Predictive big data analytics is probably the most desired type of analytics when businesses implement big data. According to a recent study, the market worth of predictive analytics is expected to reach $12.41 billion by 2022.
Nonetheless, it is impossible to get to the two more profitable types of big data analytics – predictive analytics and prescriptive analytics – without going through the other two – descriptive and diagnostic analytics.
Despite the differences in their benefits and characteristics, all of them can help enterprises to improve their operations. Let’s see how well you know these four types of big data analytics.
1. Descriptive analytics
Descriptive analytics digs into the historical and real-time big data to find out repeating patterns and data correlations. Descriptive analytics, as its name suggests, provides businesses with a comprehensive overview of what has been happening. However, descriptive analytics can’t leverage big data to tell you what you should do or what is likely to happen.
Nevertheless, the descriptive analytics of big data is not useless. Businesses can still utilize descriptive analytics tools to monitor changes and develop trend reports, classify prospects to maximize conversions and personalized interactions with audiences, or to distinguish prospects for audience segmentation.
2. Diagnostic analytics
If you have some unidentified problems in operations, diagnostic analytics solutions can mine big data to identify their root cause. In particular, diagnostic analytics of big data is able to generate unbiased, data-driven explanations of causation as well as the parameters that need to be changed to improve the situation. In addition, diagnostic analytics can help people to avoid false interpretations of big data caused by wrong information or personal bias.
3. Predictive analytics
Predictive analytics is preferred by businesses because it has the ability to suggest what can happen next by combining accumulated and analyzed data points. Predictive analytics can go through big data and provide users with the probability of specific outcomes. Thanks to predictive analytics, companies can reach a higher accuracy in prediction; hence, achieve better decision-making.
When employed predictive analytics solutions in big data strategies, enterprises can predict lead scoring by qualifying the new prospect and suggest the most profitable actions to take. Businesses can also use predictive analytics to identify models and automate segmentation to provide better-personalized contents. Overall, predictive big data analytics is a powerful solution to optimize data-driven conversion.
4. Prescriptive analytics
Prescriptive analytics may be the least mainstream type of big data analytics among the four. It suggests users with the set of actions they should take to achieve a specific outcome. Prescriptive analytics tools analyze the big data of past/current actions and their respective outcomes to generate various future outcomes, then match them with your goal to provide you with the actions you should take to realize your goal.
Prescriptive analytics is powerful, but it hasn’t come of age yet. There need to be more research, development, and employment of prescriptive big data analytics solutions for it to get to the mainstream. Once the technology is mature, the complex decision-making can be a little simpler.