According to the Professors Andrew McAfee and Erik Brynjolfsson of MIT:
"Companies that inject Big Data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers."
CIOs understand this opportunity and according to a 2012 survey of 2300 CIO by Gartner, Analytics is their number one technology priority.
Data is the critical success factor. Because without data, there can be no analysis.
Data Virtualization as an Analytic Data Enabler
Data virtualization can accelerate development of a new analytic or refinement an existing one.
With data virtualization you can easily:
- Discover available data sources across and beyond the enterprise
- Simplify access to required data sources, while complying with security and governance policies
- Combine all the data required, physically, virtually or in a hybrid combination
- Deliver the data to any number of analytic tools
- Complete all these activities quickly and easily
One-off or Recurring
Data virtualization's support for analytics can scale from one-off projects, with one-time analytic sandboxes to on-going support of multiple analytic applications via an analytic data hub.
Analytic data hubs provide greater consistency across multiple analytic applications, faster time to solution due to data set reuse and more complete governance and control.
Upsell Analysis in Telecommunications
To identify upsell opportunities within their customer base, this cable company needed to combine data from multiple customer management and operations systems. Using Composite Software's data virtualization platform they quickly created new analytics driving $21M in additional revenue.
Product Optimization Analysis in On-line Video Games
To improve the player experience for new video games and thus drive additional sales, this on-line video entertainment company needed to combine data from big data sources tracking game usage, website traffic, sales data and more. With data virtualization they identified product improvements which have leading to $9M in additional revenue.
Analytic Data Hub Design Guidance
Rick Sherman, noted business intelligence and data management analyst, consultant and educator from Athena IT Solutions recently wrote a white paper on Analytic Data Hub design entitled Analytics Best Practices: The Analytical Hub.
His paper provides excellent guidance in the form of the following five design principles.
Principle 1: Data from everywhere needs to be accessible and integrated in a timely fashion
Expanding beyond traditional internal BI sources is necessary as data scientists examine such areas as the behavior of a company's customers and prospects; exchange data with partners, suppliers and governments; gather machine data; acquire attitudinal survey data; and examine econometric data. Unlike internal systems that IT can use to manage data quality, many of these new data sources are incomplete and inconsistent forcing data scientists to leverage the analytical hub to clean the data or synthesize it for analysis.
Advanced analytics has been inhibited by the difficulty in accessing data and by the length of time it takes for traditional IT approaches to physically integrate it. The analytical hub needs to enable data scientists to get the data they need in a timely fashion, either physical integrating it or accessing virtually-integrated data. Data virtualization speeds time-to-analysis and avoids the productivity and error-prone trap of physically integrating data.
Principle 2: Building solutions must be fast, iterative and repeatable
Today's competitive business environment and fluctuating economy are putting the pressure on businesses to make fast, smart decisions. Predictive modeling and advanced analytics enable those decisions to be informed. Data scientists need to get data and create tentative models fast, change variables and data to refine the models, and do it all over again as behavior, attitudes, products, competition and the economy change. The analytical hub needs to be architected to ensure that solutions can be built to be fast, iterative and repeatable.
Principle 3: The advanced analytics elite needs "run the show"
IT has traditionally managed the data and application environments. In this custodial role, IT has controlled access and has gone through a rigorous process to ensure that data is managed and integrated as an enterprise asset. The enterprise, and IT, needs to entrust data scientists with the responsibility to understand and appropriately use data of varying quality in creating their analytical solutions. Data is often imperfect, but data scientists are the business's trusted advisors who have the knowledge required to be the decision-makers.
Principle 4: Solutions' models must be integrated back into business processes
When predictive models are built, they often need to be integrated into business processes to enable more informed decision-making. After the data scientists build the models, there is a hand-off to IT to perform the necessary integration and support their ongoing operation.
Principle 5: Sufficient infrastructure must be available for conducting advanced analytics
This infrastructure must be scalable and expandable as the data volumes, integration needs and analytical complexities naturally increase. Insufficient infrastructure has historically limited the depth, breadth and timeliness of advanced analytics as data scientists often used makeshift environments.
Additional Insights
If you are building analytics, and challenged by the data, perhaps you'll want to read Rick's entire paper Analytics Best Practices: The Analytical Hub and his companion piece, Analytics Best Practices: The Analytical Sandbox.
Subscribe to Virtualization Journal Email News Alerts
Subscribe via RSS
