The rise of Big Data and the digital transformation of businesses have brought about new challenges in data management. Indeed, before representing data on a dashboard, it is essential to ensure their quality and reliability. This is where data preparation comes in, involving the cleaning of raw data to make it perfectly usable.
What is Data Prep?
In a Business Intelligence project, data preparation (also known as data prep) is a process that precedes data analysis. It encompasses various tasks such as collection, cleaning, enrichment, or transformation of data.
What are the Challenges of Data preparation?
As companies deal with increasingly numerous, scattered, and heterogeneous data, significant preparation work is required before moving on to analysis. The relevance of the analysis directly depends on the quality of the data.
For organizations, using data preparation methods and tools is a major challenge. In a rapidly transforming environment, data must be constantly processed and updated to draw reliable conclusions.
Moreover, companies are increasingly leveraging data for strategic decision-making. To achieve this, they rely on Business Intelligence tools to generate dashboards and reports. Quality data, properly prepared in advance, is crucial for making informed decisions, staying competitive, and satisfying demanding customers.
The main steps of data preparation
To conduct reliable analyses, a company must first ensure access to data and enhance it for perfect usability. Thus, data preparation can be divided into four major steps.
The initial step in the data preparation process is to make the data accessible to users so they can improve, organize, and ultimately analyze it.
For this purpose, the data is placed in a storage space, often a data warehouse. Hosted in a data center or the cloud, the data warehouse allows the organization to collect data at regular intervals from multiple sources. The company can leverage the ETL (Extract Transform Load) process for this.
However, other storage solutions, such as data marts or data lakes, can be used, distinguishing themselves by the nature of the stored data. For example, the data warehouse is suitable for structured data, while the data lake is for storing raw data. In any case, the company can choose between on-site or cloud deployment.
Setting up a data catalog is also highly useful to facilitate data access within an organization. This centralized location serves two main functions:
- Data cataloging.
- Metadata management, i.e., “data about data.”
It provides valuable information for users to locate and understand the data while automating metadata management. Ultimately, the data catalog brings more agility to the dataprep process and allows for a better evaluation of the value of data or a dataset.
Data cleaning is arguably the longest step in a data preparation project. However, it is essential to eliminate “bad data,” which can be incorrect for various reasons: input errors, duplicates, lexical errors, missing values, semantic errors, incorrect formats, etc.
Various methods can be employed to correct these issues. In all cases, cleaning involves filling in missing information, filtering out aberrant values, or deduplication.
Despite being complex and tedious, this dataprep step is crucial because any data-related errors will impact the quality of the analysis, potentially harming service quality and customer experience. As companies accumulate more data, the risk of errors increases, making data cleaning increasingly important.
After cleaning the data, it’s time to transform and enrich it.
Data enrichment refers to the fusion of internal company data with external data. Organizations often use third-party data sources during the data prep process.
However, these external data must be relevant and complementary to internal data while adding real value to the existing dataset. Moreover, merging multiple datasets poses certain risks.
- External data may contain errors, jeopardizing the reliability of the analysis. Therefore, sources must be carefully selected, and the resulting data must be verified.
- Data from external sources may follow different patterns or rules. In such cases, it is essential to transform them before integration to ensure they adhere to the same format as internal data.
Regardless of the precision of the extracted data, all data-driven companies face a common challenge: often, data is only relevant to specific dates and contexts.
In other words, data can quickly become obsolete, risking compromising the analysis if it’s not regularly updated. Therefore, the last step in dataprep is to update datasets as needed.
Enabling the collection, correction, and enrichment of datasets, dataprep is an essential ingredient for a successful data strategy. Hence, using an efficient data preparation tool within the company is crucial.