Organizations across domains rely on data to make critical business decisions revolving around which new products to develop, investments to make, markets to enter, and so on. They also use data to identify and rectify workflow inefficiencies and other business problems that hamper growth.

Furthermore, the insights from data analytics have added value to multiple domains – from retail and eCommerce to healthcare and travel. An IBM report estimates that there will be more than 2.7mn jobs for professionals with data skills by 2020.

Out of that, 59% of all data science and analytics jobs will be in IT, professional services, and finance sectors. The study further reports that 39% of advanced data analyst positions require a Master’s degree or Ph.D. This spikes the salaries for professionals possessing such qualifications.

No wonder it has become such a  sought after profession. So, the next question that pops in our head is – what does a data analyst do? According to Porter Novelli’s analyst, Stephanie Pham, the work of a data analyst depends on the type of data they work with, i.e., social media, inventory, or sales.

But since that’s too vague, let us dig into the specifics of the responsibilities of a data analyst:

1. Collection of data

The most important component of a data analyst’s job is perhaps collecting the data. That mostly requires them to collaborate with web developers for optimizing data collection. Data analysts need to streamline the process to eliminate errors that could later cause any errors in their findings.

Therefore, data analysts work towards setting up an automated process that can be modified for reuse in other aspects of their job. To collect data properly, they use tools such as Microsoft SQL and Oracle Database.

2. Production of reports

Data analysts invest a considerable amount of time in the production and maintenance of client-facing reports. Such documentation gives the management insights into the new trends that the organization can look into and the areas in which they can make improvements.

However, creating reports is not as simple as throwing numbers onto a blank page. They must demonstrate the significance of their work in a manner that is easy for any person who is not a data analyst to understand and take decisions.

3. Collaboration with external teams

Data analysts may work as an individual contributor at an organization. However, they are still required to work with teams across departments including engineers, web developers, programmers, data scientists, and the top management for data collection.

Their role requires them to collaborate and identify opportunities for improvements, develop policies for data regulation, and recommend system modifications. Excellent communication skills and the openness to work with others are a must.

Despite an IC role, data analysts have to collaborate to gather and execute the data and deliver insights to the management.

4. Spotting patterns

To weave a narrative, a data analyst identifies essential patterns in the data. That forms the base for fixing a trend that they use for making suggestions or giving insights to their management or clients.

Data analysts need to report periodically. That could be on a quarterly, monthly, or weekly basis. That’s because regular reporting helps in analyzing significant patterns that could get missed if tabs are not kept on them.

5. Maintenance of databases

A data analyst designs databases by mining information from primary and secondary resources. They reorganize the data in such a format that both machines and people can easily understand it without a data analytics background.

Additionally, a data analyst also maintains those data systems. That job includes identifying and fixing coding errors and other related problems. Further, the data analyst creates documentation stating the steps of the mining process that can be duplicated if necessary.

Tools used by data analysts

Data analysts rely on multiple tools to retrieve data, set up infrastructures, and create insightful reports. Depending upon the industry, the data could be extracted from several sources. That includes the organization’s old drives, industry reports, news sites, case studies, and more.

To perform their job well, they use the following tools:

  • Oracle Database
  • Python/R
  • Microsoft SQL
  • Apache Spark
  • SAS software
  • RapidMiner
  • KNIME
  • Google Analytics
  • QlikView
  • Tableau

Not just that, such tools are also helpful in interpreting data, analyzing trends and patterns, and making reports that could be valuable from a predictive analysis perspective.

Conclusion

Data collected without any reason or examination is a complete waste. A data analyst, with their technical prowess, adds value and offers insights to the organization that can help them make better decisions.

So, should you become a data analyst?

A 2018 report by Analytics India magazine highlighted that the analytics industry in the country has already grown to $3.03bn in size this year. The number is expected to double by 2025. This clearly shows that there is a massive scope of building a successful career as a data analyst.

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