your systems together seamlessly for a unified view, facilitating advanced capabilities like predictive analytics.
Leading process automation platforms combine data fabric and automation technologies to help you streamline operations. This includes robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, artificial intelligence (AI), system integrations, and business rules.
In addition to data fabric capabilities, look for a process automation platform that provides pre-built connectors to link systems like CRMs, ERPs, and database applications without building the connections from scratch.
To harness the full potential of these technologies and build a robust data infrastructure for hyperautomation, it’s essential to follow data fabric best practices. Let’s dive into six critical strategies for implementing a resilient data fabric architecture that seamlessly integrates disparate systems, ensuring real-time data access and actionable insights to support your organization’s digital transformation goals.
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Data fabric implementation: 6 best practices
1. Build an easy-to-understand data framework
In large organizations, leveraging data's business value while keeping it secure is a complex balancing act. Yes, data management can be overwhelming, but it’s possible to safeguard your data while tapping into its many benefits. Start by building an easily comprehensible data framework to streamline processes and bolster decision-making capabilities. Honing in on this pivotal best practice lays the groundwork for robust data governance and automation strategies within your organization.
However, it's imperative to establish precise data governance policies and procedures. By delineating data ownership, establishing stringent quality standards, and fortifying security protocols, you can preempt data fragmentation and inconsistencies that could potentially thwart data analysis and impede decision-making processes.
It also pays to use automation tools and technologies to expedite data integration and transformation processes. Automating laborious tasks such as data cleansing, enrichment, and validation enhances operational efficiency and mitigates the likelihood of errors, elevating the integrity of your data framework.
Incorporating data visualization and reporting functionalities within your framework is equally paramount. By furnishing user-friendly dashboards, reports, and visualizations, you empower business stakeholders to interpret and act on data insights effortlessly. Moreover, collaborating closely with business users to ascertain their specific reporting requirements ensures your data framework delivers actionable intelligence that drives tangible business outcomes.
Here's the bottom line: By focusing on the best practice of crafting an easily navigable data framework, IT leaders cultivate a structured and cohesive approach to data management that bolsters security and fosters efficiency, precision, and strategic decision-making. Taking this strategic approach augments data operations within your organization and lays the groundwork for successful digital transformation and innovation.
2. Prioritize seamless data integration
Trying to integrate data without knowing your starting point is like taking a road trip without a map—you’re bound to get lost. To navigate the challenges of data integration, IT leaders must first evaluate their current data setup.
This means taking stock of all your data sources, understanding their quality, and identifying integration points. It’s like conducting a thorough inspection before renovating a house; you must know what you’re working with. Break the assessment into manageable phases, prioritizing critical areas first. Engage with legacy system experts and use automated discovery tools to uncover hidden issues.
Regular updates and assessments are essential to stay on track and adapt to changes. The data landscape is dynamic—new data sources emerge, business needs evolve, and technologies advance. Regularly reviewing your setup ensures that your data integration efforts remain relevant and practical. With a precise map, your data integration journey will be smoother, increasing the odds of a successful implementation.
[ Want to learn more about data fabric vs. older data integration technologies? Read our related article: Data fabric vs. data mesh vs. data lake.]
3. Implement data quality checks and controls
Data is only as valuable as it is accurate. Without rigorous data quality checks, it's like trying to build a house with faulty bricks—your structure will eventually crumble. IT leaders must establish robust data quality standards to ensure the reliability and accuracy of their data. Think of these checks as a quality control process in a factory, catching defects before they cause problems. Implement automated monitoring and validation tools to monitor data quality constantly.
Review and refine these processes regularly to adapt to new challenges. Standardizing data quality rules and focusing on high-impact areas first can demonstrate immediate value and secure stakeholder support in quality initiatives.
You'll build a trustworthy data foundation that supports intelligent, confident decision-making by embedding these checks into your data processes.
4. Foster collaboration between IT and business
IT working in isolation is like trying to row a boat with one oar—you’ll go in circles. Close collaboration with business stakeholders is essential for steering your data initiatives in the right direction. Involve business users from the outset to define business requirements and validate outcomes.
Regularly scheduled meetings between IT and business units are crucial for fostering a culture of collaboration. Open communication ensures IT understands the business's strategic priorities and can align data projects accordingly. This collaborative approach ensures that the data fabric integrates disparate data sources effectively, transforming fragmented data into a cohesive and valuable asset that drives efficiencies in areas like customer service and supply chain management.
For example, Amadori, a leader in the Italian agriculture-food sector, uses Appian to simplify and modernize work in its supply chain. Using Appian’s automation and data fabric capabilities for fleet management, Amadori reduced lead time between maintenance and order generation by 466%. Now, what once took two weeks takes about three days.
However, collaboration alone isn't enough. To maximize the business impact of your data fabric architecture, it’s crucial to align implementation with your organization’s strategic goals. Let’s delve into achieving value-added alignment and making your data fabric a cornerstone of operational excellence.
5. Align implementation with strategic business goals
Think of data fabric alignment as ensuring all the gears in a high-performance machine are aligned for smooth operation. Each gear represents different data initiatives and business processes, and when they mesh perfectly, the entire machine runs efficiently and effectively.
Begin implementation with a clear understanding of your organization's strategic objectives and business requirements, involving business users to ensure their needs and insights are incorporated, especially for applications requiring real-time data and predictive analytics. Review and adjust your data projects regularly to ensure they evolve with your business strategy. This strategic alignment maximizes the impact of data initiatives, driving operational success while enabling data-driven decisions that provide a competitive advantage.
This is where quick wins become crucial. By securing early, tangible successes, you build confidence within your team and demonstrate value to stakeholders. Quick wins create a foundation of trust and buy-in, essential for justifying larger, more complex projects.
Let’s delve deeper into the importance of scoring quick wins and how these early victories can accelerate your data fabric implementation, ensuring sustainable progress and long-term success.
6. Focus on scoring quick wins
Quick wins are like the first rays of sunshine after a storm—they build momentum and boost morale. Identify and implement low-hanging fruits to show instant value. These early successes, such as integrating a key data source or automating a simple data check, quickly demonstrate improvements and build credibility.
Early wins secure stakeholder buy-in, paving the way for larger projects. They build confidence, supporting further investment in your data strategy. Quick wins also provide learning opportunities, helping teams refine their approach and build the skills needed for more complex initiatives. Starting small ensures steady and sustainable progress, leading to broader, more impactful projects.
However, the implementation journey continues after scoring quick wins. Choosing the right solution is paramount to leveraging your data fabric's power. The correct data fabric solution can serve as your cheat code, simplifying complexities and unlocking the full potential of your data initiatives. Let’s explore the key factors to consider when selecting your organization's ideal data fabric solution.
Why you need an enterprise data fabric solution
Let’s face it. Organizations today face massive unpredictability and uncertainty. They struggle to find the resources needed to efficiently meet business goals and maintain a competitive advantage while being agile enough to adapt as market conditions shift. The same challenges apply to data management. As data volumes explode and microservices become more ubiquitous, traditional data management architectures struggle to keep up.
A data fabric architecture provides a unified approach to managing data across disparate systems. For example, customer support teams need vast amounts of data from CRMs, ERPs, and legacy systems to handle diverse customer issues. As enterprises grow and acquire other organizations, they often end up with multiple instances of these systems, creating a significant challenge for support agents who must sift through multiple platforms to find the information they need to resolve customer problems.
Migrating all this data is challenging and costly. In contrast, a data fabric presents a much easier solution by leaving data where it is and connecting systems with a virtual data layer. This architecture provides a single interface with a complete view of the customer, streamlines data access, and improves support teams' efficiency. To maximize these benefits, selecting the right data fabric solution is crucial. Here's your cheat code for making the best choice.
Choosing the right data fabric solution
Selecting the right data fabric tools is like choosing a good pair of shoes—they must fit and be durable. Prioritize solutions that are scalable and flexible, allowing your data fabric to grow and change with your business.
Look for data fabric tools that integrate seamlessly with your existing systems and meet your specific business requirements. Security is paramount, so prioritize robust governance and security features to protect sensitive information and keep your compliance team happy. Lastly, choose a vendor who offers solid post-sale support and training to guide your team through implementation and beyond.
Mastering data fabric implementation
Here's a recap of what it takes to implement a resilient data fabric architecture:
Create an easy-to-understand data framework
Prioritize data integration
Implement rigorous data quality checks
Foster collaboration between IT and business
Align implementation with strategic business goals
Focus on scoring quick wins
As IT leaders build the foundation for digital transformation, they must also overcome the substantial challenges of balancing the business need for data with the responsibility to protect it. But here's the good news: strategically implementing a data fabric solution strengthens your decision-making capabilities and data security while positioning your organization for success.
[Discover how data fabric fits into a modern automation strategy. Download the Gartner® Report: Build Your Hyperautomation Portfolio.]
