SAS Data Analyst: 6 Key Roles & Responsibilities Simplified

on BI Tool, Data Analytics, Data Science, Data Visualization, SAS, Tutorials • July 22nd, 2021 • Write for Hevo

Role of a SAS Data Analyst

If you are looking to get a job as a Data Analyst, you might have probably come across SAS in several job listings. Across the world, businesses are aspiring to Operationalize Analytics to Reduce Costs, Accelerate Growth and Innovation. As such, demand for SAS Data Analysts who have hands-on experience in Data Analytics, Business Intelligence, Data Engineering, and Data Science has skyrocketed. For the past eight years, Gartner has recognized SAS as a leader in their Magic Quadrant for Data Science and Machine Learning Platforms.

By 2025, researchers are estimating that the world will have cumulatively created 175 zettabytes of data. From Structured Transactional Data, down to Unstructured Data from search engines and forum discussions, businesses of all sizes are creating huge amounts of data annually. But in its raw form, this data has no meaning. Applying analytics to this data has for that reason risen to become a key strategic advantage for these businesses because of its potential to supercharge Marketing initiatives, improve Customer Success outcomes, and guide Product Road Maps.

This article will first introduce you to SAS and will then explain the characteristics of a SAS Data Analyst. It will also discuss various roles and responsibilities that come will the role of SAS Data Analyst. Read along to know about the role of a SAS Data Analyst.

Table of Contents

Introduction to SAS

SAS Data Analyst - SAS Logo
Image Source

SAS (Statistical Analytics Systems) is a proprietary software suite. Initially developed by the North Carolina State University between 1966 and 1976, and now owned and maintained by the SAS Institute — partially owned by the governments of Sweden, Denmark as well as other private investors. SAS has since had many releases.

The SAS Suite of Software finds applications in Business Intelligence, Data Integration, Fraud Management, Financial Intelligence, and IT Management. It features its programming language, PROC SQL, SAS’s own ANSI compliant implementation of the Structured Query Language that you can use to do many valuable things such as accessing data, managing data, analyzing data, and presenting the data. 

Most SAS usage is in big business, mostly Health Service Research, Oil & Gas, Big Pharma, and Finance. Citibank, HSBC, JP Morgan, and Wells Fargo are examples of enterprises that have for decades used SAS for Risk Management and Anti-money Laundering applications. They are already heavily invested in terms of cost and resources. As such, they cannot simply switch from SAS in favour of alternative tools like Power BI and SAP, which makes the prospects of aspiring SAS Analysts seem bright.

To have further information about SAS, follow the Official Documentation.

Simplify Data Analysis Using Hevo’s No-code Data Pipeline

Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources (including 30+ free data sources) to numerous Business Intelligence tools, Data Warehouses, or a destination of choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.

Let’s look at Some Salient Features of Hevo:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built to Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Explore more about Hevo by signing up for the 14-day trial today!

Introduction to a SAS Data Analyst

A SAS Data Analyst is a Business Professional who takes all the complex jigsaw of data available to an organization and uses the SAS Suite of Analytics Software to Manage and Report on that data. After interpreting the data, the Analyst then passes the insights to stakeholders so that the organization can make the best-informed decision.

SAS Data Analysts can leverage the platform to solve problems and challenges faced by their companies by finding patterns in data that can have useful and relevant insights about business operations.

By tracking KPIs in SAS, Data Analysts can help organizations recognize their strengths and weaknesses and modify their business strategies accordingly to optimize Customer Acquisition, Activation, Retention, Revenue, and Referrals.

Roles and Responsibilities of a SAS Data Analyst

SAS Data Analyst - SAS Data Analytics
Image Source

A SAS Data Analyst has the following roles and responsibilities:

1) Defining the Problem

The problem statement is the first and arguably the most important step in solving an Analytics problem. It’s the role of a SAS Data Analyst to work alongside teams within the business or the management verticals to establish business needs. They first have to determine the client’s needs (i.e. Dashboards, Reports, etc). 

The next step is to create a plan of action about where to get the data as well as where to store it. The final step is to communicate the plan to the team and other relevant stakeholders.

2) Collecting Data Sets from Primary and Secondary Sources

Downstream Reporting and Analytics are heavily reliant on consistent and accessible data. To make better decisions, SAS Data Analysts have to collect all of the data available to the organization. This is because an incomplete picture of the available data can result in misleading reports, not to mention Spurious Analytical conclusions. This data can come from a variety of sources e.g. Transactional Databases, SQL Backups, Flat Files, Spreadsheets, and APIs

Therefore, the SAS Data Analyst has to work hand in hand with Programmers to create ETL Pipelines and business rules to get the data in a format that SAS can use and aggregate. They can also partner up with companies that offer efficient data ingestion services.

3) Cleaning and Organizing Data 

Arguably the most time-consuming role of a SAS Data Analyst is assessing the quality of the data that you are collecting. Data from the various primary and third-party sources will more often than not tend to be messy. Some systems store Structured Data while others store it in an Unstructured Format. It is the norm rather than the exception that this data will have anomalies such as missing values, outliers, and duplicates. The SAS Data Analyst, therefore, is tasked with cleaning the data to guarantee a consistent and relevant predictive model through normalization and standardization.

4) Preparing Data for Analysis

Before the SAS Data Analyst can start creating their Reports and Visualizations, they first need to create Views. Views allow the Analyst to combine several tables into one, and from there, they can choose a subset of that data for Reporting and Visualizations.

For example, if the Analyst is collecting data from a website and mobile app and sending it to one table, then by default, they will be reporting on data from both data points. So if they want to analyze Mobile Traffic only, they have to use filters to customize the views and report only on a subset of the data. A SAS Data Analyst also has to format the data to ensure that it is consistent for the specific purpose.

5) Creating Reports with Clear Visualizations

Once the data is cleaned, organized, and prepared in the form of Views, it is time to focus on Analysis and Reporting. This role is all about generating a visual representation of the data using a combination of Charts, Maps, and other Graphical elements to provide simple and comprehensible insights and patterns in the data for strategy and decision-making. These Reports and Visualizations need to be organized and presented in a way that stakeholders, potential investors, or other business partners can easily understand.

6) Designing and Maintaining Databases and Data Systems

SAS Data Analysts often have to provision intermediate tables for Aggregation and Data Cleaning. They then load the data into Database Systems, normally Data Warehouses, for analysis. A properly designed Database offers Analysts access, to up-to-date, accurate information. A correct design will therefore be most likely to meet the analytical needs of a SAS-based Data Analyst and can easily accommodate change. It is therefore the SAS Data Analyst’s role to recommend, design, and maintain Data Warehousing solutions that meet the organization’s BI needs.

Conclusion

Ultimately, the SAS Data Analyst is only as valuable as their ability to present actionable insights that can help their organization drive return on investment and make confident data-driven decisions.

This article provided you with a detailed introduction of SAS, SAS Data Analytics, and the role of a SAS Data Analyst. Furthermore, it discussed the keys roles and responsibilities associated with the job of a SAS-based Data Analyst. If you’ve been looking to get into a career in SAS Analytics, this article would have offered you an insight into the major roles and responsibilities of this position.

Your work as a SAS Data Analyst will involve regular data transfers for analytical purposes. Hevo Data helps you directly transfer data from 100+ data sources to a Data Warehouse or desired destination in a fully automated and secure manner without having to write any code or export data repeatedly. It will make your life easier and make data migration hassle-free allowing you to focus on your Critical Data Analytics. It is User-Friendly, Reliable, and Secure.

Give Hevo a try by signing up for the 14-day free trial today! You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

Share your experience of understanding the role of SAS Data Analyst in the comment section below!

No-code Data Pipeline for your Data Warehouse