What Are The Following Statements About Big Data Analytics?

which of the following statements about big data is correct

What Are The Following Statements About Big Data Analytics?

Which of the following statements about big data is true? “Big Data” is a term currently in use that has a broad range of meaning. In some cases “big data” is a term that describes specific types of information collection, analysis, or monitoring. In other instances “big data” is a generic description of the information collection and analysis techniques that can be applied to any set of data. In either case, it is important for users to understand how and why they are using this term.

The answer to which of the following statements about big data is true? “It is true that big data has made it possible for companies to effectively monitor their performance and improve decision making.” While it is true that big data has made it possible for companies to monitor their performance and improve decision making, this does not mean that the decision making process is necessarily tied to the measurement of those results. For example, Netflix, one of the early adopters of big data analytics, uses complex algorithms to optimize customer search results. This approach has the potential to reduce errors by a large percent or more, but Netflix does not rely on this as part of its decision making process.

“It is true that big data analytics can be used to improve customer service.” While the potential for improving customer service is real, there is no inherent motivation within the company to use this approach. Instead, Netflix’s approach has been to build tools to enable easy aggregation of data from various sources, including e-mail, social media, and other channels. The approach takes advantage of advances in machine learning to allow agents to recognize and classify particular customers based on their past interactions with the company.

“It is true that big data analytics has produced positive results for certain businesses.” Again, while many companies have made great strides in improving customer service through the use of big data tools, there are some that have not. The metrics approach at work here is somewhat different than traditional metrics, namely the use of overall quality scores to indicate success and failure. This can be frustrating for businesses that want to measure quality of service relative to cost. In order to determine whether or not a metric is actually useful, a business will need to look at the metrics and determine whether or not they are telling the truth. It may be wise, then, for a data scientist to consult a business representative before making any statements about which of the following statements about big data is true?

“It is true that an analysis of customer data can help improve profitability.” Data scientists have long worked to help businesses make better use of information generated by their analysis. While improving profitability is certainly possible through the use of data analysis, the improvement of profitability requires a lot more than just the right tool.

“It is possible to predict how certain factors in the data will affect a company.” While this may seem like a very difficult thing to do, it is not impossible, and companies that use predictive analysis tools can take advantage of them to help them get a handle on where their data really stands. Predictive analysis is only one part of the picture, however, and using both predictive and actuarial models along with the right tools can bring even the most difficult predictive model into some form of agreement with raw data.

“It is possible to apply techniques from economics or business to big data analytics.” There are many fields that are similar to business or accounting and can be studied using the same analytic techniques. However, it is usually not that simple to turn these techniques into something meaningful for big data. Data scientists should always reserve the right to ignore certain methods completely, especially when they do not fit with their specific project. This is especially true when the analysis of the data involves a large amount of time.

“It is not currently possible to store all the data that would need to be analyzed in a single database.” Although storing terabytes and petabytes of information is not currently possible, it certainly is not out of the question in the future. Today’s computers have much better memory capacity than ever before and storing even a small amount of data on a traditional hard drive is relatively inexpensive. In the future, it is also likely that storing more information will become less expensive, but it is impossible to say exactly when that will happen.