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Data Preparation for a better Data Analysis

Alice Orecchio

“A good start is half the battle” they say, and this is also true when it comes to data governance and data analysis.

What is data preparation

Data preparation is a methodology that prepares data for data analysis. After a process of cleaning and organization, the data is easier to manage for the analysis phase, saving time and effort.

Clean data means quality and more accessible data. Of course, the more complex the data set, the more time you need to spend on the preliminary preparation, before feeding the data to the descriptive analysis processes.

Lately we are witnessing a growing trend of democratization of data virtualization tools, which are now within the reach of SMEs. This way they can obtain more integrated, flexible and activable data, automatically compliant with GDPR and other relevant regulations.

Data preparation is certainly involved in this evolution: let’s see how it is carried out


Data Gathering is the process that allows you to collect and unify data from different sources: databases, data lakes, data warehouses, websites.

Often you might need to broaden your field of analysis and rely on external, alternative data sets, which – combined with proprietary data – are able to respond to specific business needs.


With Data Discovery, the collected data is explored in order to identify any critical issues in the data sets – such as inconsistencies, anomalies, incorrect data attribution. The aim is to resolve them promptly and make the data correctly viewable.

In identifying the problems, it is also useful to draw up a list of needs: those requirements that the analysis aims to satisfy.

Cleansing and Transformation

Data Cleaning – also called Data Cleansing – is primarily concerned with eliminating background noise from the dataset.

Often when processing large amounts of data, you might notice they tend to be redundant, risking overlap in duplicates. This phase takes a long time, but it is essential to obtain a consistent, reliable and unique database.

In this sense, the Data Transformation is what you need, to make data usable and compatible with the various applications, using unique formats (such as that of the date: DD / MM / YY).

Modeling and Enrichment

With Data Modeling, the different types of data are modeled and structured to respond to your analytics tools’ specific requests.

Through Data Enrichment, data analysts enrich data with alternative sources, with new insights aligned with business needs, to truly ground strategic decisions on data.


Data Validation is the last phase of Data Preparation, where data is subjected to a further automatic check to verify its accuracy and consistency.

Although at first glance it may seem like a cumbersome process, Data Preparation is a fundamental task to derive the greatest possible value from the data at your disposal, and avoid enormous waste of time and effort later.

Before you start the process, it is crucial to understand the most useful tools and methodologies to be used in the subsequent analysis process.

To face the challenges of this autumn and winter, Artificial Intelligence applied to data becomes a key ally for companies, guaranteeing greater efficiency, flexibility and productivity.