How Are Big Data Collected and Managed?
Big data is information that’s collected from a wide variety of sources, such as internet, company databases, apps, consumer surveys and the like. Once collected, it may be stored in a proprietary database and analyzed to give companies, governments and, more importantly, anyone who may need access to such data a wide range of insightful insights! However, the question is: how is this done? And, more specifically, how can big data help me make better decisions?
In order to answer the latter question, it’s crucial to understand how decision making works in general. Consider an “if” scenario. You and your team go to a conference, you get to participate in an interesting panel discussion or workshop. During this time, you get to hear the latest success story of someone who found a cure for a specific disease, but the panelists are split on how they discovered it-some applaud, some scoff, and others shake their heads in distaste. While this situation occurs often, if the disease has been on the radar screen of a company for some time, its scientists can uncover its hidden pathogen using big data analytics.
There are two ways to collect big data. One is to work with computers, via algorithms, to analyze and mine it for insights. This method has been used by large corporations like IBM and Google to create complex, personalized intelligence systems for everything from weather predictions to airline fares to hotel prices. While these sophisticated computing systems are impressive, they’re also expensive, largely because they rely on mathematical models that are too complex for the everyday person to understand. This doesn’t mean, however, that there aren’t ways to make big data analytics cheaper for smaller companies like yours. Today, we’re looking at computer science companies like Yahoo!
The second method used by these massive corporations is to outsource the analytical work to a group of expert analysts. These workers, often former programmers or IT professionals, work with engineers to map user experience data on hundreds of millions of consumer Web sites and compile it into highly effective dashboards. By combining complex technological knowledge with statistical analysis, these researchers have learned how to collect big data using everyday objects. The result is a sophisticated dashboard that makes it possible for the car maker to diagnose a wide range of problems by gathering real-time data insights about how the car behaves.
For example, car makers have long known that they can collect massive amounts of user data in order to improve safety and make the vehicle more reliable. They’ve also realized that they can gather this massive amount of data analytics to allow them to customize their repair operations and service. For example, they might decide to improve their customer service by offering towing services at certain mileage intervals or automatically scheduling an appointment if the customer doesn’t show up at the dealership. These and other customizations have spurred such growth in the automotive industry that the car manufacturers have become one of the largest users of information technology, employing more sophisticated technologies than even several years ago.
Perhaps, the most profound impact of how is big data collected is the impact it has had on how consumers think about and interact with the products they buy. A major part of how is big data analytics is the ability to collect insights from consumers themselves, their behaviors and their purchasing decisions. Consumer behaviors are changing at a rapid pace, driven largely by social media and the influence of marketers, and this influence is having a profound effect on how the car makers to design their products. Researchers have found that the way in which consumers perceive and interact with a product influences whether or not they purchase it.
One of the primary reasons why the car makers have been able to use big data to improve their car design is because of its ability to capture and measure so many complex factors simultaneously. The use of sensors for instance has enabled the auto manufacturers to understand how a certain car performs when it is on the road, in the rain, etc., and how it performs when the air conditioner is on, and when it is parked. Through combining these detailed user experience data with traditional customer decision making processes, the auto industry is better positioned to build, create, and sell cars that are satisfying to both the customer and the manufacturer.
How is big data collected can also affect how users interpret the data they are provided with. For instance, users may be more likely to accept a car designed for men because they are attracted to the larger front wheel area. However, women may be turned off by the small size of the center console. By using different types of sensors and collecting this data together, an auto manufacturer can understand how each demographic is trending and influence their future designs. This in turn will enable them to make changes in their designs that will not only appeal to current customers but to new ones as well.