Generally, the Data Analyst works with information matrices,
executing a whole set of functions independently: data collection; preparing
data for analysis (sampling, cleaning, classification); finding patterns in
information sets; data visualization to quickly understand current results and
future trends; formulate hypotheses to improve certain business metrics by
modifying other indicators.
These tasks are necessary to achieve the main objective marketingmediaweb of
the data analyst: to extract valuable information so that the company can make
optimal management decisions.
The data analyst is also in charge, in some companies, of
data modeling, that is, of developing and testing machine learning models (
Machine Learning ). However, machine learning is, in the vast majority of
cases, the responsibility of a researcher or data scientist. In a more detailed
division of work, machine learning is the responsibility of an independent
specialist.
Also, note that the data analyst sometimes examines business
processes and works closely with other IT professionals in describing the flow
and storage of corporate information. Therefore, the data analyst's area of
responsibility also includes divinebeautytips Business Intelligence (BI) and production process
optimization tasks .
Competencies of a Data Analyst
Based on the set of tasks previously described, it is
possible to identify the following areas of knowledge required for a data
analyst:
• Information technology with methods and nanobiztech tools for data
mining (Data Mining), programming languages (R, Python, etc.) and SQL-like
languages to write queries to non-relational and relational databases, as well
as systems of BI, ETL storage, and data marts like Tableau, Power BI, QlikView,
etc., as well as the basics of the Apache Hadoop infrastructure.
• Mathematics such as statistics, probability theory, and
discrete mathematics.
• Systems analysis , quality management, project techcrunchblog management,
and business process analysis methods (Lean Manufacturing Approaches, SWOT,
PDCA, IDEF, EPC, BPMN, MTP, etc.).
On the other hand, applied knowledge and practical
experience specific to the subject area in which the data analyst works will be
very useful. For example, the fundamentals of accounting will be useful for a
data analyst in a bank, and marketing methods will help analyze information
about customer needs or assess new markets.
Data analyst functions
The functions of the data analyst lie at the confluence globalmarketingbusiness of
mathematics, programming, and product management. As a result of your work, a
company can generate more income and make consumers feel more satisfied. The
duties of a data analyst may vary by workplace and skill level.
Typically, this specialist runs statistical tests and solves
business problems for which there is still no answer. Then he makes forecasts,
strategies, plans and recommendations.
The usual activity of a data analyst is:
• Establish communication with company representatives and
identify problem areas within the company.
• It collects information so that the activities of the
company can be tracked.
• Formulate hypotheses to improve some parameters.
• Prepare data for analysis: classify, filter and take
samples.
• Identify patterns.
• Visualize the data: translate statistics and Big Data into
visual graphs and conclusions.
• Suggest solutions that are used for project or business
development.
Based on the data provided by the data analyst, the company
can make any business decision.
Personal qualities of a data analyst
A good data analyst is not limited to metrics and reports.
Regardless of the profile, a great professional must have the agile skills they
need to be productive:
The thinking and logic of systems . In this sense, it is
necessary to be able to analyze, synthesize, compare and draw conclusions from
patterns that are sometimes not obvious. The analyst must understand the
assumptions on which his judgments are based and verify that they are correct.
Attention to detail, methodology and rational skepticism .
The analyzes must be verified, reviewed and justified. It is better to clarify
vague details and challenge even the most authoritative opinion than to launch
an unnecessary product.
