What are the three distinct characteristics that distinguish big data from traditional data analysis? Researchers have described these as three distinct kinds of insights:
The first is the insight, or the perspective, that a business or a company takes advantage of big data to build up a better understanding of their environment and their customers’ behavior. For example, the beverage industry has identified certain compounds in beverages that increase their attractiveness to buyers, giving them insights as to what food to make and what flavors to offer. In this sense, big data analytics identifies an insight, rather than traditional data analysis, as the first kind of insight.
The second is the directness of the insight, or the way in which the insight is delivered. Traditional data analysis tends to be more piecemeal. It may begin with some kind of market definition or some kind of survey or questionnaire that yields interesting but usually passive information about who buys a particular product. Data scientists who work on this type of big data often depend on traditional data, but they also have a vague general idea of what should be gleaned from the information that they gather.
The third is the predictive power of the insight. Big data analytics has the potential to give researchers an insight into the future. That means that traditional data analysis tends to focus on the past or on the present. This kind of insight can also give researchers information about the behaviors of potential customers, which may help them predict what types of products or services they might purchase in the future. However, big data analytics has the potential to offer more in the way of predictions than traditional data analysis does.
There are many different ways that the quality of insight that one obtains depends on the kind of data that one has collected. For instance, if one is interested in understanding relationships, then it makes sense to collect as much information as possible about customer behavior and interactions. By combining traditional data analysis with social network data, one can get a much richer picture of the customers’ preferences and tastes. But if the traditional data only provides limited details about customers’ behaviors, then the big data analytics is less likely to provide enough information to make meaningful correlations between behavior and purchasing decisions.
Another characteristic that separates big data analytics from traditional data analysis is the availability of multiple sources of data. Traditional data analysis requires one source of data and then extrapolating from that source. In other words, if you collect certain kinds of information about customer behavior or buying habits from one source such as questionnaires, then you need to extrapolate from those answers to understand what kind of purchases people make when they are asked those questions. Big data allows researchers to collect data from a variety of different sources and then create and interpret the collected data in a way that gives accurate insights about customer buying trends.
One last characteristic that separates big data from traditional data is the level of precision with which the data can be processed and interpreted. Many traditional data analysis techniques rely on statistics and probability to approximate the value of any given piece of data. Big data analytics go beyond the simple use of probability and statistic to create highly accurate snapshots of customer purchasing trends. The end result is a single data point that is highly reliable and able to tell researchers everything they want to know about how a specific piece of customer data affects a company’s bottom line. In some cases, this single data point can tell researchers whether or not a particular marketing strategy is successful, for instance.
In a world where more businesses are turning to big data analytics to help them understand their customers and their competition, it is important for a business to understand what the three distinctive characteristics are to identify which kind of big data is right for their needs. While big data analytics has many benefits, it should not be mistaken for a magic tool that automatically makes businesses successful. It should be thought of as a tool that helps business owners understand the critical parameters of their business. This way, business owners will have greater insight into how their products and services affect their bottom line and how those elements may be optimized within the company in order to improve overall profitability. The three characteristics that distinguish big data from traditional data will help business owners focus on what they need to do to make their data collection and analysis techniques more effective, and help them turn their business’s success around quickly and efficiently.