What is data? Well, to answer this question we must first of all define what data is. Data are discrete units of information, most often numerical, which are collected by observation. In a broader sense, data can also be a collection of values of either quantitative or qualitative characteristics about one or more people or things, while a data object is a single identified value of a single quantitative or qualitative variable.
It is vital to keep in mind that data cannot be studied from a qualitative perspective alone. This means that to conduct or measure anything qualitative, we must carry out quantitative research, as well. How then do we make sense of what is data? The simplest way is to view it as raw facts, unprocessed and unformulated. We make use of statistical techniques, such as analysis, to transform these raw and unframed facts into useful knowledge. This way we can make sense of what is data.
The major difference between what is data analysis is that what is data is largely empirical in nature and what is data analysis primarily qualitative. The major aim of what is data is to provide useful and meaningful information for the decision making process of an organization. This aims at providing quality information that can be used to make informed decisions, rather than arbitrary or biased decisions. Such information should be unbiased, so as not to create partiality in the stakeholders’ decision-making process. The major goal of what is data is to make quantitative and qualitative research feasible.
The purpose of what is data is also to provide valuable insights on how the organization can improve its performance and meet its objectives. Data can help in formulating better policies and reforms, discovering what areas need improvement in performance, and measure whether the existing policies are meeting the desired objectives. It also helps in understanding customer needs and in offering solutions that can address these needs. It can even provide insight on the flaws in the business or in the organization and enable corrective measures to be taken. Finally, it can indicate what is not working well and how improvements can be made.
So, what is data driven? The answer is that data is the set of facts that are processed by a human being in order to provide knowledge or insights about a particular event or a category of events and it is a product of human thinking, observation and action. Data thus represents the summary of human learning and the output of those processes. The quality of this data is qualitative because the output depends on what is being observed and measured and therefore on what the stakeholders want to measure (or consider as relevant to their goals). Thus, what is data is a summary of what is already known and what can be quantified.
But what is qualitative data? This is another question that has to do with what is data and what is qualitative. Qualitative data is the type of data that comes from many sources and is highly aggregated (based on more than one source) and this represents an extremely complex data set. The output from this type of data may be qualitative but it cannot be quantitative because it is more of an analysis than a random occurrence.
For example, a survey question may ask participants to rate their satisfaction on service from a customer satisfaction survey. If the data set is qualitative, then we would expect that the rating would be more or less the same (in other words, people would not give a five-star rating to a business that had one bad survey and a five-star rating to a business that had one good survey). However, if the question is quantitative, such as sales for a certain month, then the rating should be based on the data gathered in the month before the survey was taken. This is why many businesses choose to collect data in a discrete, quantitative way.
The above example illustrates the difference between quantitative data (such as golf balls) and qualitative data (such as customer satisfaction). If you are a business that needs to analyze and understand data, you should consider using both types of data. Although using both types of data will require additional resources, it can be a more effective and efficient way to collect information and generate trends and analysis. Also, it can help make your data analysis more accurate and relevant. In this case, both discrete and quantitative data might be necessary for your business to make the best decisions.