Data Analysis (Definition)

The business world is getting increasingly aggressive and ambitious with every passing day. It is tough these days to make a successful market entry, and even much tougher for business owners who are already in the market to establish themselves at the top of a very competitive market and set themselves up as the go-to brand for their niche products. Put that aside, obstacles such as an unstable market and an uncertain economic situation – combined with other political factors – also suffice to further test and add a shivering tremor to the life of a business. 

Data Analysis

But all these, despite their harshness on the business economy, stands as a steep learning curve that has pushed business stakeholders to learn to invest more time and energy in data analysis and research to have, in their hands, the solutions to whatever past, current or future problem trends that might be stalling the swift sailing of their business goals and objectives. Entrepreneurs and business owners have come to understand that data analysis can help them reach the desired height because it affords their organizations with the ability to collect vital, usually actionable information about the market, their competition, customer’s demographics, among other things as they work with these variables in other to make a more informed business decision. 

Data analysis is so vast and its importance in business or any given human endeavour is immeasurable, however, before we search deep into all the vital parts of data analysis, we must first make effort to understand the simple meaning of it. 

What is Data Analysis?

Data analysis is simply the process of collecting raw information from different relevant stop points and harnessing them for the final purpose of understanding the best way to use this raw information already gotten. For a shorter understanding, data analysis can be pictured as any effort made to favourably predict future outcomes by reading of present and past information available. The major idea behind data analysis is the drive of curiosity, that is, the need to want to know why something works in a certain way or why it does not. 

How Important is Data Analysis?

Data analysis is an important subject and not only for the governments, corporations, and large businesses, but also for every person to focus on, learn, and inculcate in their daily living. Why anyone or entity should care a great deal about data analysis is for the reason that the very nucleus of data analysis is research. Now, research and its relevance to our daily living are justified already. For instance, if an individual wants to travel interstates, the right step to take making the trip is to inquire about what transport means to follow, whether plane or bus; how much it will cost you, the time it will take to get their et cetera.

This is simple research a person might undertake and it goes the same way with data analysis and bigger organizations and establishments. 

What Types of Data Analysis Exist?

In the field of data analysis, different types exist and this is largely dependent on factors such as the nature of the technology in use, the type of business available for analysis, including other ancillary markers. However, some types of data analysis have been tried and tested and declared standard by experts in the field, and these include – diagnostic, statistical, predictive, text, and prescriptive types of data analysis.

1. Diagnostic Data Analysis

Just as the name implies, diagnostic data analysis is an analytical process deployed to solve the root cause of a given problem. The major aim of diagnostic data analysis is to try and proffer a solution as to why something happened the way that it did, or why it did not happen the way that it should.

This type of data analysis is, more often than not, synchronizable with the statistical type in other to arrive at a solution. One key attribute of this is that it can be used to spot behavioural patterns among a particular targeted individual, persons, or group. Business agencies and governments effectively utilize this paradigm to search out connections to problems and trade them from the top end to the tail end before curbing out the appropriate solution for it.

2. Statistical Data Analysis

Although this sometimes combines with other types such as diagnostic, it is also able to stand on its own. It is defined as the act of collecting, gathering, and analytically examining data to solve any particular problem. Statistical analysis typically has two ways used to engage in problem-solving inquiry and they include inferential and descriptive statistics.

While inferential statistics involves the use of randomized variables to arrive at a conclusion based on behavioural observation stretching through specific periods in time; descriptive statistics entails the use of the summary concept like frequency, median, means, and mode in reading the available data on the ground. 

3. Predictive Data Analysis

The predictive data analysis offers researchers, whether of businesses or individuals, a new edge to offset what is to come by offering researchers a possibility to correctly predict or guess what is going to happen at a future date. A relatable instance here can be given when for example you as an individual decide to stock up on groceries and foodstuff because you have just realized that the rate at which things are increasing shows that you may not be able to afford them in a few months.

Any individual or business that does something like this is indirectly tapping into the offerings of predictive analysis, and this is because they gather tangible data from what they are currently experiencing before making that choice for the future.  

4. Text Data Analysis

This kind of data analysis primarily works with raw, computerized data to suggest a solution to the emergent issue. Also referred to as data mining, text data analysis often thrives best when there is a pool of data available given that it incorporates the use of databases and other mining tools to search out problems and correlations. This type of data analysis is especially very helpful for digital setups and other location-based businesses because they help transform these raw text variables into proper and actionable business ideas. 

5. Prescriptive Data Analysis

This data analysis prototype comes in as an emergence of the entire ideas of all other types of data analysis available to researchers. Analysts may sometimes hit a dead end when seeking the solution with one single type of data analysis tool, and this is where prescriptive data analysis comes into the scene, bringing about a combined utilization of various other analytical techniques to examine the available data and reach a more feasible decision 

The Process of Data Analysis

For any research endeavor to qualify as being a part of data analysis, there are usually some natural ingredients that it must possess. These ingredients are vast and complicated but can generally be characterized under a few stages:

1. Data Ideation Stage 

This is usually the first stage the researcher or analyst undertakes in the process of getting a solution for a particular inquiry. The data ideation stage is arguably the most important of all problem-solving efforts because this is the part where issues are pinpointed and earmarked for resolution. It is a vital part of the whole data analysis process and it gives the researcher that inner intuitive spunk that is needed to successfully identify potential problems, why they are considered problems, what ways can they be improved on, and what type of data analysis is most suitable in addressing them. A business researcher or owner under this stage can try to organize a brainstorming session to try and identify why the brand sale is experienced a recurring dip over a given period.  

2. Data Collection Stage

The process of data collection is the next stage that the researcher embarks on after having a well-thought-out idea about the problems. In this stage, the researcher is focused on one thing; sourcing and collecting as much data available as possible, and arranging and prioritizing them in an organized manner so they are ready for proper analysis. Here, customer demographic data can be collected out in the field through the survey to be returned in the form of feedback on their feelings about a certain company’s product. 

3. Data Refinement Stage

The data refinement stage is the booth where all the collected data are gathered and piled up so that the valid ones are separated and sifted from the invalid others. The researcher takes good care here to discard data that have errors, have been duplicated, or contain empty or void spaces. This is a very tricky stage in the whole data analysis process, therefore, the analyst must pay particular attention in other to correctly single out the ‘valids’ from the ‘invalids’ because failure to do this might affect the final numbers. Here the company might notice that some respondents misfiled the survey, left it void, or did not properly understand the use of the data collection tool in use. 

4. Data Analysis Stage

This is the stage where proper analysis of the garnered data takes place, and this process typically involves reading, examination, and representation of the raw data gotten from the field. As this stage is relieved by the researchers, they may either find out that the solution to their long-held inquiry is well within their reach, or that they are not even close and therefore must head back into the fields to collect more data. Depending on the reality of the issue, business researchers may discover, at this stage through customers feedbacks that the reason they are seeing a decline in the sale of goods and commodities is that they probably cut down on brand quality.  

5. Data Interpretation Stage

The is the post-analysis stage where the researcher can now aim to communicate or share his findings through any medium that is understandable to the particular people he or she wants to pass the information to. Depending on the discretion of the analyst – and who results are intended for – charts, tables, or words can be used to interpret these findings. 

Major Tools for Data Analysis

There are many tools used by data analysts to examine and determine the properties of data. While a lot of these tools are a bit advanced and might need the user to have undergone a proper degree in programming to be able to use them, others appear simpler, easier to use, and do not require any advanced education. 

1. Tableau

A tableau is a notable tool exploited by data analysts when examining correlations between data. It is one of the easiest analytical tools available and is very efficient in delivery with its main features including creating a nice interface for dissecting data. Tableau is regarded by many users as offering a better visualization and data handling ability than similar tools such as Excel. 

2. Excel

Probably the most popular data analytic tool that has ever been created, excel is no doubt a trusted go-to instrument for data analysts. This tool is so good that everyone, including those who already have a tableau, has it. Excel may not be the best in terms of feel and visualization, but when it comes to performing some real tedious data analytic work, excel is up there as the greatest. 

3. Oracle Cloud 

When you talk of a cloud-based busin5 data analyzer, the Oracle Analytics Cloud cannot be missed. This state-of-the-art cloud computing technology helps businesses with all the intellectual tools they need to collect and track a large mass of corporate data. Oracle cloud is especially praised for its extensive range of features which, aside from the usual properties every analytic tool has, offers machine learning algorithms for data tracking. 

4. Google Data Studio

This free Google analytical tool offers optimum data visualization and dashboarding abilities to analysts, and it is super easy to synchronize and track all your data across all the other Google analytical services. Is the tool provides a greater value for marketers who are looking to consolidate data across several Google platforms like Google BigQuery and Google Analytics, for ease of reading and monitoring?

5. SAS

If you would consider one popular analytics tool that has always strive to prove itself despite stiff competition coming from innovations, SAS is definitely among the ones you would look to. This tool brings to the table bespoke modules to offer analytics to IT hotshot trends like the IoT (internet of things) and SAS anti-money laundering.


ORI.”Responsible Conduct in Data Management.”

Jigsaw Academy. “25 Most Popular Data Analytics Tools To Know In 2021”.

Simplilearn.”What is Data Analysis: Methods, Process and Types Explained.”


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