Data mining and analytics are central to an organization’s performance. An organization’s competitive edge is determined by its ability to exploit data. Data refer to different numbers and values collected. After processing, it becomes factual information reliable for decision making. Data analytics and mining of data are co-related and used together to optimize the performance of businesses.
Data analysis is a deep analysis of raw data to look for patterns, trends, and metrics on a dataset. Specialized computer systems work on raw data to identify trends and make conclusions.
It involves many different types of data analysis. The patterns and trends found in this analysis improve a business’s efficiency and performance.
A successful data analytic process will tell you the position of your business and predict the future. You will know what you need to improve your products and services. At IronHack you can acquire a wide range of data analysis skills.
Types of Data Analytics
There are several types of data analysis.
Descriptive analytics usually describes what has happened in the past. Have your sales gone up? Are there more customers coming? Which products are fast-moving?
In diagnostic analytics, you apply a bit of hypothesis here. Diagnostic analytics tell you why something happened. You use the results from the descriptive analytics to find out why things happened that way. Did the last advertisement campaign bring any changes to your sales?
Predictive analytics tells you how things will be in the future. It will use previously collected data and denote if it will happen again.
Prescriptive analytics helps to make decisions on what should be done. Specialized computer systems do prescriptive analytics to find patterns from large volumes of data.
Data Analysis Steps
Data analysis begins with determining how to group the data. Data can be put in different categories, including age, gender, or income. The next step is collecting data through various sources.
You then use specialized programs to organize the data collected. Finally, you ensure the data is correct and accurate before being processed.
Mining data is finding useful information from large volumes of data. It is done systematically and successively to uncover hidden trends and patterns in an extensive dataset.
These hidden patterns and trends tell you about your customers and help you make decisions that bring more sales. It provides reliable information beneficial for marketing campaigns.
Also, mining data can help identify where you can cut costs to reduce your operational expenses. Additionally, mining data creates machine learning models used in artificial intelligence.
Types ofData Mining
There are different types of mining data such as:
Smoothing:To remove noise from the data, you use an algorithm. Smoothing helps to visualize trends.
Clustering:This is putting together groups with the same characters. Marketers can identify groups within their target market.
Classification:This comes after clustering, whereby you put items or persons into categories. Classification puts new data into the right group. More details on classifications are available on IronHack’s website.
Association:Which dentifies data that are somewhat connected.
Anomaly detection:This quickly detects fraud by finding data that doesn’t fit the normal pattern. Anomaly detection is beneficial in banks and businesses to help detect fraud.
Regression:A statistical tool that helps to predict the future.
Text mining: Text mining determines how often individuals use certain words. It can alert if there are data leaks by employees of a business.
Summarization: It helps to put a group of collected data into a form easily understood. You can use it to calculate the average from a particular data set.
Data Mining Steps
The first step is to eliminate conflicting information. Different data sources are then integrated and connected. The next step is to select data from the dataset. Thereafter, executing aggregation operations to transfer data into a form applicable for mining follows.
Next is the application of intelligent methods to extract data. The last step involves presenting the result to the customer through visualization.
7 Differences Between Data Analytics and Data Mining
Below are seven differences between data analysis and mining of data:
Workforce: A single person who should preferably be a specialist with coding skills is responsible for mining data. In contrast, a team of specialists does data analysis.
Function: In mining data, you want to find the hidden patterns in the data sets. However, in data analysis, you analyze the data sets.
Goal: Your goal in mining data is to make the information you have usable. Also, you want to identify patterns. For data analysis, your goal is to make data-driven decisions and hypotheses.
Method: You apply mathematical methods such as algorithms in the process of mining data. In data analysis, you use business intelligence to carry out your study.
Data sets: In the process of mining data, you use large volumes of data collected in the data warehouse. As for data analysis, you can use either small, medium, or large volumes of data.
Knowledge: You apply machine learning in the mining of data. However, in data analysis, you need to use subject and computer science knowledge.
Output:The output you get from mining data is data patterns and trends. As for the data analysis, your results are actionable insights and hypotheses.
Before you get valuable information from the data, it is important to recognize patterns and learn the trends. Therefore, you need to data mine before carrying out data analysis.
As a business, you need to apply both to optimize your business’s performance and cut your operational costs. And, most importantly, increase the sales of your company.
Ironhack has additional resourceful information that both individuals and businesses can use to polish these two vital skills. From part-time flexible learning to intensive courses you can tailor the bootcamp to suit you as you deep dive into the world of tech.