Organizations can reduce the cost of diverse data access and increase analytics performance
In most industries today, increasing the power and scope of analytics is critical for successfully solving business challenges and developing innovative products and services. Decision makers in every line of business and department need analytics insights to engage effectively with customers, run operations efficiently, and evaluate risks and threats. Analytics projects tend to have a voracious appetite for data; this means that as more executives, managers, and front-line personnel work with analytics, organizations need to make careful decisions about the best way to enable personnel to find, access, and analyze their data rapidly and cost-effectively.
Analytics is also highly varied:
- Decision makers in most organizations begin with descriptive analytics to understand what has happened. They typically look at business intelligence (BI) reports of historical, aggregated data about topics such as changes in sales over time, customer spending, and inventory.
- More advanced decision makers are interested in predictive insights; they typically must rely on data scientists, statisticians, and data analysts to build models and algorithms that can analyze different data types to discover patterns and data relationships that predict what could happen.
- The most advanced organizations are taking predictive insights further into prescriptive analytics, determining what actions they can take to produce better business outcomes and developing optimization algorithms to automate responses to predicted events or patterns.
Data virtualization helps organizations address the challenges of an expanding base of users engaging in all types of analytics, which demand access to an increasing variety of data sources. Data virtualization technology provides a virtual data layer that shields users from having to know details about the data as they access and manipulate it, such as its physical location or its format. Data virtualization is the core technology behind logical data warehouses; it enables users to connect to heterogeneous data sources without waiting for the data to be extracted and loaded into a central, physical data warehouse.
Data virtualization is particularly helpful when users need actual or near real-time data access from multiple systems of record (such as ERP or CRM) in a complex data architecture. Users can assemble data views quickly by interacting with a universal middleware layer to meet dynamic needs rather than wait weeks or months for new data to become available in a typical enterprise data warehouse. Analytics closely tied to operational business decisions often demands such timely data access.