Difference Between a Data Analyst and Data Scientist
Difference Between a Data Analyst and Data Scientist
In an article published in Forbes, Jedidiah Yueh and Randy Bean said that “every company is a data company.” According to them, companies that are fast at using data are basically eating those that don’t. The differentiator is clear: data.
Faced with the increased demand for data usage, companies hire both data analysts and data scientists in increasing amounts.
But, what are the differences between both roles?
“We’re entering a new world in which data may be more important than software.”
Tim O’Reilly
Although there is a grey area in the meaning of data analyst vs. data scientist, the key aspect that separates them is focus: where data scientists concentrate more on building models through mathematics and software programming, data analysts distillate the results obtained through the use of those models.
Both of them have a pivotal role to play in helping businesses make sense of their data, bringing the next questions to the fore: what do data scientists do? What do data analysts do? And how the difference between a data analyst and data scientist contributes to making data useful for business?
How Data Science Can Help Business
“Gartner predicts that more than 33% of large organizations will have analysts practicing decision intelligence by 2023.”
Why data science? Because it can add value to a business in several ways. Five key areas where data analysts and data scientists can contribute are:
- Making decisions based on quantifiable evidence:
Data-driven, knowledge-based decisions replace ad hoc decisions, reducing risk by transforming uncertainty into knowledge. - Improving existing products and services:
Finding new opportunities in existing products and services provides a way to expand businesses in their area of expertise. - Staying competitive by identifying current and future trends:
Markets are very dynamic. Identifying changes in customers’ needs and tastes can be the differentiator that determines the survival and expansion of a business. - Identifying opportunities:
Market continuous change means that new opportunities appear frequently. Being able to identify them can be the difference between growing and disappearing. - Involving all stakeholders in decision making:
“I work through teams. It’s the only way I know how to work.”
Angela Ahrendts
With the current prediction tools based on automated machine learning, there is no need for sophisticated modeling skills, allowing all those involved in a project to participate and understand forecasting results. As a result, it creates a democratic environment for decision making.
Countless examples show how businesses all over the world have implemented data to improve their doings. One of them is a sizeable Turkish chain of fast food stores with a presence in over six countries. They used state-of-the-art machine learning to combine market, social, geographical, and climate factors; and create a forecasting model that could be used to analyze potential expansion paths. This model allows them to make more objective decisions that were previously based more on personal experience.
How Data Science Is Impacting Business
According to Gartner’s research director Erick Brethenoux, five impact categories are transforming businesses. These are:
- Innovation: Foster new thinking and business disruptions based on data science.
A good example is the self-driving vehicle industry, where companies such as Tesla and Google’s Wayno are intensively using data science in giving autonomy to cars. Although Level 5 fully autonomous vehicles are still not a commercial reality, companies such as Nissan are already marketing cars with partial driving automation. - Exploration: Explore unknown transformative patterns in data.
Data expeditions to unknown territories are playing an essential role in many industries. Recently an oil company in South Africa used its historical data to transform its refineries when faced with different market conditions. The simulations helped save millions in research and provided a safer way for engineers to adapt to the existing structures. - Prototyping: Challenge the status quo with radical new solutions.
Creating virtual prototypes is perhaps one of the areas where data science is making its most significant impact. From material design to jewelry to drugs, the possibility of using software to try different ideas has reduced risks and costs, permitting to cross boundaries never dreamed of before. - Refinement: Continuously improve existing processes and products.
This is perhaps the more common of the data-rich applications, particularly in retail. An example is Spanish retailer Zara, who – through the use of data – controls the life cycle of products and supply, changes its outlook weekly, and supplies its stores twice a week, reducing costs, stock surplus, and promotion expenses. - Firefighting: Identify the drivers of certain undesirable situations.
Data science can help businesses identify the causes of different problems, such as why customers return certain products or the reasons behind quality fluctuations.
With the current prediction tools based on automated machine learning, there is no need for sophisticated modeling skills, allowing all those involved in a project to participate and understand forecasting results. As a result, it creates a democratic environment for decision making.
Countless examples show how businesses all over the world have implemented data to improve their doings. One of them is a sizeable Turkish chain of fast food stores with a presence in over six countries. They used state-of-the-art machine learning to combine market, social, geographical, and climate factors; and create a forecasting model that could be used to analyze potential expansion paths. This model allows them to make more objective decisions that were previously based more on personal experience.
What Data Scientists Can Do For Business
Raw data is abundant. Transforming it into information requires smart work on it. It is in this transformation that the differences between data analysts and data scientist are apparent:
- What does a data scientist do?
A data scientist needs to transform the raw data into a well-structured dataset and use advanced mathematics, statistics, and AI technologies to create tools that can extract inferences from values, transforming the raw data into usable data. - What does a data analyst do?
Once the data scientists’ work is done, data analysts come to the fore, helping create information from data by using visualization, comparison, classification, and analysis.
They also help other stakeholders, such as project managers, engineers, and financial advisors, to transform the information obtained into knowledge, which they can use to make informed decisions.
Thus, the distinguishing mark between data analyst vs. data scientist is the knowledge of mathematics, statistics, and software required. Data scientist qualifications include machine learning, natural language generation, image analysis, hypothesis testing, and many more that are not in the realm of knowledge of a data analyst. Also, they need to be capable of programming in languages such as R and Python and have a good grasp of database technologies and SQL. Senior data scientists are also asked to grasp technologies such as TensorFlow, Hadoop, and cloud services becoming mainstream nowadays. That is why many consider fulfilling data analyst roles and responsibilities as a first step towards becoming a data scientist.
Applications of Data Science In Business
The applications of data science are growing, and with them, the business opportunities they provide. Below are some of the few areas where analytics is changing our daily lives:
“Big data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.”
Ginni Rometty, ex CEO IBM
- Fraud and Risk Detection:
Pattern recognition has become a standard tool for auditors today. They not only provide a way to identify crime but also to prevent it. - Healthcare:
Data science is advancing in practically every aspect of healthcare, from using images to detecting seizures to finding ways to reduce hospital costs. - Advertising:
Through the use of data, advertising is becoming more personalized by tailoring ads to each customer’s specific and current needs. - Human resources:
Companies such as LinkedIn are making increased use of their data to help organizations find the best candidates for different roles. - Insurance:
Insurers are increasingly relying on data science to find prospect insurance buyers and tailor the policies to their specific needs. - Genetics and genomics:
Data science helps scientists decipher the impact of DNA on our health, finding new causes and remedies for many hereditary diseases. - Drug development:
Data science helps scientists find patterns and model different drugs, and create virtual tests that reduce the risks and costs of human testing. - Robotics:
From drones to robot helpers for the household, old aged and disabled, pattern recognition is revolutionizing how robots learn to adapt to new environments. - Sports:
Data helps coaches and players develop better training techniques, improve strategies, and select new players. - Industry:
From early defect detection to design, data science is assisting engineers and researchers in reducing risks and costs,
Impact of Data Science Trends In Business
Data science is conquering the world in giant steps. In 2011 McKinsey called big data “the next frontier for innovation, competition, and productivity.” More recently, Harvard Business Review has called data analyst “the sexiest job of the 21st century”. So, how is data science evolving, and where is it heading to?
Data usage is not something new. Edward Tufte, an MIT professor, proves it when he shows a chart of imports and exports to and from England from the eighteen century in his book “The Visual Display of Quantitative Information.” However, what is unprecedented in our modern times is the enormous amount of information that is becoming available, which is also transforming the way we use it.
Data Science Trends For 2021
How is this transformation happening? During the latest years, the most immense drives come from artificial intelligence, automation, and the cloud, with blockchain technologies having a lesser impact.
Within artificial intelligence, natural language processing (NLP) is creating a revolution. So significant are the advances in this area that Gartner predicts the decline of the dashboard and an increase in an in-context report based information presentation.
Automation presents AI with the possibility of trying vast amounts of learning models in a short time. As costs are reduced, automation allows small and big players alike to create and deploy prediction models. Even more, it gives the possibility of continually updating the model with new data. This feature has enormous potentials in robotics and self-driven cars, where models can adapt according to new data received in almost real-time.
The cloud brings great expansion possibilities for data science, transforming businesses into a world of ecosystems. In the same report, Gartner says that by 2022, public cloud services will be essential for 90% of data-driven systems.
Data will also become a consumable, as 35% of large organizations will be either sellers or buyers of data by 2022.
However, Gartner predicts an increase in ledger database management systems (DBMS) for single-enterprise auditing of data sources when it comes to data storage. Blockchain will play a more complementary role in other business cases.
Future of Data Science In Business
In this era of the Four Industrial Revolution, data has become the primary business driver. Data collection is increasing at unstoppable speeds: from drones to satellites, to robots, to IoT, the sources of data are evolving all the time, and thus, the amount and quality of it too.
Our industrial system is based on science, which needs the supply of data to advance. Businesses are driven by the search for profits, which is based on factual knowledge expressed in data. Therefore, data is the modern fuel that feeds the whole pipeline, from basic science to industrial production to market development.
“The goal is to turn data into information, and information into insight.”
Carly Fiorina, ex CEO of Hewlett-Packard
Consequently, the need for data specialists will increase in the coming years. Researchers at the Northeastern University of Seattle predict that data usage will expand to all industries, from its current retail, industry, and financial services to farming for efficient food growth, to medicine to find cures for deceases, nonprofits to optimize their operations, etc.
Confirming this trend, Deloitte states the average farm is expected to generate an average of 4.1 million data points per day by 2050, compared to only 190,000 in 2014, thanks to IoT advances.
With this increase in data usage, ‘data literacy’ will become the bread and butter of every business. With it comes the need to better understand the whole data life-cycle: from acquisition to pre-processing to statistical and mathematical analysis to visualization, reporting, and decision making.
The result is that data and analytics opportunities will diversify and expand, and the difference between data science and data analytics will grow. Consequently, the distinction between data analyst vs. data scientist will become broader, and new roles will appear.
Conclusion – Difference Between a Data Analyst and Data Scientist
At present, there is a data science revolution that permeates all aspects of businesses. This change has impacted the professional world, and new role definitions – such as data scientist vs. data analyst – have appeared and evolved.
Consequently, the distinction between data analyst vs. data scientist is developing, and new roles, such as the data engineer, appear. This is mostly due to the ever-increasing amount of technologies and techniques that are continuously being developed, making it more difficult for a single person to be capable of being an expert in all of them.
The advantages of this increasing labor division are many: from better performance to market accessibility for many professionals. What remains clear is that regardless of the differences between data analysts and data scientists, mathematics and programming will remain the most critical skills in demand. Complementing them with good communication capacity will make the optimal data professional.
