Who is a Data Analyst?

Data Analyst
Data Analyst

Data Analyst is a technical role responsible for handling and analyzing an organization’s data so that it can be strategically used for efficient business decisions. It requires the Data Analyst to understand data and its complexities to process it and perform statistical analysis over it.

What are the key responsibilities of a Data Analyst?

  • Collaborating– Data Analyst has to work in implicit collaboration with other members of the organization in collective analysis of the trends
  • Creating reports – Data Analyst has to generate optimized reports using relevant data to understand trends and patterns to make predictions.
  • Re-organize the data– Once important data has been recognized, it needs to be re-organized in an efficient manner so that it can be read and used by anyone.
  • Know appropriate tools– Data analysts are required to know statistical tools to interpret data sets, recognizing trends and patterns that may hold futuristic value for diagnosis and prediction.
  • Setting up Infrastructure– Streamlining the data in a correct fashion is very important for every Data Analyst and hence it is important to set up and maintain a system that works smoothly over the set of data of the organization. 
  • Recognizing important data– Identify the data in a manner that can differentiate important primary data from secondary data.
  • Handle Data in an error-free manner– Maintain database efficiently. Fix any coding errors or data system-related errors.
  • Creating appropriate documents that allow stakeholders to understand the criticality of the data analysis and use it in the future.
  • Inlining with programmers and organizational leaders to recognize and develop strategies for process improvements, recommend system modifications, and develop policies for data governance.

What are the Types of Data Analytics?

Data Analytics can be primarily distinguished into four types: 

  1. Descriptive analytics focuses on what happened in the past: It works on collected data from the past for example – The revenue of an organization, quarterly sales of a company, load patterns on a website. Descriptive Analysis thus helps in understanding the trends and patterns so that data can be used strategically in the future based on this analysis.
  2. Diagnostic analytics focuses on why something happened i.e. what condition led to the occurrence of it. This majorly helps an organization to know the positive and negative outcomes and thus can use prediction to know the outcome of their strategies beforehand.
  3. Predictive analytics straightforwardly yields the predictive results by comparing the data analysis by detecting patterns in descriptive and diagnostic analyses. This allows an organization to create efficient strategies for actions to be taken beforehand. For example, Launching a scheme of discounts to lure customers near festivals as per past records of heavy sales during that time of the year.
  4. Prescriptive analytics comes in use when an organization needs to decide what business action should be taken. This analysis is significant in providing emphasis to handle potential problems and has an edge over industrial trends. This type of analysis requires the use of complex algorithms and deep knowledge of programming language and technology. One such example is machine learning.

In a 2016 survey with many a thousand business executives, it was derived that among all the four analyses, descriptive analytics was found to be insufficient for complex and data-driven decision making. Rather diagnostic and predictive analytics are majorly important to organizations. And many organizations have just begun to start working on Prescriptive Analytics and still many are struggling over it.

How to embark on success as a Data Analyst? 

A successful data analyst should hold a good set of technical skills and leadership skills both since he will be responsible to drive the organization’s strategy and prediction making in a critical manner. Technical skills will encapsulate knowledge of Database Languages – SQL, R, or Python; Spreadsheet tools – Microsoft Excel or Google Sheets; Data Visualization Software such as Tableau or Qlik. A good Data analyst should also possess Mathematical and Statistical skills since there is such a large amount of data and these skills help in relevant gathering, accurate measuring, efficient organizing, and critically analyzing the data. Leadership skills are as important for data analysts too. Data Analysts have to, time and again indulge in decision-making and problem-solving tasks. Only good leadership skills allow the analysts to think in the correct direction with the right strategies as to which all information is needed to be analyzed. Because only relevant and useful information can contribute to helping the stakeholders to make correct data-driven business decisions which can further be communicated and transformed into making predictions and strategies with respect to the value of this information. For example, every organizations’ project managers work on the information and metrics provided by the data analysts to track and diagnose the problems that the organization may be facing or might face in the coming future. Based on these predictions only, an organization decided the actions that are to be taken to address a problem.

You can know more about Data Analyst by referring to the uCertify course of CIW Data Analyst Specialist (1D0-622). It is created for the purpose of enhancing the skills of IT and marketing professionals, graphic artists, and website designers. The course provides quizzes, performance labs, video lessons, and many more to provide you a hands-on experience. It covers topics such as introduction to Big Data, working with the Data Sources, tools for capturing and analyzing data, analyzing and reporting data, and many more. Make sure to look for this amazing course!

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