As businesses begin integrating Data Fabric technology, its implementation brings its own challenges. From data silos to how to secure data within data fabric, firms have their task cut out for developing a scalable data architecture. This is a look at the hurdles companies face alongside ideal strategies to adopt data fabric with ease.
Typical Obstacles to the Adoption of Data Fabric Technology
- Integration of multiple custom and standard systems poses a challenge to firms working with a multitude of legacy systems and multi-cloud infrastructures.
- Disconnected data silos coupled with varying representations of data negatively impact the efficiency and efficacy of business processes.
- Data fabric's answer to multi-domain approaches enables more agile and automated responsive architecture, raising challenges in regulatory compliance.
- Shifting the emphasis from public to private cloud and multi-cloud architectures, protection of data when compared to other infrastructures and platforms becomes more prudent in large corporations accumulating data.
The exponential growth of the cloud data fabric ecosystem does not seem to be stopping anytime soon. The North American region leads the race, holding a staggering 41% share in the cloud data fabric sector. Data security and intelligence compensate for multi-domain approaches over public cloud deployment due to the failure to achieve company-specific data segregation requirements. The global data fabric industry size was valued at US$ 1.83 billion in 2022 and is anticipated to witness a compound annual growth rate (CAGR) of 25.1% from 2023 to 2030. As cloud pathways increase, unwanted risks grow, forcing companies to seek environments providing better security guarantees, which, alongside the expected growth rate, means the global data fabric industry is bound to grow ever deeper.
How To Break Down Data Silos For Effortless Data Accessibility and Implementation
Sustaining data silos is counterproductive to maximizing the usefulness of data fabric. This is how businesses can achieve complete data flow:
- Driving Integration Using Metadata: The use of metadata to classify and integrate various datasets across different systems ensures unification and availability.
- Automated Data Pipelines: Data workflows comprising data ingestion, processing, and distribution perform with little or no manual supervision and with barely any obstacles, giving instant access to data.
- Hybrid and Multi-Cloud Connectivity: Use of connectors that make it possible to combine internal and external sources of data so that there is continuous availability of information.
For example, a global logistics firm implementing data fabric has the advantage of pulling supply chain data from different regions to enhance tracking and inventory management simultaneously. Data Fabric Enhances Data Management and Integration by allowing seamless access to dispersed data.
Assured Privacy Issues When Introducing A Data Fabric Solution
As the need to resolve privacy and security issues raises in urgency, enterprises must actively take steps to protect their data fabric environments. Some considerations are:
- End-to-End Encryption: The use of data in motion and data at rest encryption shielding using specialized techniques to avert unauthorized third parties gaining entry.
- Role-Based Access Controls (RBAC): Controlling sensitive data access limits manipulation only to authorized individuals, ensuring strict policies are accurately enforced.
- Compliance with Industry Regulations: Companies and organizations must have a data fabric strategy that aligns with worldwide compliance regulations like GDPR, HIPAA, and CCPA.
Top use cases of data fabric across industries include addressing privacy and security concerns. Data fabric environments benefit from end-to-end encryption, role-based access controls (RBAC), and compliance with global regulations like GDPR and HIPAA. For instance, a data fabric-enabled healthcare provider ensures patient records are compliant with privacy regulations throughout their storage and sharing lifecycle.
Best Practices for Scaling Data Fabric in Large Enterprises
In order to realize a scalable data architecture, organizations need to follow these outlined steps:
- Take Modular Cuts: Phased approaches to implementing data fabric enable managers to better manage changes with minimal disruptions.
- Make Use of AI and ML: Intelligent automation assists in establishing better processes for discovering, governing, and even detecting anomalies in data.
- Prioritize Optimization and Monitoring: Efficient and secure systems undergo regular audits and evaluations of performance.
Despite its challenges, the Data Fabric Implementation will always remain as an agent of digital transformation. Key developments in the data fabric industry highlight its role as a catalyst for digital transformation. By overcoming data silos and dealing with data security in data fabric while adopting a scalable data architecture, it transforms businesses. In addition, as the growth continues in the data fabric industry, businesses that follow best practices in the digital era stand to gain the most.
Sources:
Company: IBM, Informatica, Talend, Denodo, Snowflake, Google Cloud, AWS, Microsoft Azure
News Outlet: TechCrunch, Forbes, Business Wire
Trade Organization: Data Management Association (DAMA)
