Three distinct trends in today’s predictive analytics process.
1. Predictive hypothesis testing
As businesses begin to ask more strategic questions about what’s really driving their performance, executives are understandably demanding proof before executing on this new era of insight. Specifically, businesses want to understand the cause and effect of various sets of data, and know that this analysis can be extrapolated over different periods of time. This new form of analysis, powered by machine learning, is critical to businesses looking to gain a competitive edge.
2. Closing the gap between data and delivery
The hunger for Big Data doesn’t end with merely gathering the right kind of data. From the thousands of data sets available, executives who are looking to better leverage data to solve complex problems need more streamlined ways to glean insight from the variety of data being collected. Currently, companies often deal with this by creating their own analytics platforms, which is very expensive and doesn’t support all facets of the predictive analytics process. Companies are looking for easier ways to close the data gap and are turning toward more streamlined cloud computing in order to speed up the time to insights.
3. Shrinking the barrier between internal and external data
While internal systems have been consolidated for years, the influx of external data is creating unforeseen silos within businesses. In order to increase efficiency and streamline workflows, companies have implemented web-based data transactions, which have created a great divide between internal data and external data. Companies that are spending thousands on a tool to gather a wide variety of external data sets are now faced needing to spend even more on a solution to combine this data into their internal workflows. As such, executives are demanding easier, quicker and less expensive ways to close this barrier.
Embracing the new predictive analytics process
The diversity of data sets and sheer amount of external data continues to grow with the speed of technology. Global companies certainly recognize its power, but only now are they beginning to find ways to glean real business value from the insights this data can provide.
From implementing more seamless processes for gathering and correlating external data to finding flexible solutions that enable hypothesis testing and analysis of various data sets, companies looking to fully leverage Big Data to solve big problems must embrace this new era of predictive analytics. By incorporating these three trends into business processes, companies can truly see what’s driving their performance and, ultimately, stay ahead of global competition.
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1. Predictive hypothesis testing
As businesses begin to ask more strategic questions about what’s really driving their performance, executives are understandably demanding proof before executing on this new era of insight. Specifically, businesses want to understand the cause and effect of various sets of data, and know that this analysis can be extrapolated over different periods of time. This new form of analysis, powered by machine learning, is critical to businesses looking to gain a competitive edge.
2. Closing the gap between data and delivery
The hunger for Big Data doesn’t end with merely gathering the right kind of data. From the thousands of data sets available, executives who are looking to better leverage data to solve complex problems need more streamlined ways to glean insight from the variety of data being collected. Currently, companies often deal with this by creating their own analytics platforms, which is very expensive and doesn’t support all facets of the predictive analytics process. Companies are looking for easier ways to close the data gap and are turning toward more streamlined cloud computing in order to speed up the time to insights.
3. Shrinking the barrier between internal and external data
While internal systems have been consolidated for years, the influx of external data is creating unforeseen silos within businesses. In order to increase efficiency and streamline workflows, companies have implemented web-based data transactions, which have created a great divide between internal data and external data. Companies that are spending thousands on a tool to gather a wide variety of external data sets are now faced needing to spend even more on a solution to combine this data into their internal workflows. As such, executives are demanding easier, quicker and less expensive ways to close this barrier.
Embracing the new predictive analytics process
The diversity of data sets and sheer amount of external data continues to grow with the speed of technology. Global companies certainly recognize its power, but only now are they beginning to find ways to glean real business value from the insights this data can provide.
From implementing more seamless processes for gathering and correlating external data to finding flexible solutions that enable hypothesis testing and analysis of various data sets, companies looking to fully leverage Big Data to solve big problems must embrace this new era of predictive analytics. By incorporating these three trends into business processes, companies can truly see what’s driving their performance and, ultimately, stay ahead of global competition.
Source:
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