Do you catch yourself ever wondering how are certain companies so successful at their marketing strategies, or commending their brilliant business strategies? Well, there is a constant that backs these successes and it is called Data Analysis. In order to make the appropriate business decisions, decision-makers need to have access to the appropriate data that will best tell them how their results will fare in certain circumstances and this is how data analysis functions. So, if you are ready to learn how to grow your business by using data analysis to make better business decisions, follow along with us now!
Data Analysis Tools
Before we delve into further details regarding the whole process, here are some tools that can make your life a whole lot easier by automating a portion of the process. Some of these tools are the more well-known ones like Python, SQL, Java, and MATLAB. It will be good to have some mastery of these programming languages. You can also learn how to use these tools with data analytics course singapore.
Types of Data Analysis
Data analysis can be split into many different types depending on the method. There are five major ways to do so.
Textual analysis is also known as data mining. This is mainly concerned with uncovering patterns in large data sets. Archaic methods included painstakingly going through every set of data and writing it out to actually spot patterns. But, data analysis tools have made our lives easier as these large sets of data can simply be inputted and the tools will generate a pattern for your interpretation. Hence, this type of analysis is suitable if you have access to a large pool of data sets.
Statistical analysis is similar to text analysis however, what it does is not so much about sieving out patterns but about presenting past results in ways that tell the observer what has occurred or allows the observer to make inferences of their own accord. While text analysis is concerned with the trend, statistical analysis is concerned with the whole sets of data. That being said, there are two kinds of statistical analysis: descriptive and inferential analysis.
Descriptive analysis is better for those who work well with numbers. This is because this form of analysis will break down past data into numerical values like mean and deviation.
Inferential analysis, on the other hand, allows the observer to draw conclusions using the different sets of data to explain a certain event.
While statistical analysis is concerned with what event has occurred, diagnostic analysis mainly cares about why a particular event occurs. A diagnostic analysis allows you to categorize similar sets of events together so that if a similar problem occurs in the near future, you have some form of reference point and you can apply old solutions to these new problems first.
This is what most people are interested in. The previous few types of data analysis explain and rationalize past results. However, predictive analysis is more forward-thinking, it tells you the potential of something happening in the future. So, this is similar to the above in that, we still analyze past sets of data. The only difference is that we are interpreting these sets of data to represent future scenarios. While there is no 100% accuracy with this type, we can achieve higher accuracy with more constructive information.
The above are all data gathering, and prescriptive analysis is where you make use of all the information in order to concoct a solution. Nothing will ever change unless you decide to act on it. Hence, most companies delve into this type of analysis as it has a call-to-action.
Process of Data Analysis
Now that we know the types of analysis available, let us see where we can apply them in each phase of data analysis.
Step 1: Data Requirement Gathering
Before anyone can start on anything, they would first have to know for what purpose they are starting it. So, here, think about what you need this data for. You need to understand what you are trying to achieve and what are some of the methods to achieve it. So, this step will function as your blueprint.
Step 2: Data Collection
Next, now that you have a direction to work towards, it is time to do the actual data collection. So, whatever you planned in the previous step, it is time to execute. For example, if you decided that the best way to gather the data you need is through a survey of your current clientele, then it is time to work on doing just that. Pro-tip: organize your data, you will thank yourself later on.
Step 3: Data Cleaning
This is another step that you will thank yourself for. Not all data that you collect is going to be useful to you and adding these into your data set could give you inaccurate results. Aside from that, you might also want to look out for some human errors like duplicate records and all that. This is because if you were to just skip to data analysis, the tools might not be able to read the errors.
Step 4: Data Analysis
Here is where you bring in all the tools that we have mentioned before to help make your life easier. Here, you can refer to the above on the type of analysis that can be done on your data set.
Step 5: Data Interpretation
After settling step 4, it is now time to figure out what the data means to you. This can be done in words or it can be done by using a table or chart to represent them.
Step 6: Data Visualization
Speaking of tables and charts, here is where it is important for you to explain the results of your finding to other people. Here, writing it out in words is no longer an option. Tables and charts do a better job of conveying patterns and trends which will allow you to justify business decisions easier.
To sum up, the above are all the tips and tricks you need to know for data analysis, and remember, it is rare to get it right the first time, so keep trying!
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