Business enterprises relies on high-quality and reliable data. With the help of useful data, you can make better, more educated judgments. It’s pointless to waste time and money examining inaccurate data. You’re probably jeopardizing your goal if you’re not putting your data through a preparation and transformation process.
According to the study conducted by AIxOutlook, 60% of the business enterprises believe that the customer records data with them are inaccurate. For several reasons, inaccurate information gets included in the database, and proper steps are required to filter and sort out the data. The output obtained from the analysis using these data may be inaccurate and may ends up in incorrect assumptions.
Data science includes various tasks like data exploration, data visualization, etc., and data preparation is one of the important tasks that are not that enjoyable and are not much recognized in the process. In the process of data analysis, a data scientist spends the lion’s share of his time on data preparation. The data obtained is stored in multiple formats and this makes data analysis much more difficult. The non-uniformity in data formats will make the analysis process slower. Sometimes multiple versions of the data appear, which creates confusion for analysts. Data inconsistency always poses a great challenge to the analysis process. The accessibility to data is sometimes broad and sometimes limited. Unnecessary information always makes the analysis process more complex. Analytics will only be easier if the data is sorted and filtered. So, data preparation has an integral role in the whole analytics process.
Data preparation has reasonable importance in data-driven world. In a totally digitalized period, almost all the processes and the information gained are based on data. Huge amount of information has been generated instantly by different sources instantly. For the detailed analytics of this information and accurate predictions, evidence should be connected, collated, and consumed in a clean and simple manner that is easily accessible to analysts and data scientists.
The accuracy of the report made by an analyst depends upon the information collected and processed. So, each step is important in the process of data analysis to obtain reliable outputs. Let’s have a look at the first and foremost step, Data Preparation.
Data Preparation
Data preparation is the process of collecting and transforming unprocessed data into a format in which it can be easily analyzed. The data preparation makes sure that the data is collected and transformed into a fully reliable, and accurate format. There are several sources for gaining facts and figures, and these unprocessed reports may be in different formats. Data preparation aims at producing clean data required for accurate reporting. The process will only be completed if the data is cleansed, formatted, and transformed into a uniform standard format that is digestible for analytical tools. can read easily. The major steps in the data preparation process are:
• Accessing the Data: The information needed for the data collection needs to be accessed primarily.
• Data Discovery: information collected is studied in this stage and patterns are identified, which helps in providing a structure for data cleaning.
• Data Cleansing: The unnecessary information is removed, and the data is processed into an easily combinable format.
• Data Transformation: The filtered data is enriched and transformed into a uniform format that can be easily accessible and understandable for analysis.
• Data Storage: The last step of the process. The data is stored in the cloud, where it can be accessed by analysts for analysis purposes.
Accurate and easily accessible information helps organizations grow at a faster pace. Proper insights can only be mined if data preparation is done carefully. Without preparation, artificial intelligence (AI) or machine learning (ML) programs won’t be able to read the pattern and may exclude it in the analysis process. Proper data preparation will help in eliminating errors. The errors identified in the initial steps will makes analysis easier. The data quality increases as the processes like cleaning and transformation delivers high-quality information. The quality information will enhance the efficiency of the enterprises, as they provide accurate output which helps in the firm’s decision making.
Conclusion
We are currently going through an era of data. Data and datasets are getting bigger day by day, and the importance of data preparation is also increasing. Industries that rely on these data and datasets require data preparation services. Accurate insights can be obtained using accurate data. If data preparation is not done, there is a good chance that insights will be wrong due to trash data, neglected diagnostic problems, or an easily rectified disparity among datasets.
To gain actual and precise information, clean and consistent data is required. Significant data patterns may be missed and sometimes get excluded if data preparation is not done properly. Accurate and sorted data enables enterprises to make decisions easily and helps in their business growth.
