Within the Data or Tech division, the Data Analyst comes in after the work of the Data Scientist and the Data Engineer. He or she manipulates data to make sense of it and draw out insights and trends. These analyses will be used by managers to make strategic decisions, but also by business and marketing teams.
Data Analyst is also known as Data Miner Business Analyst, Business Intelligence (BI) Analyst and BI Manager.
All companies collect data voluntarily or not. Through their commercial, marketing and operational activities, they collect a volume of data every day proportional to the intensity of their activity: CRM, connected objects, social networks, search engines, service by the user, etc.
For the last twenty years and with the exponential growth of technologies, they have been learning how to use them, internally to improve their product/service, to support decision making, to automate through AI, to measure, etc., or externally by reselling them to other companies.
Now that everyone has understood the importance of data, it is no longer a question of wasting a single drop. It is the new wealth of the 21st century: while oil reserves are running out, data reserves are filling up ... and they have no limits.
The objective is to exploit and enhance the massive data collected and processed by the Data Engineer need a highly qualified technician: the Data Analyst.
The Data Analyst will be recruited by all types of companies wishing to rely on the collection of processed data to support their strategic, marketing and business decisions.
Wherever there is data, he can provide advice based on data that a human cannot translate. His work allows companies and managers to make decisions based on their data.
More concretely, its value translates into the translation of data into reports, dashboards and visualization tools to present its results and make them understandable to all.
He works in all types of companies in different sectors of activity with a large volume of data.
In today's digital age, companies receive a phenomenal amount of information on a daily basis to help them optimise their strategies. To exploit the massive data collected, they need a highly qualified technician: the Data Analyst.
The latter's task is to process the various data concerning customers, products or company performance in order to identify useful indicators for decision-makers.
Thus, the information provided by the Data Analyst allows companies to define the products to offer to customers according to their needs, the marketing strategy to adopt or the improvements to be made to the production process.
The Data Analyst is an expert in statistics and analytics. His role is to make data talk. His role is to manipulate "clean" data to observe and understand phenomena and behaviors.
These data can be from internal sources (CRM, DB) which will have been previously prepared by the Data Engineer, but can also come from external sources (web, data providers).
The Data Analyst then produces reports, dashboards and visualisation tools to present the results and make them understandable to everyone. Their work enables the company and its managers to make decisions.
In short, tables, graphs and balance sheets are his daily routine!
In a small tech team, he may be responsible for a data division, confusing the jobs of Data Engineer and Data Analyst. He or she will thus have control over the entire data development cycle without being able to go deeply into the subject.
This requires a horizontal knowledge of data issues without being able to develop a vertical expertise.
In a large tech team, he works under the responsibility of a Data Manager (Head of Data, CDO, Lead Data Manager), in collaboration with the Data Engineer who provides him with a volume of "clean" data ready to be used.
The Data Scientist who can work on the same decision making issues but with a different output. The Data Analyst will develop visual tools (dashboard) and reporting, while the Data Scientist will implement predictive models.
Outside of the data division, he/she may also be attached to the management of a business team in order to work on their problems in direct contact with the teams (very often marketing, but also business, financial or HR depending on the company's orientation)
Here is his daily work:
For example: an operative asks the Data Analyst "Can you give me the number of VTC runs there were yesterday in such and such an area?"
For example: Compare user behavior: what behavior does the one who comes from google search and the one who comes from this ad we are financing adopt?
Another example: Specific to an in-depth study: what contribution did this partner make? Was it a good decision to set up a partnership? What changes have occurred since the partnership was set up?
They also use reporting and data visualization tools such as:
It will use the following languages and framework:
Technically, he/she masters languages such as R, Python or SQL which are used for database manipulation. A good command of tools for data visualisation is essential, such as Plotly, Tableau, Dataiku or Qlik Sense Qlik View and of course Excel.
Analytical skills are essential to interpret and produce results. He/she is as comfortable with querying and data mining as he/she is with summarising results.
In addition to mathematical and statistical skills, the Data Analyst is able to popularise the technique to present and explain results to non-technical teams.
The Data Analyst has a strong analytical and synthesis mind. He/she has an eye for detail and knows how to keep a critical eye on the results in order to extract the key information hidden in the mass of data.
Good communication skills are also important for this profile as he/she will have to present his/her results to managers and teams who are not familiar with data.
It is necessary for a Data Analyst to have solid skills and a strong appetite for statistics, probability and mathematics applied to computer science.
Here are the salaries considered according to your experience:
This profession allows you to pursue a career as: Lead Data Analyst, Data Manager, Data Scientist, Big Data Expert.
December 8, 2020