Demand for big data expertise across a range of industries saw significant growth over the past fiscal year. This is because the majority of industries have seen how dramatically their marketing strategies improve when they capture and analyze data about buyers and suppliers, products and services, as well as consumer preferences and intent.
By using big data, the retail industry has huge potential for better understanding and serving their consumers. Not only is big data a cost-effective way to gain insight of current in-store/online consumer trends and consumer behavior, it enables retailers to effectively target messaging, product creation and supply chain planning via Intelligent Analytics.
The volume and quality of available data social-network conversations, Internet purchases, and location-specific smart phone interactions has dramatically spiked. It only makes sense, then, that retailers retailing industry would adopt data-driven customization. According to a study from the Mckinsey Global Institute, retailers who embrace big data analytics yield a 60 percent boost in margins and a 1 percent improvement in labor productivity.
Big data used in real estate identifies who buys or sells what, when, where, why and how. When real estate markets implement data modeling, it gives an in-depth analysis of consumer behavior as it relates and impacts the real estate industry. As big data usage becomes more standard, agents who practice data analysis will see their revenue surge, their costs drop and their market share increase; their execution of a real estate marketing strategy, as it pertains to customer and business information, will not only be more accurate but more precise when sending out mail shots and emails, when discovering prosperous market segments, and when making investment decisions using a combination of big data and the most basic analysis.
Achieving a boost in healthcare productivity will require the combination of data from different sources that typically do not communicate with one another. Furthermore, categories of data such as patient records and clinical claims would need to be merged. According to an estimation by the McKinsey Global Institute, if the U.S. healthcare system used big data-enabled analytics to guide physicians on which treatments offer the best outcomes at the least cost, the annual productivity of the sector could grow by around 0.7%.
If the U.S. healthcare system used big data creatively and effectively to foster efficiency and quality, the sector could create more than $300 billion in value every year. Interestingly, 66% of that would come from an 8% reduction in U.S. healthcare expenditure. Moreover, there are four segments of U.S healthcare data — clinical; activity (claims) and cost; pharmaceutical and medical products R&D; and data about patient behavior and their emotional attachment — each of which is mainly captured and managed by a different source.
The insurance industry must optimize their customer service to provide real-time access to more diverse and instant insurance services. Big data paired with insurance analytics allows insurers to leverage their success and create a personalized enterprise to retain their policy-holders' business.
As the volume of banking clients increases, it becomes more necessary for banks to provide better and safer services that are quickly accessible from their system. This helps the banking industry learn what strategies gain more attention in the market based on what is most valued by their clients. Not only does big data assist in simplifying how a bank's system filters required consumer information, but it also contributes to the prompt handling of large and small scale banking problems before they are revealed to clients. Big data also helps the industry keep track of contracts, keep a clear record of credit card limits assigned per customer, and ensure no client is exceeding their limit unjustly or that a fraud is being committed. They can easily block or prevent debtors from making unauthorized charges that could damage their customer's account.
In the telecommunications industry, predictive analytics helps service providers understand their subscribers' demographic, how they use their services and what will ultimately influence them to stay with their service. Based on these factors, telecommunications companies can target the right customers with their marketing efforts by using behavior-based analytics for telecommunications.
There is still a significant amount of data existing in paper form, or digital data not made easily accessible and retrievable through networks. Forward-thinking industry leaders should begin aggressively building their big data capabilities for several reason - raw data is translated accurately and in detail about various consumer and business activities to make better management decisions; it narrows the gap between industries and the consumer so that they can receive more tailored products or services; it can minimize risks and reveal valuable insights that would otherwise remain hidden. Lastly, it can be used to develop the next generation of products and services and offer proactive maintenance to avoid new product failures.