When we say data analysis, dozens of different techniques appear in the minds of all of us. Therefore, to answer the question of what a data analyst is, we first need to answer what data analysis is.
Thanks to the endless possibilities that technology offers us, we can now easily collect data. In the past, we tried to analyze only through surveys or data forms we made with pen and paper. Fortunately, this is no longer the case.
We can create all sorts of different kinds of data, whether it be the historical price of different items or how many daily steps a person takes, data is everywhere now. Fortunately, technology lets us transfer our data to the computer environment easily, store our research data obtained from survey forms on our computers, and even host large volumes of data in different channels thanks to cloud technology.
All this data we store is precious. The Economist, says that the world's most valuable resource is no longer oil, but data.
However, we need to process this valuable data to draw meaningful results, just like you need to process raw diamonds to create beautiful jewels. Data analysis is the processing link that offers us this opportunity, and data analysts are exactly these professionals that perform this work.
Today, one of the top emerging professions is the data analyst profession. The title of a data analyst is given to the person who collects all the information available within a company, utilizes various tools to interpret these data, and then reports this information and meaningful insights to managers and stakeholders. It is a discipline that enables businesses to make more accurate decisions with the information they collect.
To explain a bit further, data analysts follow the systematic application of statistical and logical techniques to describe data through images, tables, and graphs. They detect statistical trends by evaluating probability data and drawing meaningful conclusions.
What does a data analyst do?
A data analyst is a person who defies problems, determining the practices to be followed in a clear way. They need to prepare data about a given subject, starting by retrieving data from databases and passing them through analysis. As a result, they find the most suitable data model for their purpose. After applying the model, they arrive at the results and deliver the results to the business via communicating it in effective ways such as visualization.
Generally, the work of a data analyst consists of these stages:
Data Collection: This is the stage where the data required for the problem we want to solve or the hypothesis we want to test are determined, as well as the databases where these data are stored.
Evaluation: There may be some inconsistencies between data from different sources. At this stage, the discrepancies are identified.
Merge and Cleanup: Incompatibilities identified in the previous stage are resolved, and all data is kept in a single database.
Selection: The data required in accordance with the model are selected.
Conversion: The values of the variables need to be corrected again, according to the model.
Communication: The results and findings are communicated to managers and executives of the firm through the use of data visualization and clear reporting, which eventually creates real business value.
The responsibilities of a data analyst:
The primary duties of data analysts are as follows:
- Identifying business needs
- Producing solutions that meet business objectives
- Researching the financial records of companies
- Making the data more understandable
- Determining the analysis and measurement methods of data
- Use data to communicate insights to the corporate world
The different kinds of analysts out there
Due to the specialization of roles, the term "data analyst" is seen as way too broad by some professionals. There are different types of data analysts in the current corporate world. Though they share similar skillsets, they offer different kinds of expertise, helping organizations accross multiple areas.
Below you will find some popular types of data analysts:
Business Intelligence (BI) Analyst
BI analysts spend most of their time analyzing data to identify company deficiencies and come up with solutions. BI analysts may also be responsible for competitor analysis, tracking industry trends, and discovering where their organization can improve cost management. Depending on the organization involved, a BI analyst may be responsible for developing or researching an appropriate business intelligence solution that fits the company's needs.
Business analysts evaluate the business processes of companies, conduct improvement studies, reveal the missing parts and explain all these with reports. They thoroughly examine all elements to be tackled within a project and allow the processes to be completed smoothly. It is especially important for these professionals to have a developed mathematical and analytical thinking structure. It is also vital to demonstrate written and verbal communication skills, and to have professional skills such as producing quick solutions and planning.
Market Research Analyst
A market research analyst is a person in charge of determining the status of products and services offered for sale. They evaluate future sales and prices, making predictions such as what the customers will want based on data. Market research analysts may work with the research departments of private companies and consulting companies that only specialize in market research. A market research analyst examines any product or service that has been or will be offered to the market with all its details. This requires pooling many different skills.
Operations Research Analyst
The Operations Research Analyst collects and analyzes data related to business operations, and identifies potential or existing problems. They also demonstrate the results of different actions and assist in the decision-making process. They are high-level problem solvers focusing on optimization, data mining, statistical analysis, and mathematical modeling. Ultimately, an operations research analyst aims to minimize costs and improve efficiency on resource allocation, development of the production schedule, supply chain management, and pricing procedures, overall assisting the leadership team.
A Marketing Analyst evaluates the consumer’s profile and behavior by examining their preferences. They also have the ability to apply hypotheses based on data analysis.
In order to be a good marketing analyst, it is necessary to know the movements of the leading companies operating in the sector. This allows for the discovery of the latest tools used to develop the company's marketing strategy. Above all, a marketing analyst shall be aware of each piece of data and information in order to see its proper functioning.
You might straightaway understand from the title that these analysts work in the medical and healthcare sector. They use data from a variety of sources which they clean and analyze. They drive meaningful business outcomes that serve the healthcare industry, ranging from the caretaking of the patients to some operational optimizations.
Key skills required to be a data analyst
The data analyst gives both strategic and tactical support to the company they work for by analyzing the information gathered from each source.
To start a career as a data analyst, companies usually expect candidates to have a degree in Mathematics, Statistics, Management Information Systems, Industrial Engineering, Informatics, Computer Engineering, and similar fields. Therefore, it is usually a prerequisite to have a university education in such departments. However, having a degree is not the only option if you are dedicated to educating yourself in certain areas that are the most desired in the field of data analytics.
First, a certain level of Statistics knowledge is essential. The higher your statistical knowledge, the easier it will be for you to understand problems and find solutions to them. You have to look at things analytically. You must learn to both deduce from small details and be able to see the big picture.
The primary qualifications of data analysts that give an organization an advantage working in the private and public sectors are as follows:
- Advanced spoken and written communication skills
- Strong mathematical and numerical skills
- High levels of statistical proficiency. After all, data analysis is a lot about statistics
- The ability to research new models and techniques
- Having sufficient technical knowledge, such as database designing, modeling, data mining, and segmentation
- Understanding of data visualization
- High caliber problem solving
- Experience with queries, preparing reports and presentations
- To have the ability and willingness to learn continuously
Common tools data analysts use:
The set of skills we mentioned above lets data analysts create extensive value with data. However, these professionals still need to have a good understanding of some common tools in order to be data analysts. These tools let data analysts find, organize, analyze and present the data that firms possess in very efficient ways. If you want to have an effective and up-to-date team of data analysts, we would advise you to train your team to use these tools.
- First things first, a data management tool for relational databases like SQL is a must-have tool in any data analyst's toolkit.
- A programming language like Python is both easy to learn and versatile.
- Statistical and analytical programs such as SPSS, R, and MATLAB.
- An open source online software like Jupyter can come in very handy, helping you and your team to collaborate on projects by sharing live codes, documents, and graphics. They can be very useful for data cleansing and machine learning projects.
- To make the insights understandable to a broader business audience, data visualization tools such as Tableau and Power BI are required.
- For finding, understanding, and trusting your data, a data catalog tool will do the trick. We’ve pulled together a benchmark of all the data catalog tools available here.
Where do data analysts work?
Due to the current needs of many firms and how valuable data is in the modern world, data analysts work in all sorts of firms and areas.
The most common employers of data analysts are:
- Specialized software development companies
- Telecommunications companies
- Public sector organizations
- Social media experts
- Colleges and universities
- Pharmaceutical companies
However, we know that just reading the sectors can be a bit blunt. Data analysts work everywhere. From tech giants like Apple, Amazon, and Google to wall street wolves like Goldman Sachs or JP Morgan, you can find them in any business. The job market for data analytics is very high in demand; there are more than 170,000 data analytics jobs in the US alone, thus to be competitive and attract the right talent, companies need to offer good benefits. Remember that the employee churn rate is quite high in this sector.
According to people who are currently working in this profession, the salary a data analyst will receive is directly proportional to their experience and how high their knowledge is. According to Glassdoor, an average Data Analyst makes around $96,400 a year. Moreover, the career development path is very progressive for these professionals, and many proceed to make much higher wages as their careers move forward. To give an example, a Data Analytics Manager’s median salary in the US is around $118,000
We believe that it is always a good idea to take examples from accomplished people in any field, and understand what they did right and how they make smart and successful choices. Below, you will find an inspiring list of some of the most notable people in the data analytics sector:
- Yann LeCun, the current Director of AI & Research at Meta (Facebook) who made a name for himself for his industry-changing inventions.
- Dr. DJ Patil, who was the Chief Data Scientist at LinkedIn and moreover, helped to shape the future of data policy in the US during his times at the White House. Pretty impressive.
- Yoshua Bengio, one of the fathers of data analytics won the prestigious Turing Award due to his valuable contributions to artificial intelligence.
- Prof. Leslie Kaelbling, a leading academic at MIT, is well known for her research on mobile robotics, reinforcement learning, situated agents, and decision theoric planning. She is also the founder of a respected journal; the Journal of Machine Learning Research.
- Caitlin Smallwood, is the woman behind the reason why you get good recommendations at Netflix that match your taste. Her application of data analytics to real-life business models creates huge business value.
After all, any company that wants to thrive, or even survive in such a data-driven world needs to have a good team of data analysts. Such a team can let you outperform many of your competitors by offering your company meaningful insights that would be very difficult to uncover otherwise. We believe that companies should pay close attention to this field sooner rather than later, thus building a coherent data analytics team is crucial.
To attract and retain talented professionals in this field, giant companies are racing and offering inconceivable salaries and benefits, but that is not the only way to do so. A good working environment, respectful mutual understanding, and good enough compensation packages can let you have a great team of data analysts. At first, it could seem like a tough task to do, but have full faith that this investment ultimately pays off.
We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data. If you’re a data leader and would like to discuss these topics in more depth, join the community we’ve created for that!
At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation.
Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog software to be easy to use, delightful and friendly.
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“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.” - Michal P., Head of Data