In the 21st century, the lack of data is not a problem. Most of the organizations have already adopted an efficient process to collect relevant data they should have. Sometimes, there are ample data to derive the right decisions from the available data set. Here, the data analysis comes into play. Sorting large data demands a systemic approach to analysis.
This includes identifying the right questions that can deliver the proper answer, the ability to draw an accurate conclusion from the data set, gathering data that can verify the ultimate decision-making procedure.
The success of data analysis is measured by observing the deviation that occurred in the result and the real outcome. Optimizing the way of data analysis helps to be clear at decision points and also enhances the measurability of data analysis to the organization.
Table of Contents
Defining questions to find the correct data
The very beginning stage of data analysis is to find data, and setting proper questions is the first step to find data. The goal of data analysis should be precise. When the goal is clear, the analyzer will not face any problem meandering aimlessly through the data.
Failing to identify what the business is ultimately looking for results in disastrous failure in the end. Better the understanding of the problem, it is becoming easier for preparing potential solutions. Designing these questions should be framed in such a way that it should have the ability to qualify or disqualify probable answers for a particular question.
In this stage, the organization should identify which data sources the officials should use to finding data. It should be also checked whether the officials of that organization have the proper credentials and authorities to access these data sources.
The size of the data set is also an important aspect while setting question frames for finding the data set. The questions should be framed in a way that can decide a margin up to which the data set should be analyzed.
The quality of data analysis is measured by checking the quality of data set an organization has with it, its usability, portability and accessibility as well. There should be a scope to integrate the outcomes obtained from the question frame. Scope to data cleaning and data profiling should be available as well.
Need of changing data?
Sometimes the data are not available in a ready reckoner manner to be used instantly. Often these gathered data demands to be manipulated or manually transformed to a different format for bringing effectiveness in analysis. These mainly occur when the collection of data is done from different databases.
Different databases maintain the tables in different formats and analyzing can only be done when all the data are in the same format. Sometimes there is the repetition of the same data several times, before analyzing these duplicate data should be filtered.
If there is any need to group the available data set, changing the format is necessary. For each source, data should be accurate, up to date and should be complete. The data may need change if it is not usable in the current state or not capable of answering the questions framed in the previous stage.
Popular databases contain redundant values and inconsistency may happen in the tables, in those cases redundant data should be removed by a systematic cleaning process.
Be clear on measurement priorities
Measuring techniques draw big attention in data analysis. This step can be classified into two subcategories, deciding what to measure and also deciding how to measure. Deciding what to measure includes the identification of data that can answer the key questions of data analysis. The way of measuring includes the procedures by which the analyzer can deduce final decisions easily and effectively.
Connection of data
In databases, data are stored in multiple tables; these data should be organized in a certain manner that connection between the tables becomes easier. The data analysis should be able to connect different tables and deduce throws the expected result as per requirement.
Collection of data
There are many different strategies for gathering various sorts of quantitative data, but no matter which approach is used, it will often follow the same basic process. The following five steps make up this procedure. The first step is to decide what information should be collected.
Analyzers need to figure out what themes the data will cover and how much data is needed. Responses to these questions will determine the objectives. After that, the organization may start planning how officials can collect data. Establishing a timetable for data collecting should be done early in the planning process.
If an organization is tracking statistics for a specific campaign, officials can do that over a set period. In these cases, officials can set a schedule for when they will start and stop collecting data.
At this point, the data analyst selects the data collection method that will serve as the foundation of the data collection plan. To choose the best data collection strategy, the type of data required should be kept in mind and the duration needed to collect it, and the other factors if any.
After finalizing plans, a data analyst may start collecting data by implementing the data collection technique. In the local database, an organization can store and arrange data.
It’s a good idea to make a timetable for checking in on how data gathering is going on, especially if data collection is going on a regular basis. As conditions change and new information becomes available, data analysts should change the plan.
After gathering information, it’s time to analyze and organize it. The analysis process is critical because it transforms raw data into useful insights that can be used to improve marketing tactics, products, and company decisions.
Manipulation of data can be performed in different ways, such as finding a correlation between tables, plotting data, or making pivot tables in Excel. A pivot table allows sorting and filtering data by various variables. This also allows deducing mean, minimum, maximum and deviation for analysis of the data better.
Manipulation of data may demand revising of the question frame at a later time. At this stage, data analysis tools help the analyst a lot. Minitab, Visio and Stata are pioneer software that helps organizations to perform data analysis at the next level. Organizations can use the findings to better their business.
It is the final stage of data analytics, and the measure of success depends on the outcome of this stage. In this stage, the less probable hypothesis is decided to be disqualified. Within many probable outcomes, the best-suited result is derived by eliminating the less probable outcomes.
The final outcome should answer the question framed in the first stage. This result should be capable of defending probable objections. If any aspect is not considered previously, should be reviewed at this stage. If the final interpretation can hold up all the aspects mentioned above, then it can be declared as the productive conclusion of the data analysis.