September 2023 Summaries

AI is transforming how businesses operate by improving processes, accelerating innovation, increasing productivity, and ensuring customer engagement through natural language processing applications like ChatGPT. A comprehensive data strategy is crucial for successful AI efforts as it ensures that an organization's data can be accessed when needed to support the development of effective algorithms and machine learning models. Data integration challenges are a major hurdle in scaling AI use, with siloed or poorly integrated data being a common issue. Evaluating and integrating data is essential, including understanding business goals, mapping out the data ecosystem, and deciding how data will be integrated. Effective data governance, including privacy, security, and ethics, is critical to establishing trust in AI initiatives, and employing trustworthy data leads to trustworthy AI. A robust data strategy can help make sense of existing data and provide a solid foundation for AI success.

The powerful JSON functions in CData Virtuality simplify data engineering by enabling users to efficiently manipulate and transform JSON data, achieving significant time savings within a singular platform. This integration empowers developers and data enthusiasts to work with JSON data effortlessly, consolidating all data-related tasks into one environment. With the available JSON functions, users can seamlessly convert data, extract specific values, aggregate arrays, construct objects, parse strings, transform data, and execute SQL-like queries on JSON data, making their workflow more efficient and faster.

Santa Clara University has adopted Workday to manage student and program data, but needed more flexible reporting capabilities than Workday provides natively. The Institutional Research department turned to CData to relieve the pain of integrating Workday data with their custom database, using a solution that connected Workday to Tableau, R/RStudio, and other tools for analytics. By connecting directly to Workday, Santa Clara University can now automatically pull live and historical data into these tools, making it easier to analyze trends and improve student experiences. The CData Workday ODBC Driver provided a straightforward solution to address their integration challenges, allowing the university to work better with Workday.

CData and Tableau have partnered to bring Zero-ETL integration and analytics capabilities to Workday, enabling users to access and analyze this critical corporate data source without the need for manual ETL processes or data replication. This collaboration aims to unlock new insights from Workday data, which has previously been challenging to connect and analyze. By providing direct, secure, and live connectivity between Workday and Tableau, CData's solutions are designed to simplify data integration and analysis, allowing users to focus on creating value with their business data. The partnership is expected to benefit organizations such as BJ's Wholesale Club, which leverages CData to better analyze HR data and reduce employee churn by 10% through data-driven insights.

BJ's Wholesale Club improved employee retention numbers by integrating their Workday data with Tableau using the CData Workday Tableau Driver, a simplified way to pull live Workday data into dashboards for easier analysis and sharing, reducing manual data extraction and "data dump" frustrations. The integration allowed the HR team to visualize and share employee data at various levels of the organization, uncovering insights that led to an over 10% increase in employee retention over 12 months.

CData is helping top organizations make the most of their Workday data by providing a simple and hassle-free way to connect Workday to hundreds of applications and systems. This allows users to solve all their Workday integration challenges, including connecting live Workday data from any tool, revealing eye-opening insights, saving multiple views, and delivering a smoother Workday experience. With CData, organizations can now run consistently accurate reports from real-time data, analyze different data across the organization within Tableau dashboards, and access all the drivers they need to integrate their enterprise data.

CData DBAmp has released its Summer 2023 update with a new feature that allows users to run certain commands remotely on SQL services, eliminating the need to install the linked server or enable `xp_cmdshell`. This expansion offers flexibility and control over where Salesforce data is housed. The remote functionality provides compatibility with various SQL Server Instances, simplified setup, and selective stored procedure support. It's beneficial for users who previously faced challenges with third-party linked servers on Azure Managed SQL Instances.

CData Connect Cloud provides real-time, cloud-based connectivity between cloud platforms like AWS and cloud data like Workday, enabling organizations to easily view their Workday data in AWS QuickSight alongside sales projections to meet profitability goals. By combining sales and HR data from separate systems, such as CRMs and HCMs, organizations can analyze trends and make informed decisions about staffing growth and hiring plans. With CData Connect Cloud, users can create customized views of Workday data, connect to it in QuickSight, and combine the data with other enterprise data for comprehensive analytics. This allows organizations to ensure their staffing plan is aligned with profit goals, reducing the risk of overstaffing or underutilization, and ultimately improving overall profitability.

CData Virtuality has integrated Python into its code editor, allowing users to run Python scripts directly from SQL without the need for separate environments. This integration enables seamless management of data workflows in a single platform, boosting productivity and reducing logistical headaches. With native Python integration, users can access an extensive array of Python libraries, unlocking infinite possibilities for data visualization, machine learning, statistical analysis, and more.

The Data Mesh approach is a decentralized data engineering method that enables business domain subject matter experts to create pipelines that produce 'data products', which are reusable data sets that can be consumed by different analytical systems. This approach has given rise to two roles: data producers and data consumers, with the latter obtaining access to data products through a data marketplace. The data marketplace is an essential enabler of the Data Mesh, providing a governed mechanism for publishing, sharing, and consuming data products. Additionally, data marketplaces can be used to gain access to external data from outside the enterprise, governing this external data supply chain and making it easy for consumers to get at value-added data they need. Data virtualization software can also be used to create virtual data products that are published in a data marketplace, enabling teams across different domains to collaborate seamlessly and driving better decisions and insights.