Author: Chandra Shekhar

In a data-driven world, data integration has become a cornerstone for businesses to acquire, combine, manage, and interpret data efficiently. This blog post delves into the foundational concepts surrounding data integration, analyzing its role, key methodologies, innovative strategies, and how to effectively manage its process. We will also evaluate the pros and cons of real-time and batch integration and explore prevailing trends shaping the future of data integration. Neophytes and zealous professionals alike can derive enlightenment from this walkthrough. 

Introduction: Understanding Data Integration

The colossal growth of data has transformed the business landscape, and data integration is at the heart of this transformation. As data-centered methods have become the norm in every industry, understanding what data integration is, why it matters, and its key facets is invaluable for every business.

DevOps and AI make an inseparable pair and impact businesses of all kinds. While DevOps enables speedy product development and easier maintenance of existing deployments, AI transforms the overall system functionality. The DevOps team can rely on artificial intelligence and machine learning for data integration, testing, evaluating, and releasing the system. What’s more, artificial intelligence and machine learning can improve the process driven by DevOps in an efficient, quick, and secure manner. Evaluating the importance of AI and ML in DevOps is beneficial for businesses from the standpoint of developers’ utility and business support. 

As per the Gartner report, nearly two-fifths of the DevOps community will leverage modern infrastructure and application monitoring solutions consolidated with artificial intelligence by 2023. 

The advancement in disruptive technologies has given rise to a plethora of opportunities for miscreants to fuel cyber sabotage and data integrity risk. The proliferation of cloud-based technologies has accelerated the process of data exchange and sharing—data is more easily accessible to users across business ecosystems. The odds for any user to enter a business ecosystem are more favorable than ever. 

Organizations are struggling to deal with cyber breaches and threats on two fronts—from amateur artists who find it rewarding to particular crime syndicates with intentions to destabilize the economy and disrupt the existing system. According to a report by Security Intelligence, the average cost of a data breach is $4.35 million. As per a report submitted by Cybersecurity Ventures, the cost of cybercrime will hit $8 trillion in 2023 and will grow to reach $10.5 trillion by 2025. This means the impact cyber threats will have on the world is huge. The alarming numbers will only increase if proper actionable steps are not taken. 

While Electronic Data Interchange (EDI) has been extensively used since the late 1960s, there are many companies that rely on legacy systems for streamlining B2B transactions. Legacy B2B transactions, such as Purchase Orders, Invoices, Advance Ship notices, Sales Orders, and Functional Acknowledgement receipts, encompass a set of steps to process. In order to process these business transactions, companies need access to multiple paper documents and a lot of human intervention, which makes them prone to mistakes and risky errors. However, with the support of EDI, the use of paper documents is almost eliminated. 

EDI empowers companies to automate data exchange between different applications across supply chain ecosystems. This particular process ensures that complex, bi-directional data is transferred and exchanged on time. According to a market report, the market size was expected to reach $2.1 billion by 2018 and $2.1 billion by 2020. But what are the benefits of EDI over legacy solutions of business communication and data exchange?