How to Prepare Data Correctly
Data preparation accounts for a significant amount of time and effort in a company. According to a survey by CrowdFlower, 80% of the work of data scientists is used for data preparation. However, still from the same survey, 76% of data scientists see data preparation as the least enjoyable task about their job. What is data preparation and how can we prepare data correctly and efficiently?
Data preparation is the act of pre-processing the raw data that may come from different sources into a certain format that is ready and can be analyzed accurately. Data preparation aims to tackle two significant issues in data analytics, which are the systemic errors in a large set of data records due to non-standardized data format from different sources and the individual errors in smaller numbers of data records due to mistakes in the original data entry. In getting started with your data preparation, here are some steps that you need to do.
Start with formulating a data preparation strategy
Just like any other projects and activities, the first step in data preparation is always to develop the strategy. In data preparation, developing a strategy means to formulate a workflow process that will cover all of the steps that you need to do the required tasks and to meet the objectives and desired outcomes, as well as determining how the tasks can be applied to different types of data. In short, before you even started, you need to list out all activities that you need to do and make sure that you understand how to do them properly.
Remove inaccurate or damaged data with data cleansing
The next step is to do data cleansing. Data cleansing is an activity in which you need to remove the inaccurate, error, damaged, or corrupt data so that you don’t use this undesirable data during the analytics process because it will affect the accuracy of your decision making. Traditionally, data cleansing is the most time-consuming part of the data preparation process. According to CrowdFlower, data scientists spend 60% of their time cleaning and organizing data, but 57% of them consider data cleaning and organizing data are the least favorite part of their work. However painful data cleansing might be, this is a necessary task that removes extraneous data and outliers, filling in missing values, conforming data to a standardized format, and masking private or sensitive data entries. Once it has been properly cleansed, your data needs to be validated by doing testing to find errors. Most of the time you will find errors during this process and find a way to resolve them before moving forward.
Transform, standardize and store your ready to use data
The final part of data preparation is data transformation, standardization, and storage. Data transformation is a step to transform your data into the correct format for your analytics system to work with. Once you have transformed your data into ready to use data, you can also perform data standardization tasks, ensuring your data is presented in a uniform way, especially for specific data such as dates, names, and geographical location. This will help avoid confusion during analysis. Once data is prepared, you can store your data into a third-party application, such as business intelligence tools, and start the analytics process.
Investing in Big Data Indonesia, you need to understand the importance of data preparation before starting to do an analytics process. Companies that fail to prepare their data properly will make inaccurate business decisions and risk their business. Not only that, when you don’t do your data preparation right you will waste a significant amount of time and resources to check, validate, and repeat all the analytic processes once you find the error after you do your analytics. Moreover, if you don’t take data preparation seriously it will also affect the morale and productivity of your employees because they need to spend their time fixing errors, while if prepared correctly they can use their time to do the analysis and find the best solution for your business.