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Data preparation: what it is and how to prepare data to better process them

Manuela Bazzarelli

“A good start is half the battle” they say, and this is also true when it comes to data governance and data analysis: this is why data preparation is a crucial step for any type of project, whether impacting management or industrial processes.


What is data preparation: a methodology to rationalize data

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 a pre-trained algorithm dedicated to grid them.

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.

Obviously, this is not just a matter of technologies: skills come first, which is why the Aramix team includes different kinds of multidisciplinary STEM excellence. Our Data Scientists are Phds in mathematics, engineering, statistics, computer science.


5 steps of Data Preparation: from Gathering to Validation

Data preparation is certainly part of this evolution: let’s see what specific steps it implies.

Data Gathering

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

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.

Data Discovery

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.

The third step: Data Cleansing 

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).

Data Modeling, Structuring and Enrichment

With Data Structuring, 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: the last accuracy check

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

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


In conclusion, is Data Preparation useful even when there is little data?

What happens to companies that have not collected a sufficient number of relevant and “informative” data? AI and mathematical models can help.

In fact, the company’s experience is transferred to professionals that can turn phenomena into numerical observation, experts capable of compensating for the variables that cannot be directly seen. They are able to add exogenous variables to the process which can help to deduce the missing ones. For example, in many “theoretical” projects, it is necessary to start from a few pieces of data and then multiply them on the basis of theoretical evidence, to materialize patterns and extract value from them.

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.

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