SAS: Telling a Story With Data

Statistical Analysis Software (SAS) has long been used as a data analytical tool to perform complex analyses. With SAS, you use statements to write a series of instructions called a SAS program. The program communicates what you want to do and is written using SAS language. There are some menu-driven front ends to SAS, for example, SAS Enterprise Guide software which makes SAS appear like a point-and-click program. But in the realm of the modern era where we are progressing in Machine Learning, Artificial Intelligence, and metaverse, why do most of the consulting firms stick to SAS programming to tell a story? Let us try to break it up.

What Do Consulting Firms Do?

Economic/Management Consulting

Many big consulting firms across the world provide business insights and help in growing the business or help them bail out of financial stress and litigation. That is the reason they are so attractive and lucrative career for most of the people. Consultants will have to crunch the numbers and most importantly they should be strong in explaining the methodology used and explain even a minute detail to the clients to attract future business. Especially, Economic consultants use their expertise to analyze complex economic issues, provide insights, and offer strategic advice to clients.

Why So Popular in Consulting?

1. Reporting and Visualization:

SAS offers an easy way of communicating with the data with the statistical reporting tools and creating detailed and customizable reports. The reports generated can be in varied formats including HTML, PDF, and Excel. This makes it easier to present it in the client presentation and show the output in whatever format the client requires it to be.

2. Excellent Visual Analytics:

SAS offers a unique visual interpretation customizable dashboard that helps in solving complex and large data that can be easily understood and interpreted. 

3. Statistical Analysis:

SAS provides a comprehensive set of statistical procedures for descriptive and inferential statistics. Users can perform hypothesis testing, regression analysis, analysis of variance (ANOVA), and more.

The SAS/STAT module includes a wide array of statistical procedures that cater to different analytical needs. Unlike other programming languages like Python which uses inbuilt packages for all the regression analysis, with SAS you can replicate each step in the process without any packages which makes the analysis more robust and easier to understand.

4. Data Management:

SAS is known for its robust data management capabilities. It can handle large datasets and offers a variety of data manipulation and transformation tools.

SAS Data Integration Studio allows users to design and manage ETL (Extract, Transform, Load) processes for Integrating and cleaning data.

5. Machine Learning:

SAS Viya:

SAS Viya is a cloud-enabled, in-memory analytics platform that includes machine learning capabilities. It allows users to perform distributed and parallel processing for large-scale machine-learning tasks.

SAS Viya provides a wide range of machine learning algorithms, including regression, decision trees, clustering, neural networks, and ensemble methods. It supports distributed computing for handling big data.

6. Integration With Other Tools:

SAS can integrate with other data analytics tools and languages. For example, it can import and export data in various formats, including CSV, Excel, and relational databases.

SAS can work in conjunction with open-source languages like Python and R, allowing users to leverage the strengths of both SAS and these languages.

7. Security and Compliance:

SAS provides features for data security and compliance, making it suitable for industries with strict regulatory requirements, such as healthcare and finance. SAS provides robust authentication mechanisms to ensure that only authorized users can access the system. It supports integration with various authentication providers, including LDAP (Lightweight Directory Access Protocol), Active Directory, and other authentication services.

Industry-Specific Solutions

SAS offers industry-specific solutions tailored to sectors such as finance, healthcare, and government. These solutions provide specialized tools and methodologies. 

Finance

1. Anti-Money Laundering and KYC Compliance

SAS is utilized to identify and prevent money laundering activities. It helps financial institutions comply with AML and KYC regulations by analyzing customer transactions and identifying suspicious patterns. Advanced analytics and machine learning algorithms in SAS help identify unusual transaction behavior, such as unexpected transaction volumes, high-frequency transactions, or transactions that deviate from established patterns.

2. Insurance Analytics

In the insurance sector, SAS is used for actuarial analysis, claims processing, and underwriting. It assists in risk assessment, pricing, and fraud detection within the insurance industry. SAS analytics help underwriters evaluate various factors, such as demographic information, medical history, and financial data, to determine the appropriate coverage and pricing for insurance policies.

3. Financial Forecasting

SAS supports financial forecasting and budgeting processes. It enables organizations to create models that predict future financial trends based on historical data and current market conditions. 

4. Operational Analytics

SAS helps consulting firms to optimize operational processes by analyzing data related to efficiency, performance, and resource utilization. This can lead to improved operational efficiency and cost reduction.

5. Audit and Compliance Monitoring:

SAS is used for internal and external auditing purposes. It helps organizations monitor and ensure compliance with internal policies and external regulations, providing a comprehensive view of financial activities.

6. Portfolio Management:

SAS is applied in portfolio management to optimize asset allocation, assess performance, and manage investment portfolios. It helps in analyzing historical data, predicting future trends, and making informed investment decisions.

7. Fraud Detection and Prevention:

SAS is a powerful tool for detecting and preventing fraudulent activities in the financial sector. It uses advanced analytics to identify patterns and anomalies in transactions, helping organizations combat fraud.

8. Risk Management:

SAS is extensively used for risk modeling and analysis. It helps financial institutions assess and manage credit risk, market risk, operational risk, and other types of risks. SAS can be used to build models for stress testing and scenario analysis.

Health Care

Healthcare Fraud Detection

SAS is employed to detect fraudulent activities in healthcare, such as billing fraud or false claims. It analyzes patterns in claims data to identify anomalies and potential instances of fraud, waste, or abuse.

Population Health Management

SAS aids healthcare organizations in managing population health by analyzing and interpreting data related to patient demographics, disease prevalence, and healthcare utilization. This information is crucial for preventive care and resource allocation.

Electronic Health Records (EHR) Analytics

SAS is utilized for analyzing data stored in electronic health records. It helps healthcare providers extract valuable insights from patient records, improve clinical decision-making, and enhance patient care.

Conclusion

SAS is a powerful tool that not only acts as a typical programming language but it also offers diverse functionalities from organizing the data to statistical visualization. SAS's versatility and extensive set of analytics and modeling capabilities make it a valuable tool for financial and healthcare professionals who need to analyze large datasets, make data-driven decisions, and comply with regulatory requirements in a dynamic and complex industry. Despite the emerging technologies, because of the simplistic nature of coding and deciphering the results which can be presented easily, SAS still emerges as a top programming tool in the consulting industry.