Data Analysis within the organization

Data Quality Data Quality

A company can generate large volumes of data. From all departments: Finance, Human Resources, Sales, Marketing or even from Suppliers/vendors. Proper data management can lead companies to monetize this data.

For companies born under the digital paradigm, data analysis is their most precious asset. Gartner predicts that, in 2022, companies will be valued based on their information portfolios or databases.

That is why we will need tools that allow us to connect multiple types of data from different systems, applications, and data management software, to integrate the results in a data lake. This process is called data mining. Now, once we have our business data centralized in one place, we must convert it into important information for our organization, which adds value to the decision-making process.

For the appropriate interpretation and presentation of data, companies must make use of important concepts when processing information. Issues such as data quality, preparing data, knowing how to visualize it in the best way, are important procedures when taking advantage of data, as well as recognizing when there is presence of Big Data.

Data Quality

Data quality is the process of conditioning databases to meet the specific needs of corporate users. Remember that data is your organization's most valuable asset and decisions based on erroneous data can have a detrimental effect on your business.

Data Quality benefits

Companies obtain important benefits that add value to the business and are able differentiate themselves from the rest of their competitors when they give importance to the quality of their data. This allows them to:

The high price of overlooking quality

The information that a company extracts from data mining will be of the same quality as that data. Poor data can come from any area of an organization and in any formats, which can lead to difficulties in extracting information and ultimately poor decision-making.

Over time, doing nothing ends up taking its toll. Poor data quality can be mitigated much more easily if detected before it is implemented, at its point of origin.

Analyzing company data

Data Analysis Process can be divided in 5 main tasks:

1. Obtain the data:

Proper data mining guarantees a company to have the current situation at hand. Companies can have various data sources and data types (Structured: databases / Unstructured: files, images, videos, etc.)

2. Prepare the data

Proper separation, grouping, filtering of data and analysis is imperative. For example: by type of source or by type of data, seeing as how each classification will be given a different treatment.

3. Curating data

One of the most important phases of the process, as it allows for the identification of incomplete, incorrect, inaccurate, and irrelevant data. Data that does not add value to the information is discarded -- so that it does not make noise to the process.

4. Data analysis

It consists of consolidating, inspecting, and transforming data with the objective of highlighting useful information and brining about conclusions for the company's decision-making process. In addition, in this phase, data projections can be made to predict future behavior or present diverse scenarios.

5. Data visualization

This phase is the one that the user captures in its entirety. The data must tell a story. It is the process of searching, interpreting, and comparing data that allows in-depth and detailed knowledge, in such a way that it becomes understandable and useful to the user. This is where data management software comes into play.


The information society has large amounts of data. However, it is not just about having the technology to obtain and analyze data, but being able to give meaning to these figures and statistics to be able to tell a story and most importantly, be able to make the best decisions for the good of an organization.

"Data is one of the organization's most valuable assets" - Cintia Carmona, Senior Consultant


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