data scientist and data analyst in fashion and retail

Know more about difference between data analyst and data scientist

data scientist and data analyst in fashion and retail

How you can differentiate between a data analyst and data scientist

The world is looking at the extensive use of data generated in huge quantities by electronic devices.

The fashion industry, too, has evolved in the last decade and has seen increased use of data to extract insights that improve operational efficiency and effective decision making.

Moreover, increasing automation, IoT, and robotics in the industry necessitate artificial intelligence to solve people, processes, and technology problems.

Some consider data analysis and data science as tools of the present and future. However, some still believe these as buzzwords.

In this article, FashionLiteracy dives deeper to explain more on data scientist vs data analyst, and explains the difference between these two work roles concerning fashion and retail.

With the increase of deep learning methods to pursue AI’s objectives, it has become all the more important to understand these differences.

Data Analyst In Fashion And Retail

A data analyst is responsible for bringing insights from data by analyzing it in the context of the questions already asked by business stakeholders. The focus of the data analyst stays on improving the performance of a business.

Therefore, data patterns discovered by a data analyst enable business stakeholders to make informed decisions for an organization’s better performance concerning revenue generation or operational efficiency.

Some of the activities performed by a fashion data analyst are:-

  1. Collect Data from various sources
  2. Cleanse data in the context of a business problem
  3. Create or Select an appropriate data model
  4. Convert complex data into clear and straightforward stories
  5. Perform analytics for customer segmentation and personalization

If one wants to be a data analyst in fashion and retail, she/he needs to get proficient in the following skills/tools.

  1. Structured Query Language (SQL) to query databases and data sources.
  2. Microsoft Excel or other spreadsheet software
  3. R or Python programming language
  4. Data Visualisation skills
  5. Presentation skills using PowerPoint presentations or other software

Machine Learning techniques include regression, classification, clustering, dimensionality reduction, neural networks, transfer learning, reinforcement learning, etc.

The above-given list of skills helps a fashion data analyst assess a brand’s performance based on data captured from various digital channels such as social media, websites, web analytics platforms, etc.

Customer behavior and engagement are analyzed based on what they like, what they wear, what they say, etc. to improve products and processes to find a winning ground between data-driven decisions and human experience.

Data Scientist In Fashion And Retail

Fashion and retail have been relatively slow in adopting new technologies. Because of this slow response to the latest and upcoming technologies, brands have the risk of losing in favor of the latest and agile startups, who focus on being proactive to the disruption caused by such new trends.

Contrary to a data analyst, a data scientist focuses on bringing the power of prediction based on captured data analysis. He/she studies the data to get insights into a brand’s performance before it releases a new collection.

Some of the activities performed by a data scientist in the fashion industry are:

  1. Collect customer data from social media and other sources
  2. Treat data for inaccuracies, outliers, and missing values.
  3. Create or Design Machine learning models
  4. Use big data analytics and big data streaming tools.
  5. Create a proof of concepts and simulations of new business models

If one wants to be a data scientist in the fashion industry, then they need to have the following skills/tools:-

  1. Big data processing tools such as Apache Hadoop, Apache Spark, Apache storm
  2. SQL and NoSQL based data querying languages
  3. RDBMSs and document-based databases
  4. Programming languages such as R and Python
  5. Predictive analytics, Visual Search, Natural Language Processing
  6. Machine learning with Deep learning skills to train and use neural network models.

Using data science has become essential these days for the brands to leverage the power of technology to retain existing customers and acquire new customers.

Data science also helps predict a design collection’s performance before its release and make necessary modifications if required based on insights acquired from data captured from various customer engagement digital channels.

Brands can determine how changes to fabric, design details, price, color, and other parameters can affect a customer’s response and prevent a new product, such as a garment or an accessory, from failing before release.

Data Analyst vs Data Scientist

In this data analyst vs data scientist article, below given slides presents a more discrete comparison between these work roles.


The competitive nature of the fashion world and the advent of new technologies have propelled today’s brands to stay relevant in these changing times by using data analysis and data science.

Traditional closed-book methods of retail data analysis are the thing of the past. A lot of important information related to customer data, pricing, online user visits, choice of clothes, choice of design, social media engagements, etc. is being used by contemporary brands to drive value and lead data driven decisions with human centric goals in mind.