Research Data Analyst Comparison: 5 Critical Differences
With analytics being a hot career option in the 21st century, the term “Analyst” is now attached to almost every job title. Cambridge dictionary defines “Analysis” as the process of studying or examining something in an organized way to learn more about it. This gives you a peek into what a Research Data Analyst does.
Table of Contents
Research Data Analyst studies or analyses data in an organized way. The word ‘organized’ is the key term here since it has a bigger impact on the success or failure of an Analyst’s job. The number of job titles in the field of an Analyst is significant enough to surprise anyone who doesn’t have extensive knowledge of job titles and human resource experience. Also, Research Data Analysts play a crucial role in the effective decision-making process of any business or an organization.
This article will give you an introduction to Research and Data Analysts. It will also cover the major Roles and Responsibilities of a Research Analyst and Data Analyst. The major focus of this article will be on the key differences between a Research Analyst and a Data Analyst.
Table of Contents
- Introduction to Research Analyst
- Introduction to Data Analyst
- Roles and Responsibilities of Research Data Analyst
- 5 Critical Differences between Research Analyst and Data Analyst
Introduction to Research Analyst
As the name suggests, a Research Analyst is the one who does some Research before the Analysis part. In other words, Research Analysts may not always have the data ready for them to analyze. They are supposed to investigate and explore to get hold of the data first. Then they analyze the data and try to derive insights that may form the foundation of some recommendations that they will make.
For example, a Stock Market Research Analyst explores data about the company, its competitors, its products, and anything that may affect the company in the short and long term to arrive at a recommendation about whether to buy, sell or hold the stock. So, a Research Analyst has to keep track of everything they use in their Research and ensure that they come from authentic sources. They also have to organize all the trailing information in a presentable way for any of their customers to verify.
To know more about the Research Analyst profile, visit this link.
Introduction to Data Analyst
A Data Analyst explores the data that is available to them and unearth insights about the data that can assist in enhancing the outcomes. The data that a Data Analyst is supposed to work with is usually presented in a refined manner. They then use their intellect and formal methods to analyze the data and come up with conclusions.
For example, a Sales Data Analyst takes a look at the yearly Sales data and comes up with numbers that can be used by higher authorities to decide how to allocate their next quarter’s marketing budget. They will look at the Sales accumulated on a region basis and go deep into those Sales Data to find what products account for what percentage of the revenue. At the end of the Research, the Data Analyst makes conclusions like ‘Only 5 % of our revenue comes from product A on spending 25 % of our marketing budget on that particular region.’
A Data Analyst will generally be more concerned about visualizing the findings in a report than worrying about the authenticity of the data since the Data Analyst has pulled the data from the organization’s well-maintained repository anyway.
To know more about the Data Analyst profile, visit this link.
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Roles and Responsibilities of Research Data Analyst
Research Data Analyst plays a very crucial role in any organization. You will get to know the roles and responsibilities of Research Data Analysts in this section. Some of them include:
- Research Data Analyst analyzes the patterns from the data which can help in effective decision makings.
- Research Data Analysts are also responsible for appropriately presenting the data with Bar Graphs, Histograms, etc.
- Research Data Analysts should collaborate with Engineers, Sales, and Marketing teams to establish the business needs.
- Research Data Analysts should also create well-defined documentation on their findings so that higher authorities can easily understand them.
- Research Data Analysts should use Statistical methods to analyze the datasets.
5 Critical Differences between Research Analyst and Data Analyst
The difference between a Research Analyst and a Data Analyst is best understood from different perspectives like Area of Focus, Skills, Target Audience, Data Sources, and the Tools they use to get the work done. Below is the list of differences:
1) Area of Focus
The primary area of focus for a Research Analyst is to gather data from various sources and verify the authenticity of the sources. The sources from which the outcome was derived are as important as the outcome itself to the Research Analyst. They generally spend more time collecting data than analyzing them or drawing conclusions. It is not uncommon for a Research Analyst to continue gathering data to support their recommendation even after it has been completed.
A Data Analyst is primarily focused on digging through data available to them and analyzing it to derive patterns and insights. They are not much concerned about gathering data from sources other than the organizational repository. Their main focus is more on mining patterns from numbers and how they can be presented to the stakeholders in a way that helps in making effective decisions.
2) Target Audience
The Target Audience for a Research Analyst is usually higher management who is directly in charge of allocating funds. It can vary from domain to domain. In a financial domain organization, it could be a Fund Manager who will straight away exploit the report to open a position in a specific asset. In the case of the Marketing domain, it could be a Marketing Vice President who decides how much money to allocate to a specific product in the next quarter.
While the Target Audience for Data Analysts is also similar to the above, they can also be much lower in the hierarchy or reporting structure. It can be a specific Sales Manager In-charge of a region or even a Marketing Manager In-charge of a specific product who already has a specific budget allocated.
3) Skills and Qualifications
The primary skill set for a Research Analyst is Research skills. They should be focused enough to keep digging for facts from various sources and corroborate them via alternate sources. A Research Analyst is usually very domain-focused. So they need to have qualifications and formal education in the specific domain to make sense of the information that may come from various sources.
For example, a Financial Research Analyst usually has a Master’s Degree in Finance or even in a narrower area of Finance like Asset Research or Accounting. They should have good comprehension skills and report writing skills since the expected outcome is usually long reports that organize the complete Research and arriving at a concrete conclusion. The ability to work with numbers is also a big advantage.
A Data Analyst is usually not as domain-focused as a Research Analyst. While it is an advantage for a Marketing Data Analyst to have a Master’s in Marketing, however, it is not a mandatory requirement. A person who is good with numbers and excellent logical skills can become a good Data Analyst.
Skills in Mathematics and Statistics are of utmost importance. Knowledge of SQL (Structured Query Language) and a Programming Language can be a huge advantage since a Data Analyst will mostly be working with data that is hosted by the organization in a database. The ability to present the findings via great visualizations is also an added advantage. Knowledge of Analytical Tools like Power BI, Tableau, etc is also handy. Exposure to Cloud-based ETL Tools like Hevo can make the job of a Data Analyst much easier.
4) Source of Data
There is virtually no limit to the source of data that comes under the purview of a Research Analyst. They can use any data that is available in the public or a private domain as long as they are confident about its authenticity and legality of use. Deciding which data to use is also an important skill set of a Research Analyst and plays an important role in the outcome.
A Data Analyst’s source of data is typically a Data Warehouse or an organizational Data Mart. A Data Warehouse hosts all the data that is available to an organization from its customer interactions and Sales. A Data Mart is a subset that is generated for a specific function like Marketing or Sales. Data Analyst is not usually concerned about gathering data external to the organization. They will use them if it is available to them in the Data Warehouse.
For example, Analysts who are working with Real Estate loan customers may use the Real Estate price data that is gathered from a third-party source or already dumped data in the Data Warehouse.
The internet is the biggest Tool for a Research Analyst. They may also use their relationships and networks to gather the data. At times they may also have the liberty to hire third-party Researchers or data sellers. They use corporate databases, data providers like Bloomberg Terminal, Research Reports from reputed sources, etc. In addition to this, they may also use number-crunching Tools like Power BI or Tableau in rare cases.
For a Data Analyst, Databases and Visualization Tools form the major chunk of the armoury. They may use ETL (Extract, Transform, and Load) Tools to transform the data. Cloud-based ETL Tools like Hevo can make the job easier to a great extend. They may also use visualization Tools like Power BI or Tableau.
As evident from the prose above, apart from the presence of the word ‘Analyst’, there is not much in common between a Research Analyst and a Data Analyst. A Research Analyst is a much more domain-focused position handled by people with specialized knowledge in that area. A Data Analyst on the other hand is focused more on the available data and their job revolves around Cleansing and Transforming data till a pattern emerges from the data. However, Research Data Analyst forms a critical part of any organization as they are the ones who help in making future decisions.
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