What is In-Memory Analytics?
In-memory analytics helps companies manage growing volumes of data by moving their data as close as possible to their processors.
There are three main challenges that big data presents for companies: volume, velocity, and variety. With ever-growing volumes of data generated by companies today, all of that data needs to be stored, easily accessible, analyzed, and distributed to those who need it most in the organization. All of this needs to happen at a greater and greater velocity to maintain a competitive advantage and in a wide variety of different formats, adding to the complexity. In-memory analytics helps organizations overcome these big data challenges.
In the past, information was accessed from a database for analysis, leading to delays due to a slow disk drive where the data was stored and latency when transferring data through the input/output connection between the storage device and server. Using multi-core processors with servers and large amounts of local random access memory (RAM), in-memory analytics reduces latency issues to zero and enables organizations to perform big data analysis in real time.
Why is In-Memory Analytics Important?
Across a variety of industries, in-memory analytics tools are enabling businesses to access and analyze data faster than traditional analytics tools. These tools are allowing decision makers to find answers faster and giving companies a competitive edge by speeding up time to market with new products or capabilities.
In the manufacturing industry especially there is intense pressure to beat out the competition with quick turnaround times from design to market. It’s a huge challenge for manufacturers to gather and analyze huge volumes and diverse sets of data from manufacturing equipment, testing systems, operational, and supply-chain feeds to inform processes in real time. Furthermore, not all users within high-tech manufacturing companies are using the data effectively. In-memory analytics is helping more people within a company from engineers to researchers easily access the data they need from multiple, diverse systems to solve business problems faster.
Benefits of In-Memory Analytics
Business users are able to analyze much larger volumes of data with in-memory analytics—faster than traditional analytics tools. According to some studies, users are able to process more than three times the volume of data more than 100 times faster than competitors with in-memory analytics. That is due to the fact that in-memory analytics tools allow you to access and act on data without the usual latency issues of traditional technologies, giving companies with in-memory analytics an advantage over competitors in terms of quicker, smarter decision making and increased productivity.
Bringing together data from various channels, business units, and organizational functions can be difficult. In-memory analytics can help to solve this problem, integrating all of a company’s information and delivering a comprehensive view of the data to key decision makers and stakeholders to help improve decision making. As a result, companies create a data-driven culture, operationalize analytics throughout its business processes, and see significant gains in profits and productivity over the competition.
In-memory analytics helps companies get the most out of their data in less time by informing critical business decisions in near real-time. Industry leaders make their data accessible to everyone within their organization while ensuring that it is accurate and trustworthy to use. The increased access capabilities supported by in-memory analytics allows organizations to provide decision makers with the right information at the right time. This responsiveness to data and insights has helped many companies increase customer retention and acquire new customers. In-memory analytics empowers users to better understand current customer needs and react quickly to changing demands of potential customers, leading to increased sales and a better bottom line.
In-Memory Analytics Resources
Reporting, Predictive Analytics, and Everything In Between: A Guide to Selecting...
Analytics is no longer a luxury; it’s a necessity to survive. Businesses today must collect, analyze...
In-memory Analytical Systems: Perspective, Trade-offs and Implementation
This white paper examines the pros and cons of in-memory analytics with particular reference to...
In-Memory Computing: Lifting the Burden of Big Data (with Aberdeen Group)
In this webcast, Nathaniel Rowe of Aberdeen Group discusses findings from a recent study on the...