- July 3, 2019
- Posted by: admin
- Category: Predictive Analytics
Data Mining Vs Predictive Analytics:
What is the sole aim of every organization, big or small? To satisfy their customers, outperform their competitors and achieve astounding success. For all this, customer understanding is of prime importance and technological advancements has made it quite interesting. It is imperative to leverage complex technologies such as data mining and predictive analytics in the best possible way.
But what if these two path-breaking technologies are pitted against each other? What do we need to know in order to make sure we leverage the best out of them? We need to understand them in detail and then comprehend how Data mining vs Predictive Analytics unfolds.
Here’s a tabular format for a layman understanding of Data mining vs Predictive Analytics.
| Parameter | Data Mining | Predictive Analytics |
| Definition | Data mining involves processes that analyze and identify patterns in large piles of data contained in the company data warehouse. With the aid of statistical methods and various algorithms, usual data patterns plus abnormalities – everything can be easily spotted by data mining. Organizations also employ diverse predictive data mining techniques to extract insightful details that empower their business. |
Predictive analytics is all about analyzing data to forecast future events. This is based on historical trends and patterns obtained from user data. When data mining and predictive analytics combines, the overall procedure results in a more optimized outcome. |
| Purpose | After collecting data from various sources of an enterprise, data mining will help in gaining a better understanding of the acquired set of data. Companies can better understand market dynamics and customer demands across different segments. This eventually leads to enhanced customer satisfaction, resulting in better sales. | Predictive analysis, along with strong domain knowledge plus data mining, reveals deeper insights about purchasing trends of customers, and projects likely shifts in demands. When companies are aware of likely changes in market demands, they can formulate business strategies accordingly. |
| Who does it | Strong background of mathematics is required for effective data mining. So machine learning engineers and statistics professionals are best suited for the roles of data mining. | Predictive analytics requires clear concepts in business understanding and sound domain knowledge. Business analysts and domain experts are best suited for such roles. |
| Future Scope | With data mining, data scientists would be able to apply Artificial Intelligence and machine learning techniques like classification, regression on data to automatically unearth better business opportunities for enterprises. (Source) | With predictive analytics empowering smart discovering of data and market predictions, it is expected to become the most favoured activity in the entire domain of business analytics. The market value for predictive analytics is expected to reach $10.95 by the year 2022, which is a jump of around 21% from 2016 to 2022. (Source) |
Process based differences between Data mining vs Predictive Analytics
Data Mining
The crucial process of Data mining can be categorized in to siz important phases namely, the business research phase, data understanding phase, followed by data preparation phase, data modelling, evaluation, and deployment phase. Each of these six stages in the data mining process is critical for its overall success.
Predictive Analytics
Unlike Data mining, predictive analytics can be segregated in a three-step process- definition of the business goal, collecting additional data with the help of third-party tools, and drafting a predictive model that may involve of complex mathematical model or may be as simple as identifying the customers based on their geographic location or age group.
Data Mining vs Predictive Analytics OR Data Mining + Predictive Analytics
Now, after comparing both the concepts, the main question is how data mining vs predictive analysis can yield better outcomes for business concerns.
The truth is that it is not separately but together, that the real power of both predictive analytics and data mining will be unleashed in the future. The rise of smart and actionable data is what researchers and data science experts are looking forward to when predictive analytics combines with data mining.