Enter the Machine: The Arrival of Next-Gen Analytics
As new data sources contribute to the value and volume of big data, interpretations and correlations become too complex for analysts’ existing analytical technologies, and trial-and-error — or ad hoc queries — to extract results. In essence, big data is becoming a catalyst that is changing the tools required to process and analyze these large data files.
Moving forward, retailers will need assistance in gaining more detailed context from incoming information. Executives need accurate answers that can help them make more informed, accurate business decisions — but results are subject to error due to users’ manual intervention or cognitive bias. Enter the value of cognitive systems, or machine learning.
Using artificial intelligence, cognitive systems rely on algorithms within a computer program to observe, learn, analyze, offer suggestions, and even create new ideas. Machines — or hard-core robust computers — apply historical data to a problem by creating a model and using it to predict future behavior or trends.
Unlike machine-to-machine computing, which exchanges information between networked devices or hardware, then performs action without assistance of analysts, machine learning is a transformative process that leverages computers’ artificial intelligence and algorithms to learn what is relevant without being programmed.
Applying an array of algorithms to vast data input, machine learning is conducted over what is described as “supercomputers.” These “machines” use a “factorization approach,” which identifies specific factors that impact an outcome. The machine’s network of algorithms digitizes and ingests incoming data, analyzes the information, and continually learns from this process.
Since algorithms are programmed to “get smarter” with each application,they offer more effective and deeper data mining than currently available through human-run analysis.
For example, retailers are often focused on time-based forecasts, based on what happened last year or the last timeframe they compiled sales figures. Too often however, new variables — some that are yet undetected —play a role in reshaping forecasts. Still, human beings cannot cognitively decipher and tease out the underlying factors that are impacting forecasts. Fast-forward to machine learning. The supercomputer retains details and results, but since it is an evolving entity it can also modify output as it receives new sets of data.
Innovative and forward-thinking retailers are turning to the evolving field of machine learning to gain valuable actionable insight from the mountains of shopper data collected on an ongoing basis. Download the RIS News Retail IQ Report "Rise of the Machines: Machine Learning and the Future of Analytics" to learn more about how the future of big data analysis is in the hands of machines.
Moving forward, retailers will need assistance in gaining more detailed context from incoming information. Executives need accurate answers that can help them make more informed, accurate business decisions — but results are subject to error due to users’ manual intervention or cognitive bias. Enter the value of cognitive systems, or machine learning.
Using artificial intelligence, cognitive systems rely on algorithms within a computer program to observe, learn, analyze, offer suggestions, and even create new ideas. Machines — or hard-core robust computers — apply historical data to a problem by creating a model and using it to predict future behavior or trends.
Unlike machine-to-machine computing, which exchanges information between networked devices or hardware, then performs action without assistance of analysts, machine learning is a transformative process that leverages computers’ artificial intelligence and algorithms to learn what is relevant without being programmed.
Applying an array of algorithms to vast data input, machine learning is conducted over what is described as “supercomputers.” These “machines” use a “factorization approach,” which identifies specific factors that impact an outcome. The machine’s network of algorithms digitizes and ingests incoming data, analyzes the information, and continually learns from this process.
Since algorithms are programmed to “get smarter” with each application,they offer more effective and deeper data mining than currently available through human-run analysis.
For example, retailers are often focused on time-based forecasts, based on what happened last year or the last timeframe they compiled sales figures. Too often however, new variables — some that are yet undetected —play a role in reshaping forecasts. Still, human beings cannot cognitively decipher and tease out the underlying factors that are impacting forecasts. Fast-forward to machine learning. The supercomputer retains details and results, but since it is an evolving entity it can also modify output as it receives new sets of data.
Innovative and forward-thinking retailers are turning to the evolving field of machine learning to gain valuable actionable insight from the mountains of shopper data collected on an ongoing basis. Download the RIS News Retail IQ Report "Rise of the Machines: Machine Learning and the Future of Analytics" to learn more about how the future of big data analysis is in the hands of machines.