Data analysis is the process of processing raw data and extracting valuable information that can help businesses make informed decisions. An average of 55% of collected data goes unused for analysis. This is due to the fact that the sheer amount of data nowadays being posted on a daily basis online can not easily be assessed by employees because no human can analyse and process thousands of volumes of data 24/7. Fortunately modern-day data analysis tools can process these large volumes of data using data mining, artificial intelligence and text analytics. Advanced automated AI data analysis tools can extract and analyse significant insights with very little manual input required. This can help companies improve their overall business performance and customer engagement. 

The terms data analytics and data analysis are often used interchangeably, however there is a difference between the two.

Data analytics refers to the whole process of data management: data collecting, organizing and analyses. It includes the techniques used to gain deep insight into data, as well as the tools used to show the processed results i.e. interactive dashboards with graphs and charts displayed. 

Data analysis focuses on processing raw data into useful information and statistics.

In terms of analysis, data can be divided into two categories.

For years businesses have concentrated more on structured data because aggregation, regression analysis, and predictive analysis make it easier to interpret. However with advancements in linguistics (Natural Language Processing) and data processing techniques businesses are able to gain deeper insight into their analysis of unstructured data. Unstructured data doesn’t necessarily have to be text; pictures, sound and video are also considered unstructured as they follow no generic format.

There are 7 common methods for data analysis

1. Descriptive Analysis
2. Statistical Analysis
3. Data Mining
4. Diagnostic Analysis
5. Predictive Analysis
6. Prescriptive Analysis
7. Text Analysis
   
   

1. Descriptive Analysis
   Descriptive analysis explains the reason behind the analysis of quantitative data. It is focused on describing or summarising a set of data. It draws insights from past data, while filtering out less important data, and focusing on the patterns present in a data set and not the outliers.

2. Statistical Analysis
   Statistical analysis analyzes and categorizes large amounts of quantitative data to find patterns and trends. Statistical analysis is an efficient way for companies to do market research. Regression analysis e.g.. is a statistical technique that measures the relationship between two (or more) variables. An example would be you want to see the number of visitors on your site and how much money you spent on marketing. This will let you see the specific impact of a change in spending on marketing.

3. Data Mining
   Data mining is the process of detecting patterns and relationships to predict outcomes. It includes techniques such as clustering (grouping related objects), sequential pattern mining (finding repeated object or event sequences), and detection of anomalies (spotting outliers, rare items, or unusual records). 
   
   An example of where data mining can be applied to would be clustering (when points of data are grouped according to logical relationships or consumer preferences) to define your target audience. Example: Annual income of 500 customers and their annual spend on your e-commerce business. 
   

4. Diagnostic Analysis
   Diagnostic analysis, often known as root cause analysis, attempts to address the question: “Why did something happen?” It’s an exploratory method of analysis that detects anomalies and uncovers the data with trends and stories. 
   
   Perhaps you have realized a sudden increase in customer complaints. Why is that? Has there been a problem with the customer service process, overloaded call centres, new untrained staff, slow replies from the chat support? Diagnostic analysis will help you uncover if the association between data points and potential factors is high or low. 
   

5. Predictive Analysis
   Predictive analysis uses historical data to gain insight about future events. Creating a predictive analysis model can be a big help for companies. An example of how companies can use predictive analysis would be to foresee consumer needs by using forecast customer turnover, and decide how many leads can be converted to sales. Predictive analytics is also becoming increasingly important for governments as it helps them address potential problems before they become major issues and predict the need for respirators, for example.
   ‍
   

6. Prescriptive Analysis 
   Prescriptive analysis integrates and transforms all the facts and observations you have into actionable insights. Prescriptive analysis works by analysing multiple scenarios and evaluating them to determine the most likely outcome. This is the most advanced method of data processing, which governments use to analyse the probability of worst-case scenarios such as a plane crash, where the cost of human error is high. The government will build an efficient response plan on the basis of the results from this analysis. 

7. Text Analysis
   Text analysis combines statistical and linguistic analysis methods to extract and categorize data to perform unstructured data analysis. Click here for a detailed article about text analysis.

Have a look at how Cauliflower with the help of advanced automated AI analysis helped Tchibo (Germany’s leading retail and consumer goods company) identify 80% of it’s flop products before they hit the market.