Analytics for Big Data

Companies are just now beginning to harness the power of big data for the purposes of information security and fraud prevention.

Only 50% of companies currently use some form of analytics for fraud prevention, forensics, and network traffic analysis.

Less than 20% of companies use big data analytics to identify information, predict hardware failures, ensure data integrity, or check data classification, despite the fact that by doing so, companies are able to improve their balance of risk versus reward and be in a better position to predict potential risks and incidents.

Banks, insurance, and other financial institutions use big data analytics to support their core businesses. Large volumes of transactions are analyzed to detect fraudulent transactions and money laundering. These, in turn, are built into profiles that further enhance the analysis. Some insurance companies, for example, share and analyze insurance claims data to detect patterns that can point to the same fraudulent activities against multiple companies. Healthcare is another area in which data analysis can be used for information security.

Big data can arise from internal and external sources, spanning social media, blogs, video, GPS logs, mobile devices, email, voice, and network data. It's estimated that 90% of the data in the world today has been created in the past two years, and some 2.5 million terabytes of data are created every day.

Although many companies already use data warehousing, visualization, and other forms of analytics to tap into this high-volume data, using that data to prevent future attacks or breaches remains relatively uncharted territory. This is changing and will continue to do so as security increasingly moves from being a technical to a business issue.

To balance the business benefits of big data analytics with the cost of storage, organizations need to regularly review the data they are collecting, determine why and for how long they need it, and where and how they should store it.

The Human Element of the Big Data Equation

Because data volumes grow considerably every day, deciphering all the information requires both technology and people-driven processes. People often find patterns that a computer can pass over. Some other steps organizations can take to analyze big data for information security purposes include the following:

• Identify the business issue;
• construct a hypothesis to be tested;
• select the relevant data sources and provide subject matter expertise about them;
• determine the analyses to be performed;
• interpret the results.

Most companies struggle to find value from their customer analytics efforts. Lack of data management, integration, and quality are the biggest inhibitors to making better use of customer analytics. 54% of surveyed companies have difficulty managing and integrating data from the many varied sources, while 50% are concerned about consistent data quality.

Companies also struggle with assembling the right type of analytics professionals, communicating the results of the analysis to relevant colleagues, performing real-time analytics and making insights available during customer interactions, protecting data and addressing privacy concerns, and keeping pace with the velocity of data generation.

While key drivers of adoption include increasing customer satisfaction, retention, and loyalty, analytics use skews largely toward acquisition of new customers. 90% of surveyed companies use analytics for this purpose.

Other factors driving the use of analytics include reacting to competitive pressures, reducing marketing budgets, and addressing regulatory issues.

The use of predictive analytics as a growing trend, with 40% of organizations use it. 70% have been using descriptive analytics and business intelligence reporting for more than 10 years.

Organizations that have already mastered basic analytics methodologies and gained efficiencies in aggregate analysis are now looking to adopt advanced ways to do real-time, future-looking analysis.

Additionally, companies would like to start using social data as a viable source of customer analytics. This was cited as a long-term goal by 17% of the companies in the survey.

Organizations should look beyond social media for unstructured data. While many marketers have embraced social media as an effective way to engage customers, from an analytics standpoint, they have only scratched the surface in how other data sources, such as call center data and voice-of-the-customer data, can feed traditional customer analytics processes.

Analytics can also be used to improve customer engagements. Customer engagement features at the bottom of the list of metrics. This is a missed opportunity for customer analytics practitioners to gain deeper insight into how individual customers interact with content, offers, and messaging across various touchpoints.

Customer analytics practitioners do a number of things right, including focusing on the right types of analytics and methodologies to achieve a basic understanding of who their customers are, their propensity to buy, how to target them effectively, and how best to experiment with content, features, and offers. But despite this, companies should develop a holistic customer analytics solution framework.

Although individual customer analytics techniques answer specific business questions, they fail to deliver efficiency in generating insights at an aggregate level.

Organizations should look outside their own four walls and connect with partners who are knowledgeable in analytics technology, analytical services, and data mining to explore the next steps for customer analytics. It is not just about buying an analytics tool, it is also about employing the professional services to make sense of the data.

Galaxy Consulting has 16 years experience in the area of analytics. Contact us today for a free consultation and let's get started!