Data analytics has grown well beyond tech companies in recent years and become a foundation for businesses to make strategic decisions in nearly every industry. And as companies depend more on data analytics, the importance of the data analyst has only increased.
Depending on their role, seniority, and the company’s needs, a “typical” data analyst ends up doing anything from writing algorithms to explaining the importance of a finding to the C-suite. In addition, many of these roles are highly specialized; a data analyst who works in healthcare, for example, will need to rely on a totally different knowledge base than a colleague who works in finance.
Given the increasing demand for the role, plus the relative infancy of concepts like Big Data, it's not always easy to find a data analyst with the relevant credentials and level of experience you need. But with the right strategy, you can better assess standout hires with the right skill sets.
Typical Data Analyst Job Posting
An effective data analyst job posting will emphasize the ideal candidate’s mix of technical skills, analytical abilities, and soft skills (such as communication). Main areas of responsibility might include:
- Collaborate with managers to formulate requirements for data.
- Work with end users to determine data/reporting requirements.
- Define database quality and address any data quality issues.
- Develop appropriate documentation.
- Establish strong communication with other teams.
- Support data inquiries and questions from the broader organization.
Depending on the position itself, the exact technical qualifications will vary (“strong knowledge of relevant tools and platforms” and “strong analytical skills” are two bullet-points that pop up again and again in postings, but those are broad; specific companies should seek professionals who can work with their specific tech stacks). On the “soft skills” front, however, all companies should generally expect a data analyst to do the following:
- Demonstrate well-rounded abilities; technical and communications skills must be equally strong.
- Display a strong initiative (i.e., self-management).
- Act as a team player with both technical and non-technical colleagues.
- Have knowledge of relevant business and technical terms.
Data analysts must understand how to use various types of data-analytics software, including:
- RapidMiner (a data-science platform used by many companies)
- Knime (an analytics platform)
- Datawrapper (an online tool for creating charts and visualizations)
- Tableau (another visualization tool)
- Postgresql (an open-source relational database management system)
- Google Fusion Tables
- SAS Sentiment Analysis
- Apache Hadoop
That’s in addition to knowing R and Python, two programming languages that are driving data analytics at the moment (although R is more of a language for academics and research projects; within many organizations, Python’s ubiquity is making it the language of choice for commercial endeavors). Knowledge of SQL, which is used to manage data within relational database management systems (RDBMS) is also essential for work at many companies.
There are also a few certifications that data analysts can obtain, including:
- CCA Data Analyst
- SAS Certified Data Scientist
- Data Science Council of America Certification
- Microsoft MCSE: Data Management and Analytics
Interviewing Data Analysts
When interviewing a data analyst candidate, you’ll want to make a few determinations: Does the candidate have the necessary hard skills for the position? Are they capable of gleaning actionable insights from whatever data sets they analyze? Can they effectively communicate crucial trends and important findings to other stakeholders within the business?
In addition, try to assess the candidates on the following:
- How well they communicate with stakeholders.
- Their skill with various types of data analytics software.
- Their approach to data-analytics projects.
- How they handle pressure (complete with examples).
- What they like about data analytics.
As with any interview, getting the candidate to provide relevant examples from their experience is key to getting the best understanding of their ability and work style. In addition, it may be worth your team’s while to give the data analyst candidate a few problem sets to solve. These tests can be take-home or in-office, and should address a practical issue (for example, ask the candidate to demonstrate the best practices for cleaning a data set).
Closing the Deal
Most data analyst candidates will know their skills are in high demand. This means your recruiting process requires a much more purposeful approach than making an offer and hoping they accept. These candidates want a clear picture of what will be expected of the position, what the road map looks like and how they can continue to grow their career and skill set. Delivering information on these aspects of the position will give a candidate a much better picture and is bound to pique their interest.