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How to Become a Data Analyst

What Does a Data Analyst Do?

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Data analysts collect, organize, and interpret data and information to create actionable insights for companies. To accomplish this, Data Analysts must collect large amounts of data, sift through it, and assemble key sets of data based on the organization’s desired metrics or goals. Analysts then often transform those key datasets into dashboards for different departments within the organization, presenting their insights in ways that can be used to inform activities and decision-making.

Data Analysts work in everything from political campaign management and finance to mining and epidemiology. But to give an example: imagine a corporate website that uses content marketing for lead generation. Tracking the conversion rates of visitors into customers yields data that lets a Digital Marketer follow a potential customer from their arrival at a blog post or other landing page all the way through to their signing up for a newsletter or even purchasing a product. Seeing what happens at each step helps the Marketer understand what content is working, why it’s working, and hopefully expand on that success.

What Does a Data Analyst Actually Do?

Data Analysts’ specific tasks vary wildly from industry to industry, company to company. Generally speaking, though, as a Data Analyst, you can expect to perform some or all of the following tasks and responsibilities:

  • Researching your company and your industry to identify opportunities for growth, vulnerabilities, and areas for improved efficiency and productivity.

  • Data requirement gathering, beginning with determining what you hope to accomplish, and arriving at a clear sense of what information you need and how to measure it.

  • Data collection, either from existing sources, or by developing new channels for obtaining the information you need—while making sure the data is in a usable form.

  • Data cleaning, including reformatting data for consistency, removing duplicate entries and null sets, and so on. In very large datasets, this task is too onerous to complete by hand, and requires the use of purpose-built tools and software.

  • Creating and applying algorithms to run automation tools, the better to understand, interpret, and reach solid conclusions about what the data shows.

  • Modeling and analyzing data to identify significant patterns and trends and interpret their meaning.

  • Presenting your findings to other members of the organization, digested and packaged in a way they can easily grasp. This can include creating visualizations or dashboards for other members of the organization to refer to.

This diverse range of actions can be generalized by four fundamental categories: understanding the data, analyzing the data, building and managing databases, and communicating the data to others.

In the most recent BrainStation Digital Skills Survey, most Data Analyst respondents said they spend the largest amount of time wrangling raw data and cleaning it up. The primary use for this data? Optimizing existing platforms and products, as well as the development of new ideas, products, and services.

When BrainStation further correlated these responses to major job titles, an interesting discrepancy between Data Analysts and Data Scientists emerged: the majority of Business Analyst and Data Analyst respondents indicated that they tend to focus more on the former (optimizing existing platforms and products). Data Scientists, on the other hand, hew primarily toward the development of new ideas, products, and services, where strategic planning comes to the fore—possibly a result of differences in experience, knowledge levels, or degree of specialization.

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