April 2022 Summaries

Data fabric is an emerging concept that combines multiple data delivery technologies to create centrally governed, flexible data pipelines, enabling enterprises to connect information silos and define how data flows, ultimately gaining valuable insights and creating new opportunities. Data fabric aims to unite data access and make tech stacks more manageable, supporting many enterprises facing roadblocks to their data, such as data silos, incompatibility, and scarce technical talent. It finds hidden opportunities by making complex digital landscapes more compatible and flexible, leveraging modern concepts like cloud technologies and real-time intelligence to connect existing and new solutions, allowing legacy solutions to work alongside modern resources in the cloud with minimal disruptions. Data fabric connects and translates data from its sources to make it browsable for user consumption, ensuring that data is governable and secure across an organization, operating in a multi-phase flow to deliver context-rich data, and simplifying and democratizing data management by implementing adaptive, non-technical data bridges to unite teams.

In the software industry, customer churn is a significant challenge that can impact revenue and corporate valuations. To address this issue, providers must focus on integrating their applications with existing workflows to create a seamless experience for customers. By understanding their target users' needs and goals, product managers and engineers can design comprehensive coverage of essential use cases, prioritize integrations to boost in-app activity, improve productivity by connecting with complementary applications, and expand the impact of the product across the customer organization. Through these integration strategies, software providers can reduce churn, increase revenue, and nurture a loyal customer base.

Our newest 2022 release of CData DBAmp brings significant improvements, including an entirely new driver engine for improved performance, expanded support for DBAmp Ultimate customers, and updated documentation. The update features enhanced Salesforce connectivity solutions across various tools like SSIS, Excel, Tableau, and Power BI, allowing users to leverage more data integration capabilities. With the release of CData DBAmp Ultimate, users can access a suite of additional Salesforce integration products, providing new ways to connect, update, and work with their Salesforce data. The new driver engine runs as a Windows Service on the SQL server machine, improving stability and performance. Existing customers will need to upgrade their installations to receive these core updates.

A B2B business can benefit from adding an e-commerce platform to its capabilities, as it simplifies the process of generating and satisfying orders, but these platforms must interface with other aspects of an enterprise data ecosystem to provide a comprehensive solution. A siloed e-commerce platform can hinder business growth and visibility into success by not integrating order data throughout the data ecosystem. Data automation services solve this problem by facilitating communication between the e-commerce storefront and the rest of the data ecosystem, centralizing the connection between various SaaS applications and reducing friction and error within the data flow. To choose a suitable data automation service, consider universal connectivity, intelligent data transformation, and simplicity and accessibility, as these features enable seamless integration with other data endpoints, transform data into usable formats, and provide non-technical users with easy access to data, respectively. A data automation platform like CData Arc can provide complete e-commerce data connectivity without coding, allowing businesses to quickly automate order processing and integrate data with their analytics, reporting, and data management tools.

CData Sync has expanded its support to include PostgreSQL Change Data Capture (CDC), allowing users to track events and feed changes from their source data to multiple downstream systems in real-time, streamlining data integration processes and improving business decision-making. By leveraging CData Sync's CDC capabilities, organizations can prevent system slowdowns, alleviate network traffic overload, and accelerate business decisions, while maintaining consistent information and performance across their data-driven workflows.

In today's fast-paced business landscape, organizations are struggling to keep up with the ever-increasing amount of data they generate, with Gartner predicting that 80% of organizations will fail to scale their digital business by 2025. To address this challenge, businesses are considering either building or buying a data integration approach. Organizations can leverage multiple solutions such as embracing data fabrics, focusing on DataOps, and amplifying their data integration processes. A reliable data access layer is crucial for propelling business forward, democratizing data, and enabling data-driven decisions throughout the organization. When choosing between building or buying a data access layer, organizations should consider factors such as streamlining development, taking action in real-time, being future-proof, benefiting everyone equally, and introducing universal compatibility across all data touchpoints. With the long-term costs of in-house custom builds often outweighing the benefits, specialized data integration providers like CData Drivers offer high-performing standards-based solutions that can simplify the journey towards making diverse tools work together.

Data integration is the process of uniting data from multiple sources into a single source of information, allowing organizations to move and transform raw data from disparate applications and systems to a data storage solution. This enables teams to easily distill their data into relevant, actionable insights, making better business decisions, gaining holistic views of customers, and streamlining operations. Data integration solves data silos by connecting systems, moving transformed data across the organization, and preserving master data sets while relocating critical information to downstream databases. The process typically includes replicating, cleansing, mapping, transforming, and migrating data to a data warehouse, database, or data lake. Various patterns, such as Extract, Transform, Load (ETL), Extract, Load, and Transform (ELT), Data Streaming, Data Virtualization, and Application Integration, are used to implement data integration. Effective integration streamlines the process of sharing and using siloed information, providing benefits like better collaboration between departments, security, efficiency, and cost savings, as well as enabling organizations to make stronger, well-informed decisions. However, challenges such as standardized interfaces, growing business demands, integrating external data, and choosing a suitable data integration tool provider must be considered when evaluating solutions for data strategy.

You Are Losing Customers to Missing Integrations` As prospects increasingly look for solutions with better integrations, businesses are facing a data fragmentation crisis, with most enterprises leveraging over 200 applications and generating more than a petabyte of data per year. To address this, solution providers must scale up their integration capabilities, providing native integrations that unify customers' data and meet the core requirement for every solution they add to their IT ecosystems. With the rise of logical data warehousing, businesses are seeking platforms with rich data connectivity across multiple data sources, eliminating custom coding and saving customers from waiting on IT teams to set up their tool with necessary integrations. Most ISVs have some basic integrations, but it can be a tall task to obtain and maintain the breadth and richness of data connectivity necessary to become competitive in today's tech market.

Data silos are collections of data held by one section of a company but not readily available to other departments, often due to an individualist mindset and technological challenges. This leads to bottlenecks, hindered decision-making, and affects the bottom line. The cost of data redundancy is also significant, with duplicate data and discrepancies causing standardization and compliance issues. As a result, businesses can lose valuable time and resources that could be used elsewhere. However, using a data connectivity platform like CData can help organizations eliminate data silos, resulting in organization-wide cost efficiency and improved productivity. By bridging disparate data sources and digital workflow tools, businesses can demolish data silos and improve business outcomes for all.