The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing an analyst does for business purposes is called Data Analysis.
Determining the most profitable directions, identifying the sources of hidden costs, assessing the effectiveness of marketing strategies, clarifying the portrait of the target audience, drawing up a plan to increase conversion – this is not a complete list of typical tasks of a data analyst.
A complete picture of historical data (cost statistics, sales dynamics, target audience growth, the influence of seasonality and political environment on business, as well as other external and internal factors) contributes to a systemic vision and identification of new opportunities for growth and development. Therefore, it can be said without exaggeration that data analytics is needed today by every leader of any company.
Since the volume of analyzed data in a large company is even larger, and the IT infrastructure is richer, it will need Data Professionals with specialized divisions:
Data Architect is responsible for designing a new system for collecting, storing, and processing data, including the features of all current and future data sources and models, their integration and presentation processes, as well as technical means of implementation;
Data Analyst builds hypotheses and extracts useful information for business from “raw” data arrays, clearing them of incorrect values and outliers, as well as selecting the variables necessary for modeling – machine learning;
An ETL specialist works with dashboards and structured repositories (Extract – Transform – Load, ETL), creating analytical reports.
Data Engineer creates and maintains the infrastructure of the Big Data project, providing collection, storage, and management of data streams in real-time;
ML-specialist develops models and machine learning algorithms, and is also responsible for their implementation in software applications;
Data Scientist is engaged in the analysis of information, and also develops models and machine learning algorithms that test or refute the hypotheses of analysts;
The Chief Data Officer (CDO) manages the data lifecycle so that every corporate client (user, information system, or cloud service) receives the necessary information in the right form and of acceptable quality in time. CDO also oversees the work of all Data Professionals: architect, analyst, engineer, and researcher.