With so much focus on big data and big analytics, it may be easy to think that there is only one answer to which of the following choices is a good way that big data science can be applied. This, however, is not the case. In fact, one of the best things about big data and its impact on business is that no single solution is right or wrong. Rather, each business can use data in a way that is most appropriate for that business. Therefore, it is helpful to think in terms of “which of the following choices is a good way that big data science can be applied.”
One example is the use of big data analytics for determining which of the various products offered by a manufacturer will ultimately be best sellers. In doing so, it is important to remember that no single solution will be right for every business, or for every situation. Rather, the key is to choose among the many different options and evaluate them based on their expected performance. If one uses traditional methods to evaluate such choices, such as surveys or focus groups, then there is a good chance that one’s answer will likely be biased due to one’s preexisting feelings about the product in question. However, if one uses data science techniques, such as pattern recognition or machine learning, then one is more likely to be able to come up with a more valid result.
Another example is when a person is interested in which of the major sporting events will be held near where they live. This type of evaluation often requires taking stock of past and present data, as well as trends in real time. By using data science techniques, one can evaluate this data in a manner that is more accurate and unbiased than even traditional methods. In short, such methodologies enable one’s business to make the most of big data.
In a similar fashion, the question of what of the following choices is a way that big data science can be used to increase customer satisfaction ranks is easily answered. Such evaluation questions involve evaluating whether a company’s service level levels meet the customers’ expectation. In doing so, one must consider factors such as timeliness in response, and the company’s ability to work with customers in whatever manner they might need. For example, if a restaurant is slow to answer a table reservation, or is otherwise unreliable, then such elements will certainly affect an individual’s opinion of the business. Therefore, it is important for management to first address issues such as customer satisfaction before moving onto the option of which of the following choices is a way that big data science can be used to improve the customer experience. Then, if and when it is appropriate, it can be paired with other disciplines to increase one’s strategic competitive advantage.
Data mining is perhaps the most famous example of a decision made based on big data. It involves the extraction of unstructured and structured information from databases, including unstructured data such as web records and real-time web feeds and analyzing it in order to extract insights which may be relevant to the company in question. In a way, big data analytics provides managers with more in-depth and granular perspectives than traditional decision making methods. While this form of big data science may seem theoretically limitless, it is quite often not.
The above example illustrates how certain business factors are crucial for an individual business in particular. In fact, given the complexity of many problems, managers will usually make decisions based on their own judgment and experience. However, given the speed, volume, and quality of such information today, intuition or “gut feel” is not always enough to make sound business decisions. In addition, even if a decision is made based on intuition, there is no guarantee that the decision will be right at all times. But what of the future, when the volume and quality of available data increases even further?
At this point in time, there are two different possibilities for using big data to improve a business: one is to continue to utilize traditional decision making techniques, and the second is to embrace a holistic approach, using big data science to enhance both traditional and new decision making techniques. Which choice is better, though? Both options have different pros and cons, so it will largely depend on what type of business a manager has. However, it’s safe to say that most companies would prefer the latter option, especially now that big data science are beginning to play major roles in the overall success or failure of a company.
So which of the following choices is a way that big data science can be applied to improve a business? The answer, of course, depends on the manager’s goals. However, one thing is clear: whatever decision a manager makes, there will always be at least some consequences. This is especially true if the manager makes a decision based on intuition or personal preference.