Big Data: What are the Qualities Required to Become A Data Analyst?

At every moment, business must process a lot of information. Queries from customers, interactions with prospect, use of online platforms, lead scoring, supplier risk management…Big Data is everywhere.To transform Big Data into smart data, it is necessary to have a person who can understand data, organize and process data correctly so that it has concrete and real utility.

Data Analyst: a passionate profession

Making the numbers speak: that’s what drives a Data Analyst on a daily basis. Its role is to explore an ocean of data to identify trends and indicators that are useful for the branch or for a particular service. Data Analyst makes it possible to improve existing products, to create and structure a new offer while optimizing certain production processes.

Transforming raw data into useful information requires extensive engineering work and mastery of statistical tools and methods. Data Analyst may also be required to manage complex application implementation projects related to the creation or update of a CRM (Customer Relationship Management) tool, DMP (Data Management Platform), MDM (Master Data Management) or Data Warehouse.

The qualities of the Data Analyst:

1. Scientific rigor

No one can work in statistical engineering without a scientific approach. Data Analyst must be rigorous in its daily organization, but also in its tools and working methods. From data extraction formulas to the compilation of algorithms, and to the tracking of requests and requests, everything must be perfectly framed in order to respond to work processes of irreproachable quality. Providing a recommendation based on wrong calculations could have significant consequences for the company.

2. Economic and marketing culture

Data Analyst is at the service of other services and departments. This means that it works in an interdependent ecosystem where each decision can impact many parameters. That’s why the analyst must have a good economic, marketing and organizational culture, map the interactions between services and be able to work with many people in multidisciplinary projects. Having the desire to understand how the business works and how internal information flows are organized makes it possible to work more efficiently to optimize data processing.

3. Fluency in English

In the professional environment of new technologies, English remains unavoidable. Some software and most of the available resources are in English. In large groups with an international dimension, the Data Analyst may also be required to work with data from foreign subsidiaries, for which its restitution will also be in English.

4. The analytical mind

The role of the Data Analyst is to look for simple solutions to complex problems. To achieve this, the analyst’s reasoning and analytical capacity must be particularly well structured. The analytical mind is what makes it possible to synthesize information, to have a holistic look at a problem and to look for answers that analyze the facts, beyond a simple display effect, a way to create significant added value to bring a new perspective on a key issue or issue.

5. Confidentiality and discretion

Because it may have to work with sensitive data (financial and accounting information, personal data, etc.), the Data Analyst must be trustworthy. The confidentiality of information is an absolute rule in the business. It manipulates the data for a specific purpose and must follow very strict protocols for not disclosing information that must remain secret. Knowing how to remain discreet, humble and responsible are essential qualities for a good Data Analyst.

While the Data Analyst business is growing in large groups, mid-sized companies, specialized firms and agencies, it is a rapidly evolving business. It has to adapt to new codes and technologies, and continually enrich its knowledge in a highly mutagenic market. After several years of experience, the Data Analyst can then evolve to positions of Senior Data Analyst or Data Scientist.