What is a Data Analyst and Types of Data Analytics? A key role for data-driven business decisions:
Data analysts analyze a wide range of data to help companies identify the current state of the business.
What is a data analyst?
Analytics combines theory and practice to find and communicate data-driven insights that enable managers, stakeholders, and other executives to make more informed decisions in a company. Experienced data analysts analyze their work in the context of their organization as well as other external circumstances. Analysts can also account for the competitive climate, internal and external company interests, and the absence of specific data sets in data-driven recommendations to stakeholders.
Four types of data analytics build on each other to bring increasing value to an organization.
Descriptive analytics - examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, etc. These types of findings allow an organization to spot trends.
Diagnostic analytics - considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.
Predictive analytics - seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example.
Prescriptive analytics - attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.
In a recent survey of more than 2,000 business executives, the consultancy PwC found that organizations find descriptive analytics to be insufficient for informed, data-driven decision-making. As such, diagnostic and predictive analytics are increasingly important to organizations.
Most Valuable Skills for Data Analysts:
Effective data analysts have a mix of technical and people skills.
Knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets; and data visualization software such as Tableau or Qlik is required. Mathematical and statistical skills are also useful for gathering, measuring, organizing, and analyzing data.
A data analyst's leadership abilities prepare him or her to perform decision-making and problem-solving responsibilities. These skills enable analysts to think strategically about the information that will assist stakeholders in making data-driven business decisions and to successfully communicate the value of this information. Project managers, for example, rely on data analysts to track the most essential metrics for their projects, detect potential difficulties, and estimate how different courses of action can address a problem.