The growth of big data is accelerating. IDC predicted the big data market will reach $23.8 billion in 2016 and $43.3 billion in 2017. Gartner estimated big data spending on big data infrastructure and services will total $232 billion by 2016. Those projections might be understating it. According to Wikibon, big data spending was over $27 billion already in 2014.
That spending is coming because more and more companies are getting into big data. Gartner reports that 25% of companies will be using big data for security and fraud detection by 2016. Considering all the additional use cases for big data, there are lots of opportunities for value-added resellers to profit from helping customers build the data center infrastructure needed in order to support big data analytics.
General Big Data Use Cases
Big data lends itself to certain general kinds of analytics that can then be applied to industry-specific applications. The most common use cases are in the areas of customer analytics, operational analytics, and fraud and compliance. One change coming in 2016 is that companies that have become comfortable with those initial use cases will be expanding to new use cases in order to help with critical decision-making and influencing the market. Accenture reported that companies will be applying big data analytics in order to achieve these six goals:
- impacting customer relationships
- redefining product development
- reorganizing business operations
- creating a more data-focused business
- optimizing the supply chain
- fundamentally changing their business
In order to achieve these aims, companies adopting big data projects will look at the following types of projects:
- recommendation engine
- sentiment analysis
- risk modeling
- fraud detection
- marketing campaign analysis
- customer churn analysis
- social graph analysis
- customer experience analytics
- network monitoring
- new product R&D
Industry-Specific Big Data Use Cases
Most of the big data work to date is in the software and computing industry, with other large users being financial, manufacturing, and retail businesses. But usage is spreading in other industries, including entertainment, telecom, utilities, and healthcare.
Retail usage of big data analytics is increasing, with sales analytics usage expected to increase by 58% in 2016. The motivation? Four times as many high-performing teams use predictive analytics. Other big data uses in the retail business include fraud detection, personalization, supply chain management, dynamic pricing, and forecasting. With these applications, retailers can target their marketing, effectively manage loyalty programs, and optimize their yield.
The healthcare industry is looking for big wins from small improvements in efficiency; a 1% efficiency gain might lead to $63 billion in savings. Big data in healthcare will improve treatment studies by reviewing database records of patients not officially enrolled in the study. Cancer diagnosis and monitoring will become quicker and easier, through using analytics techniques in order to assess genetic information in blood that indicates tumor response to treatment. Big data analytics will also help pharmaceutical companies review data for drug discovery.
Insurance companies are committed to big data projects, with 39% of billion-dollar companies having projects underway. The biggest current use cases are in risk evaluation; newer projects will apply to customer acquisition and customer service work. For automobile insurance, expect companies to start using data collected by vehicles in order to provide information that will inform pricing based on actual behaviors rather than assumed group norms.
Online travel sites will use big data in order to personalize the user's experience, by making recommendations based on social media analysis. They'll also make more sales by analyzing the customer's browsing behavior.
The usage of smart meters will spread, as will the use of building automation in order to manage energy usage.
Online retailers will extend use of predictive analysis in recommendation engines. They'll make use of cross-channel analytics in order to link sales to specific marketing campaigns. Big data analytics will help give customers more personal experiences on the websites they visit.
Fraud detection usage will continue to expand. Big data analytics for risk management will also expand. Trades will be made based on analytic assessments of data; big data analytics will ensure that trades are in compliance with all appropriate regulations.
Analytics will underlie digital marketing, including clickstream analytics and targeted ads. Social media analytics will be used in order to understand the audience and derive and monitor campaigns and loyalty programs.
Fraud detection, threat analysis, and other security concerns are primary uses of big data in government. Big data also has a role in politics; campaign managers use social data analysis in order to target voters. With 2016 being a presidential election year, that may be the big data use case with the biggest impact in the near future.