The Fundamentals of Predictive Analysis

Predictive Analysis

Analytics is playing an increasingly important role in our lives thanks in large part to internet of things (IoT) developments and a greater appreciation of Big Data. With solutions that range across business productivity, health care, individual and national security, new insights are regularly generated. But just as such technology enriches our day-to-day lives, it produces considerable privacy and security vulnerabilities.

Unlimited Possibilities with Big Data

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In a recent IDG Research Services study, in partnership with SAS, it was noted that IT and business leaders are expressing excitement about Big Data’s potential impact on process optimization and customer experience, along with product and service innovation. People are quickly learning that Big Data has great value, but it’s taking a little longer to understand how. We’re faced with an explosion of stored data as cloud data-housing tools making large-scale retention possible, the popularity of IoT devices collecting information that was previously discounted, and social media churning out customer needs and wants faster than ever. The analysis of Big Data, however, promises us the possibility of understanding everything. How specific diseases are spreading; which politicians will gain public approval; why products succeed or fail; when to withdraw your pounds and reinvest in the dollar.

The Value of Predictive Analytics

The key, however, to accessing all the benefits of Big Data, is effective analytics. Merely employing analytics isn’t enough, and so analysts are advancing into the data-supported forecasting of predictive analytics. In its entirety, predictive analytics helps organizations discover, analyze, and act on their data, using historical information to uncover trends and calculate probable results. Further, predictive analytics provides a framework for periodic analysis which results in more precise insights and enhanced strategies.

Analytics, Cyber Security, and Safeguarding Information

As always, what benefits the good can just as easily aid the corrupt. Data privacy and security is a weighty concern that’s gaining momentum as the collection and access of data increases. With private and sensitive information collected through financial applications, healthcare systems, social structures, and consumer databases, just about everything concerning an individual or organization is viewable to those who, legitimately or not, gain access to the data. And so, much worth is placed on cybersecurity strategies, data protection measures, and the control of both data and the tools we use to collect it. Organizations must implement robust cybersecurity plans that encourage security as a standard practice; employee education, too, is fundamental to a protected environment, as is network awareness, suitably trained security staff, and the implementation of sustainable security solutions.

Event Trails

Of course, there is another security slant to analytics which should be considered, and that is the security it can provide. Already, advanced analytics are used in the fight against money laundering, bank and insurance fraud, and evolving cybersecurity threats. It can potentially even help welfare organizations prevent child abuse. While security experts previously focused mainly on security tools that would prevent cyber attacks and keep sensitive information locked away from prying eyes, cybersecurity analytics is a new weapon in the arsenal. Because it’s almost impossible to completely prevent data infringement, it’s important to consider what can be done after an intrusion occurs. Every attack leaves a network event trail, such data forming the digital DNA of the attacker, and this data is the beginning of analytics value to cybersecurity through investigation and cybercrime detection.

Big Data and analytics may still be in the early stages, but the value we’re already experiencing suggests a future of improved solutions, practical forecasts, and advancement through mere bits and bytes.

Article sponsored by SAS Software and Big Data Forum

By Jennifer Klostermann

Deepak Jayagopal

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