Data Science

Data Analysis Techniques

Posted in Data Science
Data Analysis Techniques

Introduction

In this competitive world, where every business person wants to grow his or her business, for that, there is a need to make certain business-related decisions that are based on the past and present facts of the business activities. Every employee has to think about what happened in the past and what will be the impact of the decision in the future. By doing so, they are analyzing the past and present information that will prove useful while making business decisions.

This process of cleaning, changing, and modelling data to get authenticated and make it useful information for business decision making is called Data Analysis. This step is very necessary for every business field because they have to analyze the mistakes committed in the past because of which they have to suffer loss in the future so that they can correct all such mistakes in future dealings. Analyzing the data helps the business firms to work effectively and efficiently without any hindrance.

Data analysis is required in both cases either firm is earning profits or incurring loss because in profits they have to look forward in doing the business to grow more and in case of loss, they have to think about past failures and mistakes and make plans and strategies against it so that they take business decisions without repeating the same mistakes.

Issues with Data Analysis

Data analysis is not so easy to perform because you have to grow through a lot of information and data to look for loopholes. The number of data analysis tools and techniques are used to make his processing easier, for example:

Tools: Java, MATLAB, SQL

Data analysis techniques include text analysis, statistical analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

Data Analysis Tools

Data analysis tools make it easier for various business users and firms to extract information from a huge database. Moreover, it also helps the users to identify patterns, relations, etc. which may be a tough job for an employee to do. These tools are used to explore information from various sources. After that, the relevant information is combined, and such information is used by firms to make better business decisions and enhance the working of the firm.

1. WebFOCUS

This is one of the most reliable and flexible business intelligence analytic platforms that help its users to get important information from various sources like ongoing trends in the market, information regarding their creditors, customers, suppliers, etc. This relevant information is used by big firms to make an appropriate business decision.

2. Excel

Microsoft Excel system is one of the largest platforms for data analysis, among other tools. It is most suitable for small data and information, but with the help of using a plug-in, it can handle millions of data.

3. BI Tools

Here, BI stands for Business Intelligence. The main objective of using this tool is to shorten the time while making business decisions. This tool is specially designed as a data analysis tool.

4. R and Python

It is considered as a very powerful and flexible. In this, you can use code to do the work that you want.

Different Types of Data Analysis Techniques and Methods

There are various types of data analysis techniques which are used in cleaning and transforming the information so that business can make a reliable decision for the future. All such techniques are based on business and technology. Different techniques are given as follows:

1. Text Analysis

Another name of text analysis is data mining. This name is given because to get reliable information from a large database. We use data mining tools to extract information. The main purpose of this technique is to convert the raw data into reliable business information so that resourceful information can be used to make a decision. It also helps the business to extract and classify relevant information from text. Here text includes product reviews, product pros, and cons, the demand of the customer, tweets regarding companies. But we all know that it is quite difficult for a human being to collect relevant information from a large number of tweets. Moreover, it is a quite time consuming and expensive process. It is very obvious that every business firm wants key information from a large database, and it is only possible because of text analysis or data mining. There are many methods to perform text analysis. There are some simpler methods and some advanced methods. The explanation of simpler methods are given as follows:

1. Word Frequency

As from the name of the method, it is very clear that all those words or phrases that are very frequently used in the tweets or large database are highlighted and given much more important than the other words. So the company only focuses on those words and tries to use that important information to make a decision.

2. Collocation

The main purpose of this technique to identify the words that commonly co-occur. This is very useful to identify the hidden semantic structure and helps to improve the insight vision of the business firms to make the decision.

3. Concordance

This technique is used to know about the sets of words that are very commonly used and turns into eye-catchy words because of which the whole attention of the company is drawn towards those words.

These are some of the simpler techniques, but there some advanced techniques also which are very efficient in a performance like text classification, sentiment analysis, etc.

2. Statistical Analysis

Statistical analysis is performed by using past data in the form of dashboards. This type of analysis includes the following steps. First, it includes a collection of a huge database. Analysis and interpretation of the huge database take place, and in the last reliable and resourceful information is presented in front of the company. This type of analysis is categorized into two types of analysis that are

1. Descriptive analysis 

In the descriptive analysis, a large database is summarised in a numerical database. It helps the company to calculate the mean, median, and frequency of the mode, which proves to be important in making business decisions.

2. Inferential analysis

In an inferential analysis, we can find different conclusions from the same data by selecting different samples.

This statistical analysis usually uses drawn structures like pie charts, bar graphs, line graphs, etc. And the information drawn from all such graphs is quite useful and provides overall data information to its users and helps them to make a decision that is free from any errors.

3. Diagnostic Analysis

Diagnostic analysis always tells the reasons and causes of an event, that is it usually deals with (why did it happen?) somehow in the ongoing business if a problem arises in the business or if a taken decision proves to be a failure then we can look for the cause of such failure, that why such failure happen? And if the same problem is faced by the company in the past time, then the company can take the same prescription for the new problems. Data mining techniques such as clustering analysis, and anomaly detection, etc., and there are many more methods to perform analysis. Although it is very useful in decision making, there are certain challenges also because sometimes a business person may misinterpret the causes of the problem. By not understanding the proper cause, they might take go for the wrong prescription because of which the company may suffer loss.

4. Predictive Analysis

As from the word predictive, it is very clear that when we talk about what will be the likely impact of the decision taken in future dealings or what will happen in the future? And previous knowledge plays a key role in predicting what will likely happen in the future? the easiest example to make it understand is that suppose if you purchased two pairs of shoes based on your past savings and suppose you get a promotion letter next year, then you can think of purchasing more than two pairs of shoes. Still, your decision not only depends upon the income, but it depends upon a lot of other things that are the price of the shoes, or you can decide to invest those extra savings in some other assets. The predictive analytic process that includes the following steps

At first, we look at the large database from which we need to extract key information then we prepare data from various sources. This collected data is analyzed by cleaning, transforming with the motive of gathering resourceful information. Based on this information, future models about the future and after that, developing best action achieves the desired results and making supervision on the designed plans.

5. Prescriptive Analysis

This analysis deals with the solution of the difficult problems, after having proper insight into the previous problems and how those problems are solved or which actions were taken to solve those issues, by utilizing those facts and figures we can easily find the solution of the present problems. This analysis is mostly used by business companies because the predictive and descriptive analysis is not enough to improve data performance. So this analysis mainly deals with finding the best course of action to ensure implementation of the plans. And this best is derived from various alternatives after evaluating various alternative courses of action. Evaluation involves looking for the pros and cons of each alternative action. It also reduces the risk factor in the business because the business decision is taken after proper evaluation of alternative actions, so there is a reduction in future errors.

Advantages of Data Analysis Techniques

Every business deals with a lot of business-related information, out of which some are useless, and some are resourceful. But it is quite difficult for humans to differentiate between them. This process has turned out to be a simpler one because of data analysis techniques. Some of the merits of these techniques are given as follows:

  1. It helps in the data cleansing, which improves the quality of data and brings benefits for both customers and companies.
  2. It also helps to remove duplicate information from various databases because of which a lot of memory is saved, and this will ultimately remove the cost of the various business firms.
  3. It also helps in declining the fraudulent activities by providing authenticated information to the companies about their clients. It also tells about the creditworthiness of the various creditors and helps the company to make proper investments
  4. It helped the companies to make more informed business decisions and backed up by facts; moreover, the decision-making process becomes much faster.
  5. It gives a deeper and better understanding of the demands and requirements of the customers, which, in turn, builds better business relationships.
  6. It gives better insights into the financial performance of the company. It tells about the proper information on the profit and loss account statement.
  7. It provides the benefit of economies of scale because it reduces the cost of production, thus increases the profit of the firm.
  8. It also gives proper information regarding business risks, enabling the company to take preventive measures on time without suffering any loss.
  9. The Decision-making process becomes more accurate, which means decisions free from Errors.

Disadvantages of Data Analysis Techniques

  1. As there are a lot of data analysis techniques that are used in different circumstances, so it becomes difficult for the company to select appropriate analytical tools. If the firm chose the wrong technique, they might have to suffer loss.
  2. Data analysis techniques give resource full information to the company. Still, sometimes this vital information of the company can be misused by the competitors who will harm the working and the structure of the company.
  3. There may be a chance of a leak in the customer's information like their bank details, their day to day transactions. Which can be used by the company for their mutual benefits, harming the goodwill of the company.
  4. Moreover, it can only be operated by that person who has complete information regarding all techniques. As there are a lot of data analysis techniques, so it becomes difficult for a person to become specialized in all the techniques. It is not a cup of tea for a layman.

Data Analysis Process

Data analysis process means when we get information or large database from various sources with the help of data analysis techniques, or the data analytic tools are called a data analysis process. From the big information, we get key or relevant information that is required by the businessman in making business decisions, and this step takes place with the help of cleansing and transforming the huge database.

This data analysis process includes different phases, explained as follows:

1. Data Requirement Gathering

In the very first step, the company always sets a target regarding which they have to collect the information because they have a lot of issues in the business life, they cannot get information on such issues at the same time. So they usually decide the key areas for which they need information at the first spot. Then they start collecting the information from various reliable sources. So, at last, they get a huge database from which they can get key information with the help of data analysis techniques and data analytics tools.

2. Data Collection

As discussed in the above step, after selecting there a key area of operation regarding which they need information, they start a collection of the data for that subject. Every company has its trustworthy agents that collect information about that subject only from reliable sources because they believe that such information reflects the true image of the market. So they never take information from unreliable sources.

3. Data Cleansing

After getting information from various sources, the company has a huge database. However, still, there is certain information that is not so important, so there is a need for cleansing of that huge database so that company could get appropriate and key information, which will help in business decision making. This cleaning of the data usually takes place by performing various data analysis techniques. And after getting the cleaned data, that is, resourceful data, the company, starts the next step that is analyzing that key information

4. Data Analysis

Sometimes after getting the cleaned data, the company may find that the information is not sufficient for the plan to implement, so they go for more information. Sometimes it may also happen that the director of the company may not find the cleaning information a reliable one. All such things only take place after analyzing the cleaned data. During this phase, you can use data analyzing techniques or data analytic tools and software, which will help you to understand, interpret, and derive conclusions based on the requirements. After this step, there is an interpretation of the key information.

5. Data Interpretation

Once we get reliable and sufficient information regarding the target set, the company has to communicate that information to their trustworthy employees because they are the one who has to draw alternative actions to achieve that desired result. They use that information in making out the action or the steps that will be used in the implementation of the goals. After making various actions, they have to choose the best alternative that has the least negative aspects and consequences. Only that alternative will prove to be profitable for the company.

6. Data Visualization

This is the last phase in which the company draws graphs regarding unpredictable events. So that appropriate action could be taken on time. In this phase, the company also thinks about the results of the plans; that is, what will be the likely outcome ?. So this phase completely deals with future events.

All these phases are performed with the help of data analysis techniques and tools. These techniques make the process less time consuming and effective. This results in the improved performance of the business units. So these techniques play a very important role in the betterment of the business.

Conclusion

Data analysis techniques are mainly used by companies to make their work easier and effective. Data analysis means a process of cleaning and transforming the data to collect reliable information. It has a positive impact on the profit of the working units because it provides a lot of benefits to these working sectors like economies of scale, time reduction, faster decision making. Due to all these merits, the company is gaining better goodwill. Their techniques are very easy to perform and implement.

There are different types of techniques that can be used for data analysis that are text mining, prescriptive, descriptive, and many more, and data analysis consist of various phases like data requirement gathering, data collection, data cleansing, etc. It is very necessary that before applying any technique, the person should know completely about his work so that the appropriate technique can be applied to get better results. Moreover, different types of data analysis tools are used to perform these techniques that are excelling, java, MATLAB, and more.

I hope you have understood the techniques behind analyzing data to generate business insights. Did you find this article descriptive and innovative? Let us know in the comments.

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Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

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