Data preparation is the act of pre-processing raw data from different data sources, into a form that can easily and accurately be analyzed. And today there is no point in spending time and money analyzing bad or unreliable data. If you’re not putting your data through a preparation and transformation process, you’re likely compromising your observations.
Data preparation is the first step in data analytics projects and it includes piling- up data, identifying & assessing data, changing & validating data, remolding & refining data and accumulating data.
Steps of Data Preparation:
Piled- up Data: It means gathering relevant data from different data sources like operating system, data catalog, data warehouses.
Identify & Assess Data: This step implies that the gathered data needs to be explored as it is important to understand what can be done with the sets of raw data.
Change & Validate Date: This is the cleaning step and it is the most time-consuming step. It is the process of setting all raw data and eliminating the identified error data to make the data ready to be processed. That is: faulty data is removed or fixed, missing values are filled in and inconsistent entries are balanced.
Remold & Refine Data: After the error are resolved, this step performs. That is, it takes the set of refined data and transform to the format, where the data can be easily understood and analyzed. And then the data enrichment further enhances data sets with related information to produce the desired insights.
Accumulating Data: This is the final step, where the data is stored safely into a third-party application which is also know as data warehouse being easily taken and looked over.
These Data Preparation have many benefits as well:
- Avoid duplication of efforts in preparing data that is used in multiple applications;
- Prepare data for analysis in a cost-effective and efficient way;
- Identify and fix data issues that otherwise might not be detected;
- Make more informed business decisions because executives have access to better data.
And moving the data and data processes to the cloud makes data preparation more beneficial, such as:
- It allows team to work and complete faster, as it does not require any technical installation.
- It helps the business perform in a higher pace and enhance the growth of the organization.
- In cloud, data preparation upgrades automatically, in result the new capabilities and problem fixes are turned on as soon as they are released. So, the organizations stay on point without any delays.
