Why Data Preparation is Crucial in Data Science?

Data is described as the “new oil”, as well as the new language today. Nevertheless, the profit delivered by the data is not volatile like the real oil, in contrast, this “new oil” provides valuable insights and meaningful information that enable the organisations to make informed and actionable decisions to effectively run their businesses. Before they do that, the organisations must first organise the data and make it ready for analysis to gain further understanding, which is what Data preparation serves for.  

  

Most of the companies have been collecting different kind of information from their operational transactions and business processes, in fact, not all the data collected is actually useful for them and some organisations might not even understand the value of the information they have been collected throughout the years. In the meantime, your organisation may have equipped with the best models or BI applications for business analytics. If the coffee beans are not processed and roasted properly, you would not get a nice brew of coffee, even you have the best coffee machine in the world.  

  

Data wrangling

Data preparation (also known as “Data Wrangling” or “Data Cleaning”) plays a critical role in Data Sciences, particularly in the context of AI, self-service analytics, and predictive modelling. Data scientists have been spending most of their time and effort to review, reshape and refine this huge amount of data into usable datasets, so that the datasets can be leveraged, exploited and analysed to gain meaningful insights to support decision-making. In the process of data preparation, the data scientists (1) collecting data from various sources, (2) collating, consolidating and “cleaning up” data, then (3) reformatting and restructuring them to be ready for use by other analytics tools and BI applications. Therefore, data preparation is undeniably is the most time-consuming phase of the entire life cycle of analysis.  

  

How data preparation works? 

While data is gathered, the raw data are stored differently based on their data formats. In this situation, different type of tools is required to connect to the respective data sources, which can be difficult and a burdensome for data scientists and business analysts. For instance, the structured data (e.g. Excel files) is commonly stored in the relational databases and individuals use SQL to query the data, whilst the unstructured data (e.g. video files, audio, No-SQL databases, etc.). 

  

In the data preparation process, the data scientists will implement data profiling, which is performing a study or analysis to evaluate the data quality, find out the fields with no right information values, blanks, and duplications that could distort the model and affect the results the for predictive analysis, then take further actions to clean these messy data (e.g. merging, filtering, aggregating, transforming, deduping, appending and editing). Afterwards, the entire data sets will be broken down into smaller datasets (also known as data sampling). These sample sets will be utilised for testing, validating and training the model based on different scenarios. 

 

Advantages of Data Preparation 

Firstly, when the raw data are sanitised, enriched and structured by applying the data preparation methods, the organisation will be able to have a clear and comprehensive overview of the real situations in both internal (company and operation) and external (targeted market and industry) environment, as well as gaining a better understanding about the market trends, consumer behaviour and their current needs. This helps to nurture a data-driven culture and support the management to make higher-quality decisions which will not only able to addressing the current issues in a practical and rational manners, but also satisfying the needs and wants of their end-customers. This is because their decisions can be justified and developed based on credible information and useful insights from cleaned and consolidated data.  

  

Besides that, data preparation will make the data accessible for the users (internal and external stakeholders) across an organisation, which the users will gain their authority to get the information that is related to them with their own access and permission credentials. This method will not only empowers collaboration and enhance communication between teams, departments or even groups, it will also allow the users to leverage the data found in a single, code-free environment to deliver useful information that helps them to make effective business decisions to take necessary actions immediately at the exact timings without any delay. 

  

The data preparation tools enable the business users to evaluate and investigate the accuracy of the raw data in advance before they decide to make investment of time and resources on conducting a range of analyses to do further digging, which is more cost-effective and time-saving for the organisations. Furthermore, the data processing will be faster and more efficient with improved data quality and accuracy. As a result, the users will be able to extract the insights instantly, interact with other parties or customers in real-time, while the enterprises can be more responsive and proactive to deal with the changing business and market dynamics to meet their business objectives successfully. 

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