More than ever before, business executives today truly understand the inherent power that information technology can provide in terms of making better business decisions. The real challenge in finding value in Big Data isn’t so much mastering any given set of advanced technologies — it lies in combining technologies in a way that adds value to the business by making it simpler to achieve results. For that, CIOs are turning to applications running in the cloud.
Rather than requiring an IT organization to stitch together technologies, tools and databases to analyze massive amounts of Big Data, cloud services like Amazon Web Services Marketplace empower end users with a robust set of analytics options that are easy to integrate, adopt and leverage. To achieve the same result on their own, CIOs would need to acquire and deploy all the IT infrastructure required to support a Big Data analytics application, develop or license an application, test and validate software, and then develop an application programming interface to efficiently share the data.
Once CIOs gain an efficient way to access and analyze their Big Data, they want to leverage that data to become more confident in the decisions they are making. They want to clearly see how those decisions might impact the business both before, and after, they are made. Visualization tools enable business users to visually inspect data in a way that gives them that confidence.
Traditional analytics platforms frame Big Data within the context of a period of time, but that can be limiting to the data’s value. For example, it’s fairly straight forward to understand how many units of something were sold over a particular period of time. It takes a much more sophisticated approach to learn both when and where those units were sold.
Location can be critical to corporate performance metrics when additional factors can have an influence on sales and a business’ bottom line. For example, weather patterns have a direct effect on retail traffic. Ideally, business managers operating in a retail setting should be able to easily correlate weather events to forecast the impact that weather might have on their retail sales.
Gaining actionable, real-time insights not only leads to better decision-making, but making decisions at just the right time. Commercial use cases for advanced analytics now include everything from a marketing and advertising application being used to import over 15 billion records per day from 110 million smartphones, to applications being used by insurance companies to minimize risks. In the public sector, a plethora of federal and state agencies are partnering with pharmaceutical companies to better identify where outbreaks of diseases are occurring.
Combining time and location within a single query requires geospatial analytics. In the past, the only options CIOs had when it came to processing those queries was to either invoke extensions to a standard relational database that compromised performance, or deploy a geospatial database that required dedicated expertise to run it. The end result was that very few organizations wound up doing either.
Traditional SQL databases can be adapted to support GIS applications, but the results are often suboptimal for numerous architectural reasons. As a consequence, supporting these requires a lot of work and expertise on the part of a database administrator and often still yields disappointing results. Alternatively, IT organizations can deploy their own spatial database which is optimized to run GIS applications. But those organizations need to fund dedicated DBA experts to manage a system that won’t meet the scale and performance demanded by users and analysts and must still deal with the issue that databases don’t render interactive graphics.
With the advent of the cloud, however, it’s easier to invoke a geospatial database service that end users can use to self-service their analytics needs using robust visualization tools embedded within the cloud service itself.
Today, organizations have access to all kinds of location-based data that is being generated by everything from end users keying in data on mobile devices to thousands of sensors continually streaming data. Spatial queries involve geometric computations which are often compute-intensive. Geometric computation is not only used for computing measurements or generating new spatial objects, it’s also needed to establish topology relationships. Spatial cross-matching or overlaying multiple sets of spatial objects on an image or map can easily wind up being prohibitively expensive to execute.
Because of those requirements, it makes a lot more sense to take advantage of elastic compute resources in the cloud to process and render spatial data. Given the fact that demand for access to spatial analytics tends to vary wildly, dedicating IT infrastructure to service those requests on-premise not only winds up costing a lot of money, it generally takes the IT organization a very long time to set up.
The end result is that CIOs now have dynamic access to massive data sets using visualization tools that incorporate a critical geospatial dimension. Business leaders across the organization caninteractively compare and contrast how changes to data will impact an organization by rapidly exploring multiple what-if scenarios without any intervention on the part of an already overworked and understaffed internal IT department.Analysts and end users can query any combination of dimensions and visualize the data instantly, which allows them to explore areas of their data that were previously impossible to reach without support from a technical team that might take days or weeks to respond.
CIOs now have the ability to layer a wide variety of data streams into their business analysis for a more complete understanding of how different factors can impact business results. Whether it is for shipping, tracking weather patterns or resource allocation, CIOs can now visually inspect Big Data in way that gives them more confidence in their business decisions. Put it all together and it becomes clear that providing access to Big Data analytics in a way that is much simpler to consume has now become a major business priority.