Businesses generate large amounts of data from many activities such as sales, customer relationship management, order management, logistics and market research. To benefit from these data assets your business needs to organize, analyze, and interpret your data.
The responsibility of gathering and interpreting these insights from your data assets is often handed to a data analyst. Depending on the size of the organization and the task to be completed the job title of wrangling data can be referred to as a data scientist, data engineer, data analyst, business analyst or financial analyst.
All of these job titles have a common objective of enabling an organization to solve its problems using data. The basic skill required to process and understand data is having an ability to analyze numbers, patterns, and trends. The level of advancement and experience varies across the job titles. Each of the job titles requires a technical and a soft skill set.
At the highest level is a data scientist, followed by data analyst then business analyst. In this article we will discuss the typical job descriptions and responsibilities of the different job titles and roles.
The common soft skills required for data analysis include:
Effectively communicating technical concepts in simple language
A good understanding of the industry you work in and how data can help your organization to be successful in its industry
Creatively thinking on ways to analyze data
Let’s take a look at the various roles of data analysts within an organization. Keep in mind that these are high level generalities for each data analyst position. The roles are varied and depend largely on the needs of an organization. Thus, this list is hardly exhaustive because every organization has different needs.
Most data scientists have mathematics, computer science, or technology backgrounds. A data scientist requires an overlap of computer science, statistics, and domain expertise skills. These individuals are able to combine a good understanding of business, data management, programming, and visualization skills in a way other individuals cannot.
Some common roles of a data scientist include:
Cleaning, deduplication, and assuring the quality of data used for analysis
Applying machine learning algorithms to organize and wrangle data
Integrating external and internal data to enrich the value of data
Analyzing data and communicating the results in a simple and effective way
A data scientist requires a combination of skills listed below to successfully perform assigned job tasks:
Excellent communication skills
Working knowledge of machine learning techniques such as random forests and support vector machines
Knowledge of data querying using tools such as relational databases, Hive, and Pig
Knowledge of Hadoop ecosystem tools such as Spark depending on specific tool used within the organization
Knowledge of statistical inference
Computer programming skills that are essential for integrating data products into systems
Data engineers are experts who process data that is often used by data scientists and analysts. They are able to gather data from different sources into a single repository. Thus, making the data assets of the organization easy to access.
Data engineers perform extraction, transformation, and loading of data into data warehouses which can then be used by data scientists and analysts for reporting and analysis. The focus of data engineers is not on analytics instead they focus on designing and architecting data infrastructure that will be used by analysts and data scientists.
Data engineers require knowledge of relational and nonrelational databases, data integration tools, Hadoop tools relevant to data integration, and computer programming.
The roles of data engineers often include:
Grabbing data from different sources, cleaning, and storing it in a single repository
Implementing the right data systems for a variety of data problems
Ensure data systems are consistent, able to handle scalability in workloads as well as are secure and protected
Data analysts are professionals who are able to query data, create reports, and deliver data visualizations. Data analysts embrace various techniques and use existing tools and techniques to solve problems organizations may be facing. Usually data analysts are not expected to develop new analytical techniques and algorithms to handle big data.
You can expect your data analyst in Washington D.C. to use reports and charts to help others make better decisions. In many cases, data analysts are considered an entry point for a data science career. Data analysts need to have working knowledge of data visualization, analysis, statistical tools like IBM SPSS and SAS, and various data management tools such Microsoft Access and Excel. They are also well versed in business intelligence tools like Tableau, and querying data with SQL.
Roles of a data analyst can include:
Sourcing, cleaning, and transforming data to make it appropriate for analysis
Using tables and charts to present data
Communicating insights gained from statistical analysis
Developing data collection tools
Writing SQL queries
Business Intelligence (BI) Developer
BI developers are experts who work with data consumers to identify and understand their needs. They use user requirements to design and build reports. BI developers need an applicable knowledge of data to extract, transform, and load tools such as Microsoft SSIS.
BI developers also need knowledge of relational databases and BI tools such as Tableau. BI developers do not typically analyze data; instead they help other users be able to analyze and interpret data.
The roles of a BI developer often include:
Create dashboards and reports
Develop cubes for exploratory data analysis
Support development and maintenance of data warehouses
Develop packages for data extraction, transformation, and loading
A business analyst focuses on identifying the type of data to be collected and how it will be collected. Other data related tasks performed by business analysts are defining business metrics, designing dashboards, reports and creating alerts that are relevant to the business.
Business analysts have deep domain expertise. They perform non data tasks such as evaluating return on investment, planning, budgeting, risk management, business process re-engineering, and executive reporting. In smaller organizations data tasks typically performed by a business analyst are also performed by data scientists. For example, in startups one individual may be performing the duties of a data scientist and a business analyst.
In most cases, businesses often bring in an experienced business analyst first. Then if there are tasks that are beyond the ability of a business analyst a data scientist can be hired.
In this article we identified the different professionals in the data collection, analysis, and interpretation pipeline. At dc Analyst we offer services for a variety of roles and can help you identify the best analyst for your next project or organization.