Get To Learn The Basics Of Data Analytics

Data analytics is a critical field for many companies globally. These companies collect a lot of data throughout their operation. However, in its raw form, such data is often meaningless. Data analytics comes in handy to arrange and analyze these data with the aim of drawing meaningful and actionable insights from them. The findings drawn from the collected data are useful for the company in decision-making. The findings are often presented in the form of recommendations or suggestions on what the company ought to do as the next step. Ikigai Labs is one of the renowned data analytics platforms on which many clients rely to sort their data. This blog will dig deep to explain the critical aspects of data analytics.

Types of Data Analysis

There are four types of data analysis. These are discussed below.

Descriptive Analytics

This surface-level data analysis focuses on what happened in the past. Descriptive analytics employs two main techniques when analyzing data. These are data mining and data aggregation. This implies that a data analyst first gathers data and sorts it in a summarized manner (aggregation) and then sorts it to discover its pattern (mining). This data will be presented in a format that is easily understandable by the majority of the audience

Diagnostic Analytics

Diagnostic analysis of data aims to describe why the data exists the way it is. A data analyst will start by examining the data for anomalies. Anomalies are portions of the data that the data pattern cannot explain. The analyst will focus on finding any additional data that will attempt to explain the anomalies during a stage called the discovery phase. To sum up the diagnostic analytics, the analyst will try to draw relationships by finding data that correlate. Regression analysis, probability theory, time-series data analytics, and filtering are some of the main principles that may be employed at this stage.

Predictive Analytics

Predictive analytics in the Ikigai Labs attempts to predict what will happen in the future using the data. Data analysts come up with data-driven suggestions and actionable recommendations that may be useful to a company. Predictive analytics is used to estimate the probability of a future occurrence using probability theory as well as historical data. This might not be entirely accurate, but it eliminates any guesswork and has a great margin of accuracy. Therefore, this is essential in increasing a company’s odds of making the most appropriate decision.

Prescriptive Analytics

Prescriptive analytics comes in handy to build on predictive analytics. Prescriptive analytics advises a company on the decisions and actions to be taken. They analyze and come up with reasons that support decisions arrived at earlier. In addition, data analysts often consider an array of possible scenarios and look into the different actions a business might take.


Ikigai Labs data analytics is critical to many businesses. It offers a convenient platform where businesses can have their raw data sorted and analyzed and progressive actions drawn from them. In addition, the data will go through the different phases of analytics to ensure the final decisions arrived at are result-oriented.