The big data and analytics industry is expected to expand at a compound annual growth rate of 26.4 percent to a value of $41.5 billion by 2018, according to the International Data Corporation. This growth is partially due to big data's various applications, including fraud detection, competitive analysis, consumer sentiment analysis, traffic management, smart power grid management and call center optimization.

Once your organization starts to collect big data, you should get started on analytics. Unfortunately, many businesses don't know exactly what this means. Where do you begin? Which types of analytics would benefit your business the most? The following four types of big data analytics need to be considered when debating how to best leverage the information within your business:

1. Predictive

Predictive analysis identifies past data patterns and provides a list of likely outcomes for a given situation. By studying recent and historical data, predictive analysis presents you with a forecast of what may happen in the future. While it does not predict what will happen, it does provide probabilities into what could happen. This is one of the more commonly used analytical methods, since it can be applied to sales processes such as analyzing lead sources, types of communication, social media and consumer relationship management data. In the simplest of cases, you may simply use your big data to predict other data you do not have.

One common type of predictive analysis is sentiment analysis, in which the model predicts the sentiment score based on data it has. The outcome is actionable, valuable data that can be used for business decisions. Predictive analysis can also be very useful in optimizing customer relationship management. By leveraging data on customer behavior and spending trends, it is possible to more efficiently cross-sell or upsell products.

2. Prescriptive

Prescriptive analysis reveals actions that should be taken and provides recommendations for next steps, letting you answer your business questions in a focused manner. It goes beyond predictive data analytics, since it recommends multiple courses of action with likely outcomes for each decision. So, a prescriptive model is also, by definition, a predictive one.

According to Michael Wu, Ph.D., chief scientist of San Francisco-based Lithium Technologies, "a prescriptive model can be viewed as a combination of multiple predictive models running in parallel, one for each possible input action."

While studies have shown that only a small percentage of organizations use this method of analysis, prescriptive data can provide impressive results when used correctly. For example, Datafloq reports that the Aurora Health Care system used prescriptive analysis to reduce readmission rates by 10 percent, allowing it to save $6 million annually. Similarly, prescriptive analysis can be used to improve drug development, reduce time to market for new medicines and find the right patients for clinical trials.

3. Diagnostic

Diagnostic analysis looks at past performance to understand what happened and why. Businesses use this type of analysis to complete root-cause analyses and uncover patterns in their business processes. Ultimately, it can help identify factors that directly or indirectly affect their bottom line. Business growth can often be driven by the smarter decisions made as a result of diagnostic analysis.

The most common application is in social media, where you can use this type of analysis to assess the number of posts, shares, mentions and fan interactions to figure out what worked in past campaigns and what didn't.

4. Descriptive

Finally, descriptive analysis examines what is happening in real-time based on incoming data. Descriptive analysis is often referred to as the simplest type, since it allows you to convert big data into useful bite-sized nuggets. However, the results need to be monitored in real-time through email reports or a dashboard. This method is also known as data mining and is used by a majority of organizations. One common use of descriptive analytics is examining historical electricity usage to plan power needs and set prices.

Harnessing big data and analytics can deliver immense value to your business by providing context for collected information and a big-picture view of the organization. The bottom line is that by turning complex data sets into actionable intelligence through one or more of these four analysis methods, you can make better business decisions.

Nida Rasheed is a freelance writer with a serious caffeine addiction who has previously written for GMO Cloud, Kevin Muldoon, ShoutMeLoud and PakWired. She loves her dogs, wants to travel the world, and dislikes referring to herself in the third person. Follow her on Twitter: @nidarasheed