What Is Data Analysis? Find Out the Whole Process in 4 Steps

In the era we live in, everything is data and data is everywhere. Today 2,5 quintillion bytes of data are produced each day, and it is estimated that the global amount of data which is 45 zettabytes in 2019, will increase to 175 zettabytes by 2025. So, how can a business or organization process such a great amount of data for its benefits? In this article, you will find out what is data analysis and how the whole process works.

What Is Data Analysis?

Data analysis is the name of the whole process of data management that allows obtaining useful information from raw data. In this way, the meanless data turns into more comprehensible information and provides us beneficial knowledge. So, you can observe historical data for understanding the current state of your business and organization or you can also use them for predicting future possibilities. When we can see the first examples in history such as Sumerian or Egyptian census, now many sectors from healthcare to marketing take advantage of it as increasing efficiency and optimizing performance by advanced technologies.

There are many types for different purposes but before starting let’s quote from the most known nurse by the public but also a fascinating woman who creates diagrams and graphs with mathematical skills, Florence Nightingale. She emphasizes the power of data visualization as follows; “Diagrams are of great utility for illustrating certain questions of vital statistics by conveying ideas on the subject through the eye, which cannot be so readily grasped when contained in figures”.

Types of Data Analysis

There are 4 main types of data analysis that two of them concern with the past and the rest with the future. Descriptive analysis is in the first group and frequently used by organizations for summarizing and visualizing a data set. This means you make historical data turn into more readable visuals with graphics, charts, and tables. The most common examples are last year’s sales figure chart or survey results table.

Diagnostic analysis also explains causes of past happenings as distinct from previous. If we exemplify it again by sales figures, besides the numbers, it will also show why they increase or drop. For instance, while advertising accounts for an increase in sales, the weather can cause a decline for off seasonal products.

Predictive analysis also uses historical data but this time for forecasting possible results by identified trends and statistical models. Artificial intelligence and machine learning are so effective to this type. As an example, by examining previous customer preferences, a business can estimate who buys which product and so determine the market strategy accordingly.

With prescriptive analysis, you can achieve the best solutions based on previous types. It allows to determine long or short term plans and strategies by observing past and current performances, possible scenarios and existing resources. As the most advanced type of data analysis, this really improves decision-making by using machine learning and neural networks.

Now, let’s examine the process from beginning to end.

Step 1- Determining Requirements

The first step is so important for finding your way in a huge data ocean and your requirements will be kind of your compass here. Firstly, you have to identify your aims, needs, and expectations by scanning documentation and making conversation with key stakeholders. After that, the next to do is synthesizing the results and once the data quality analyst clearly determines the requirements in detail you can pass the next step.

Step 2- Collecting Data

In this stage, data is collected from all sources and transferred to the storage system. You can start with collecting data from the existing databases and after new ones. Before, there needs to be established a solid system to prevent the disorder. For instance, in the beginning, by determining a file storing system you can avoid collecting the same information twice, hence you will save time. Here we can mention edge analytics that allows real-time analysis at the same time with the collecting data step. It is used for smart city elements such as streetlights or any IoT devices or factories.

Step 3- Data Processing

After collecting data, you need to process data to be useful by organizing and cleaning it. In this step, data turn into information that makes more sense and finally is stored in a database like SQL or Oracle DB. Here are some important points to attention. Categorizing and classifying them as a specific folder will make it convenient to find them when you need them. Forming a file organization scheme is also important as not to lose time with reorganizing them. These are so efficient solutions especially if you work in a group.

Step 4- Analyzing

This is now the final part that data becomes comprehensible for everyone by visual representation. You can start to analyze the stored data and create data modeling by using various end-to-end analytics platforms such as Oracle or SAP. All these provide you to analyze and visualize data much easier, faster, and smarter. For instance, uploaded data is visualized automatically or as you add new data, there will be intelligent updates. In addition, as cost-saving is one of the key points for businesses, here, you can also reduce investment costs by toolkits, libraries and hardware-software optimization.