Gaming DI + DV

Today, Enterprise Data Warehouses (EDWs) are expanding to support higher scalability, higher performance, deeper Hadoop integration, more automation, and realtime streaming technology. Large EDW vendors have started to offer more scalable platforms that use solid-state drives and offer distributed in-memory technologies to process large amounts of data faster, delivering both predictive and prescriptive analytics that can increase optimization and deliver a strong ROI.  

Today’s EDWs enable gaming organizations to deliver actionable, timely, and trustworthy intelligence to a wide range of business users and operational systems. An EDW can organize and aggregate historical analytical data from functional domains, such as customer, finance, and human resources, that align with key processes, applications, and roles.

An EDW serves as the repository for a substantial amount of an organization’s operational history. It offers in-database analytics, predictive models, and embedded business algorithms to drive business decisions. An EDW is a robust, secure, and proven ecosystem that supports integration with data models and security frameworks, real-time analytics, automation, and a broad range of business intelligence (BI) and visualization tools. It is the foundation for BI to support timely reports, ad hoc queries, and dashboards, as well as supply other analytics applications with trusted and integrated data.

Figure 1

The next generation of EDW is expanding to support higher scalability, higher performance, deeper integration, real-time analytics, stronger security, and more automation (see Figure 1). Recent innovations include:

  • Integrating with in-memory architectures – data stored in-memory can be accessed orders of magnitude faster than that stored on disk.
  • Leveraging Hadoop to support larger and more complex data sets as well as unstructured data, such as social media analytics data. 
  • Integrating with data virtualization platforms to simplify ingestion.
  • Integrating with real-time stream processing services.
  • Supporting advanced data compression to manage larger data sets more efficiently.
  • Enabling in-database analytics to process complex functions rapidly.

The Enterprise Data Warehouse should:

  • Leverage data to better drive marketing, customer service and other corporate decision making.
  • Leverage existing gaming architecture to better understand corporate businesses. 
  • Build up over time to contain necessary historical data stores that will support long range forecasting and patron activity tracking.
  • Provide easy access to frequently needed data.
  • Create a collective view by a group of users.
  • Improve end-user response time.

These capabilities will be used to support the following business objectives:

  • Enhance the patron experience.
  • By accessing data in any format from virtually any source, existing investments in enterprise resource planning and operational systems can be extended, and enterprise data integration can be streamlined.
  • Easily create marketing campaigns.
  • Improve marketing performance.
  • Increase the number of active reward card members.
  • Increase the gaming and non-gaming revenue from these reward card members.
  • Increase cost effectiveness of marketing activities.

The data integration tool market has established a focus on transformational technologies and approaches demanded by data and analytics leaders. The presence of legacy, resilient systems and innovation all in the market together requires robust, consistent delivery of highly developed practices.

Data Virtualization

Data virtualization provides an agile data platform to support new and emerging business use cases. It delivers a data services layer that integrates data and content on-demand from disparate sources in real-time, near real-time, streaming, and batch to support a wide range of business processes. Automated processes can update, transform, or cleanse data provided through the data services layer. A critical component of data virtualization is the metadata catalog, which keeps track of all data, its location, availability, and state and ensures trusted and timely availability of data.

Data virtualization also supports transactions that write back to the original data sources, whether online or offline, on-premise, or cloud. EA pros like its automation and self-service capabilities for data integration, access, and management, which reduce time and effort to support new business use cases. They have been expanding beyond customer analytics to support analytics for social media, the internet of things (IoT), fraud detection, and integrated insights.