What Does A Data Analyst Do?

What Does A Data Analyst Do?

If there’s one special role that recurrently gets lumped into the wad of professionals associated with product management, it would be data analysts. 

For years now, the natural complement to the discipline and skill of solid product management has been the work offered by data analysts.

This is a notion that has filtered down to even the smallest of startups. Businesses recognize that the foundation of successful products and services hinges upon someone who can offer superior data analysis. 

That said, despite the simplicity and seemingly self-explanatory nature of their job title, surprisingly, data analyst responsibilities remain largely elusive. 

While they vary from business-to-business, through speaking to hundreds of companies over the years, it’s clear that there isn’t a clear consensus among industry heads as to what a data analyst really does. 

The resulting inconsistencies can set businesses up for false expectations from recently-hired data analysts, among other things. 

Generally speaking, the main challenge with defining what data analysts do is that their role in and of itself has acquired a fluid definition over time. 

Data Analyst: Executive Summary

In an effort to offer some closure on what a normal day in the life of someone taking up a data analyst job looks like, we’ve decided to break up the following content like this: 

  • General overview of what the role of a data analyst is within a business
  • Data analysts & ‘The Analysis Process’
  • Breakdown of the types of data analysis
  • General responsibilities associated with data analysts
  • Typical skills needed to become a great data analyst

We’ll end with a brief presentation of the usual requirements and demands expressed in data analyst job descriptions.

Let’s dive right into it. 

What Does A Data Analyst Do?

In a nutshell, a data analyst is responsible for the retrieval, collection, organization, and review of data in order to draw conclusions from it. 

In short, they find data related to a particular subject and scrutinize it in order to read between the lines and understand processes. 

That said, while generally speaking data analysts share similar profiles descriptions, the specific responsibilities naturally vary not only based on the specific company that the data analyst is working with, but also the type of data being analyzed.

Before delving more into the intricacies related to the tasks fulfilled by different data analysts, it must be said that companies across industries and niches can benefit greatly from these types of professionals. 

Health care companies, retail stores, small fashion businesses, clothing retailers, textile companies, and social media influencers: there’s a place for a data analyst to help a business in any and every niche to grow.

The insight that they provide is instrumental to company heads in terms of market research and product refinement. 

While responsibilities will differ, what is true across the board is that professionals in charge of data analytics are key players who influence business decisions and play a fundamental role in problem-solving mechanisms related to both general and specific business operations.

The Data Analysis Process

In order to understand the responsibilities that data analysts have, it’s important to state one 

thing: data analysis is a process that is not for the weak or faint-hearted.

It is a rigorous one that consists of compiling complex data sets with the objective of analyzing and testing them based on set criteria.


To explain, here’s a breakdown of what the process entails: 

  1. Definition of questions: In order to begin the data analysis process, it is imperative that the data analyst be aware of what they are investigating. Otherwise, how will they get results if they don’t know what to look for, right? Therefore, the foundation must be laid by qualifying and disqualifying questions that will condition the scope of the research and, in turn, predict the type of conclusions that can be drawn. 
  2. Select measurement plans: Having identified the items to be investigated, the data analyst must then proceed to determine the metrics that will provide them with the data required to arrive at the conclusions they need. In addition to that, a data analyst's responsibilities also encompass setting up a method regarding how the data collected from select metrics will be analyzed. 
  3. Data collection: With the foundation laid, the data analyst proceeds to collect and record data obtained from the sources established previously. In this stage, it is vital that the analyst implements a storage mechanism that facilitates easy access and identification of data that is collected. 
  4. Data analysis: This is where the bulk of a data analyst’s technical skills, analytical skills, and prowess come into play. Here, the data is systematically manipulated and revised in order to identify trends, correlations, patterns, inconsistencies, and anomalies. 
  5. Data interpretation: Once the analysis has been done, the analyst proceeds to draw conclusions based on their data-driven findings. Once those conclusions have been stated, the result is compiled into deliverables that are presented to the interested parties.

While the process is five-pronged, a data analyst may not necessarily be responsible for all five stages. For example, one may be presented with the first two steps already completed. This tends to happen when data analysts are onboarded when data analysis procedures are already put in place.

Types of Data Analysis

In addition to the various segments within the data analysis process, a data analyst’s responsibilities can also be conditioned by the type of data that they are analyzing. 

In much the same way that there are different types of questions that can limit the scope of an investigation, so too can the data analysis itself be limited based on a particular method:

Credits: Science Soft

  1. Descriptive analytics: This type of analysis seeks to identify what were the elements that led to a particular occurrence. In doing so, it is strictly a presentation of facts that happened that condition a business’ current performance. 
  2. Diagnostic analytics: Unlike descriptive analytics, diagnostic analytics actively pursues the underlying causes that created a particular event. In other words, it doesn’t seek to describe what took place; rather why it did. In business terms, that implies getting to the factors which caused your sales to increase or drop, or even why a subscription-based business had more registrations in May than in April, for example. 
  3. Predictive analytics: Predictive analytics centers around creating estimations about what may happen in the future. To do this, analysts examine data to determine trends and tendencies. They then use this information to make forecasts that coincide with what has happened previously.
  4. Prescriptive analytics: Different from predictive analytics, prescriptive analytics is all about figuring out what to do to solve a problem or take advantage of a positive trend. A prime example of this would be data analysts who are charged with examining patterns in order to identify possible products that a company can launch in the future in order to capitalize on market research and customer pain points.

Data Analysts: Common Responsibilities

As mentioned previously, identifying specific responsibilities for a data analyst is nearly an impossible feat. However, there are some commonalities that most people who work in data analysis share: 

  1. Report production: One standard practice which data analysts tend to do is report generation. These reports form the basis of the conclusions drawn from the data collected and analyzed during the data analysis process. These reports contain the insight that management needs to make informed decisions that’ll benefit their business.  That said, report production isn’t a matter of putting together a series of baseless numbers. Instead, data analysts are tasked with the responsibility of stringing together conclusions with each one's correspondent data samples in order to create a cohesive narrative that answers the questions established at the beginning of the data analysis process.
  2. Pattern recognition: A chief responsibility of most data analysts is the need to recognize patterns, trends, and tendencies that are present in data. This is important as it forms the cornerstone of the conclusions that will be included in the reports that they deliver. Consequently, it is almost incumbent upon the person filling a data analyst role to identify and web together trends that may pop up from data analysis. 
  3. Data collection and administration: Needless to say, a huge chunk of a typical data analyst’s responsibilities center around recording data that they collect. In doing so, this tends to constitute the most technically challenging skill as it involves leveraging tools that facilitate data collection and storage. 
  4. Inter and cross-departmental collaborations: While data analysts are usually thought to be lone wolves, the reality is that most of them need to liaise with other departments within a company. In fact, these professionals tend to work closely alongside database developers, data architects, and marketers. 

Data Analyst vs Data Scientist vs Business Analyst

Part of the confusion surrounding what a data analyst does lays in the notion that their responsibilities are identical to that of a data scientist or business analyst.

While all three profiles rely heavily on data to complete their duties, there is a fundamental difference that separates all three: it centers on how each one uses data. 

  • A data analyst is responsible for presenting complex company data in a simple format that is understandable to stakeholders and interested parties. Using this data, stakeholders can make strategic decisions that will impact the future of their business.
  • A business analyst’s position is less technical and more strategic. Using company data, they identify problems and propose solutions in much the same way that a stakeholder would. 
  • Lastly, a data scientist is one who borrows a bit of both of the previous roles mentioned. One who works in data science sifts through the information provided by the data analyst and, in turn, uncovers possible opportunities that the company can capitalize on. Conversely, they may also identify weaknesses that a company needs to address.

What Skills Does A Data Analyst Need?

In light of a data analyst’s responsibilities, it comes as no surprise that anyone looking to accomplish their duties well requires a specific background.

That is true: but only to some degree.

It is true that data analysts need to have a combination of technical, analytical, and leadership skills. 

Technical skills would cover the knowledge and ability to dexterously use common database languages in data analysis such as Python, SQL, and R. 

Abilities such as being able to dissect a particular data series in a coherent and objective manner while being disciplined in adhering to delivery dates would fall under both analytical and leadership skills.

That said, data analysts do not need to have a Bachelor’s degree in Big Data or Computer Science.

In fact, data analysts come from a diverse background within the academic sphere. 

Usually, since most Master’s, MBA, and Doctoral programs require investigative methodologies, it’s not uncommon for businesses to select a candidate who has a Doctorate in Philosophy over someone who has a Bachelor’s in Computer Science. 

The reality is that the person with the Doctorate is probably more adept at data analysis than the person with a Bachelor’s degree, irrespective of their field of study.

Common Demands in Data Analyst Job Descriptions

Despite the inconsistencies in job descriptions across the data analysis industry, there are a few demands which businesses require from data analysts in general: 

  • A university degree: As mentioned before, when scouting for data analysts, businesses usually look for candidates who have completed at least a Bachelor’s degree. Where a candidate hasn’t completed a Master's degree, a degree in Statistics or Mathematics is preferred. 
  • Experience with programming languages like Oracle, Python, and SQL
  • A high mathematical and computational ability
  • Ability to collect, study, and interpret data
  • Excellent task management skills
  • A systematic approach to data analysis
  • Experience using Microsoft Excel, Google Sheets, and other Business Intelligence tools
  • Proven experience in research and investigation (academic or professional)
  • Accuracy in data interpretation and process improvement
  • Knowledge of machine learning software and data systems
  • Ability to adhere to deadlines
  • Willingness to work in a team
  • Quick decision-making abilities
  • Stellar communication skills

Naturally, the requirements for an entry-level data analyst would not be as stringent as those for a high demand senior data analyst.

Data Analysts: The Recap

As you’ve guessed, data analytics is a broad area.

Consequently, those who work in it have big shoes to fill. 

In order to be successful, data analysts must bring their best analytics, technical, and leadership skills game to work. 

Otherwise, the slightest inaccuracy may lead to an incorrect conclusion that thwarts a business’ strategy.

Needless to say, data analysts have a lot riding on their shoulders.

That said, the role is exciting and fun for people who enjoy mind games.

Just like chess is a mind sport, so too can data analysis give the mind a thorough workout.

So, for those persons who enjoy mind puzzles, data analysis can never seem like a chore because they’ll always feel right in their comfort zone.


Published in Career Path, Career Resources

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