What is Qualitative Data Analysis? Definition and Methods to Measure Performance


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Qualitative Data Analysis Definition:

Qualitative data analysis is a process of evaluating industrial data based on subjective data. For example: quality of innovative work, management decisions, industry model, relationship with other business entities, productivity, etc. This data is examined based on different types of ratio from various accounting statements, profit / loss statement and balance sheet of the company or an industry.

In previous chapter we have discussed about what are the indicators of productivity and how to analyze business intelligence applied by the company to improve job performance of workers. We could see that as productivity indicators, there are hundreds, depending on the industry, the position held in the same, and the tasks performed in these positions. While it is true that some are common to all businesses.


Qualitative Data Analysis of an Industry Performance:

Here we will understand about the qualitative data analysis methods and process wide used to evaluate the industry performance under fundamental analysis. This evaluation will assist you in making right strategic decision to sell or buy a stock of an industry or not.


Qualitative Data Analysis – Indicators:

Mathematical and financial data are always indicators that analysts use when evaluating diversion opportunities. However, there are also qualitative data on the quality of a business that can also provide valuable information.

First you have to keep in mind that quantitative variables such as the rate of growth of an industry or its profitability, always rely on the bottom of qualitative issues as their business model, their expansion opportunities or competitive advantages. When levels exceed the average expansion achieved based on an innovative technology, it is important to analyze the sustainability of these figures over time to assess whether or not to buy their shares.

If the market opportunity is exhausted or if competition eroding gains market share growth rates, it is expected that a considerable adjustment occurs in the asset price. Therefore, automatically project expansion levels past to the future, usually one of the most costly mistakes investors by focusing on a purely quantitative analysis. In the same vein, margins, high returns tend to attract competition, so it is important to assess whether the industry has strong advantages as to hold them in case their lower-cost rivals try to erode their market position by offering alternatives Price lower.

Let’s see some of the qualitative data analysis examples of the two types of indicators: qualitative indicator of productivity and quantitative indicator of productivity.


Wiki Finance pedia - e-learning course on Fundamental Analysis Wikipedia Chapter - What is Qualitative Data Analysis? Definition and Method to Evaluate Industry PerformanceQuantitative Indicators Of Productivity:

These productivity indicators always are based on amounts of time. It is what most companies are based on measuring the productivity of the company, team or product. Always it based on a ratio between quantity and time spent.

For example, we can measure the rate of productivity of an assembly line, dividing the number of pieces produced by the time it has been used in producing them. This index will tell us how productive we are in quantity, manufacturing speed.

The same happens in an office where an administrative need to answer emails, you can answer a certain amount of them at one time, and also see the advantage that time (breaks, distractions, interruptions, etc.).

In the commercial field, without going any further, there is also a quantitative indicator of productivity, and can be seen in the number of sales made, or the entered amount of money with those sales in a given time.


Qualitative Indicators Of Productivity:

These productivity indicators are based on the quality of the product or service offered and is closely linked to the efficiency of our productivity, and not on the amount produced.

Let’s continue with the above examples, and see what happens in that assembly, when analyzed qualitatively. In this case, we have two parallel chains, same part to be manufactured. The number of parts produced in one hour is measured, and those, which are counted, are good and if there is defective. The ratio of the two creates us an indicator of productivity on the quality of our product.

Qualitative data analysis is so important to know that a chain that produces 300 units in one hour, of which 50 are defective, is less productive than one that produces 270 and just 10 defective. Often, more quantity does not mean increased productivity, and not only for the right pieces, but because the wrong, or you have to fix them manually, or should be recycled, and both have a cost.

If we transfer this to the administration, the truth is that what matters is not the number of emails to answer, but the efficiency of their answers, problems to solve the company with them, and the work subsequently save to correct problems not they have remedied via email.

As for a trade, it could be applied in a similar way, thinking about sales, but also in the costs generated by that sale. That is, a business that sells a quantity of a certain product, but it has misjudged the margin, or arising unforeseen has not provided (transport, packaging, special modifications), increases the cost and reduces the margin, which reduces the benefit, and therefore, the quality of the sale has been low.


Conclusion:

Most companies are obsessed with quantitative factors to measure productivity, and forget qualitative indicators of productivity when analyzing the performance of the company. This error, which is very common, makes the difference between companies that have a strong brand, and companies with a weaker brand, you may have more ability to produce and billing, but whose benefits are not as high. There must be a balance between quantity and quality for an industry to progress properly and without making many efforts to manufacture more.

 

  

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