Data Analysis

Fellow steemians,in my recent post I spoke about data mining which explained the types and the usage of each data mining types. But in this post I would like to talk about something with is closely related to it that is data analysis. Fist of all I would like to explain how data analysis is different from data mining. Data Analysis is a way in which we inspect data in an effort to clean , transform and to make meaningful models about that data in an an effort to get information that will be useful for processing. But data mining is all about find pattern in large chunk of data or finding trends that may exist in a set of raw data.

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Reasons Why Data Analysisis Important

Data Analysis in large companies is a game chAnger for these companies as they are able to make good and informed decisions from it. With the help of this types of analysis companies and individuals can reduce cost as they will be able to predict or find patterns and take it to their advantage. Data analysis is able to help to target the right customer for particular product as it is a able to dind certain behaviour patterns and things of interest.

Five essential that you need to consider in data analysis

First and formost, you will need to identify the type of data that will be necessary for your data analysis. As not all types of data is suitable for the types of information that is need to be generated.

The second thing to do is to collect the data that is needed at the relevant sources say from questionnaire from internal a d external sources of data.

Third thing to do is to clean the dat by getting rid of all unwanted and duplicated data and then prepare that data to be analysed

The fourth thing to do is to actually to analyse the data using the various techniques that are available to you. This is to find the patterns ,trends and relationships that are available.

The last stage is to interprete the results that have been given. This is the stage where the researcher makes the decision based on the findings of the research.

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Types of data analysis

In data analysis, there are four different qays i which we can use to analyse data. These four has a unique way i which they are programmed to analyse the data that is given. These four types of data analysis are as follows.

The Descriptive Data Analysis

In data analysis, the descriptive analysis is what breaks ground and is the point of where data analysis start and it creates foundation for us to gain more insight into the data that is analysed. This type of data analysis seeks to find out what happened. This type of analyse does so by manipulating the data that is given by ordering and interpreting this data in a way the it will make sense to us. One of the beggest of of this discriptive analysis is to study the key performance indicators of the business and how it is performing to achieve their goals.

The Diagnostic Data Analysis

The Diagnostic Data Analysis seeks to answer the question why did it happen. This type of data analysis uses the result from the discriptive data analysis in an effort to find certain answers to why those results were achieved. This is what organisations and individuals use to track patterns and find any connectionthat might exist in that data. This type of data analysis creates more detail information about a set of data. So when we are in need of this kind of information we can easily identify and make use of them.

The Predictive Data Analysis

The third type of data analysis is the predictive data analysis. What it seeks to achieve is to find out what could likely happened but looking at the data that has been provided by the discriptive and Diagnostic Analysis. This analysis is one step higher in a senses that it has the ability to use the ability to use the summarised that to make predictions into events that are likely to happen. One of the most important aspects to remember is that this type of analyse is more of Forecasting and the more detailed the information the better the result.

The Prescriptive Data Analysis

This types of data analysis is the frontier in the analysis but most companies do not have the capacity to implement it or are not well equipped to process this type of data. Prescriptive data analysis combines the insights of knowledge from all the three previous sata analysis types to decisions or solve problems that they are facing. This data analysis style makes use of powerful technology and it requires the best data practices. Most organisations would need to me more commitments i order to achieve their desired goals. The artificial intelligence (AI) is an example of the Prescriptive data analysis they require look od dara to learn and improve in order to make decisions.

Conclusion
As i have clearly stated in the article above, all the four types of data analysis depend on each kther to a certain degree in order to function perfectly and provide the best of results. Even though they provided different types of information to the user and serve different function in data analysis, they provide the best insights for decision making and therefore improve productivity to help organisations and individuals.