Data is a force that is transforming industries before our eyes. Decision makers in every sector now have access to more data from more sources than ever before. And businesses that can capture higher value from these new data sources—weather, healthcare, traffic, retail, social sentiment—and use it to differentiate themselves, will be the ones to lead in their sectors.
Just imagine if your organization could:Personalize loyalty and retention offers for individual customers in real-time.Resolve maintenance issues before they disrupt operations.Detect the probability of fraud in transactions as they occur.
Clearly, the ability to find insights in structured and unstructured data and use that knowledge to successfully anticipate upcoming actions is critical. But how? Enter data science
– the practice of extracting knowledge from massive amounts of data, using methods such as statistics, machine learning, data mining, and predictive analytics. This discipline is revolutionizing the way organizations solve problems and gain competitive advantage.The transformative power of predictive analytics
Perhaps the most far-reaching and significant method in the discipline of data science is predictive analytics
, which enables organizations across sectors to reinvent the way they conduct business by identifying what is likely to happen in the future, and determining how they will respond to it. Organizations that have adopted predictive analytics are empowered to:Optimize operations and processes. An auto insurance company knew 80 percent of claims were minor and the risk was negligible. The goal was to determine which cases fell into the precious 20 percent of major claims so they could be prioritized. To that end, IBM built a system that could determine, in real time, which claims to fast-track. When customers called in, the claims person had all their pertinent data, which made for a more positive interaction. This had an amazing impact on customers, so the company modified the call-center process, which improved customer relations, closed claims faster with less staff and reduced fraud. All of this resulted in huge cost savings.Prescribe the next best action: A telecommunications company identifies high value customers who may be at risk of churn due to dissatisfaction with service quality. The company uses predictive analytics to correlate the health of customers’ modems with the results of satisfaction surveys, enabling it to predict which dissatisfied customers are at risk of churn and intervene with the appropriate solution. Automate and simplify processes: An oil and gas company uses predictive analytics to identify potential equipment maintenance issues across its vast network, conducting pattern-matching and analysis across all relevant data streams, and generating warnings and notifications for engineers and operators in the field. The majority of these issues would go undetected without the ability to automate the analysis.
The power of data science is in its ubiquity. It is not a crystal ball. But by anticipating the most likely outcome or behavior, such as who will buy a specific product, default on a loan or credit card, file a fraudulent claim or require a certain medical procedure, firms can significantly improve the accuracy of thousands of decisions made every day.
How can your
organization benefit from data science? Attend our webinar on December 14, 2016 with Eric Siegel, founder of Predictive Analytics World and author of Predictive Analytics: The power to predict who will click, buy, lie or die.
He’ll share real-world examples of how organizations across industry sectors are transforming their business processes, and offer a glimpse under the hood to help you understand how data science works. Whether you work in retail, manufacturing, insurance/financial services, public sector or healthcare, you will discover how to use the vast amounts of data you collect to improve your business and keep your competitive edge in a constantly-evolving world. #bigdata#datascience#EricSiegel#events#industry#predictiveanalytics#SPSS#SPSSStatistics#Webinar