Analytics

Data Warehouses and Decision Support Systems (DSS)

By Brynne Henn - July 8, 2021

With the growing scope of modern-day business, you can pull massive amounts of data from all aspects of your business. But while it’s great to have so much information available, it can take time to organize and glean insights from your data extractions, much less move forward with decision making.

Don’t worry: A data warehouse decision support system (DSS) can help you make sense of that data in time to act.

What exactly are DSS and data warehouses? We’ll take a look at the basics in this guide.

What are decision support systems?

DSS helps businesses make sense of data so they can make more informed management decisions.

Pretty much any system that provides clarity about data can be considered a DSS. That includes GPS route planners like Google Maps or Waze, which evaluate multiple routes and traffic conditions to find you the best route between points A and B. In healthcare, a doctor might use a computerized DSS to diagnose and prescribe medication for a patient.

What are decision support systems used for?

A DSS can be used in several ways:

  • To produce easy-to-understand reports about key data trends based on user specifications
  • To build predictive models and visualizations, such as of projected revenue, sales, cash flow, and inventory
  • To identify the health of a business based on current and historical data and evaluate the need for action

What are the major components of a decision support system?

DSS frameworks typically consist of three main components:

  1. The model management system, which uses various algorithms in creating, storing, and manipulating data models
  2. The user interface, the front-end program enabling end users to interact with the DSS
  3. The knowledge base, a collection of all information including raw data, documents, and personal knowledge

What are data warehouses?

A data warehouse is a collection of organization-wide data, typically used in business decision making.

Data warehouse toolkits for building out these large repositories generally use one of two architectures.

Inmon method:

  • Normalized
  • Focuses on data reorganization using relational database management systems (RDBMS)
  • Holds simple relational data between a core data repository and data marts, or subject-oriented databases
  • Ad-hoc SQL queries needed to access data are simple

Kimball method:

  • Denormalized
  • Focuses on infrastructure functionality using multidimensional database management systems (MDBMS) like star schema or snowflake schema
  • Can store all data from an organization
  • SQL queries are more complex

What types of decisions do data warehouses support?

Data warehouses are handy for making big-picture business decisions, since they pull data from all different sources within a business.

What are the four stages of data warehousing?

Data warehouse development typically involves the following steps:

  • Offline Operational Databases: The database of an operational system is copied to an offline server
  • Offline Data Warehouse: Data is regularly updated from the Operational Database and organized to meet the objectives of the data warehouse
  • Real-Time Data Warehouse: The data warehouse is updated in real-time every time a transaction or event takes place in the operational database (e.g. a customer purchasing an item from an online shop)
  • Integrated Data Warehouse: The data warehouse is updated continuously with every transaction; it also generates transactions that are passed back to the operational system and used in day-to-day activity

What is the difference between data warehouses vs. databases?

Though they rely on similar relational database technologies, data warehouses are not the same as databases.

Why is it important to differentiate them?

Database systems are used for processing day-to-day transactions, such as sending a text or booking a ticket online. This is also known as online transaction processing (OLTP). Databases are good for storing information about and quickly looking up specific transactions.

Meanwhile, data warehouses are used to analyze transactions from multiple data sources. This type of online analytical processing, or OLAP technology, is used, for example, in data mining, as well as forward-looking business intelligence functions like budgeting and forecast planning.

How do data warehouses and decision support systems work together?

Think of a data warehouse DSS like a buffet.

If a data warehouse is the collection of raw ingredients, then a DSS consists of the oven and other cooking tools engineers can use to manipulate the ingredients. If you know what you want to cook and use your DSS to cook up data in the right way, end users can decide which dishes look best to them and their businesses.

What is the difference between a DSS and a data warehouse?

A DSS is a system (specifically, an interactive information system) built on top of a data warehouse to make it easy to query, or pull information from, data.

What are the advantages of a data warehouse decision support system?

Data warehouse DSS benefit organizations in many ways:

  • Holistic point of view: The broad scope of data warehouses make them well-suited for conducting organization-wide data analyses
  • Enhanced business intelligence: Data warehouse DSS can hone in on all the specific factors affecting your business performance, beyond just top-line revenue metrics
  • Data consistency: Since data warehouses use standardized processes to transform data from across an organization, data is more consistent, cohesive, and accurate across the board
  • Speed: Having all your data and queries in one place makes automation easy, removing the need for manual analysis and testing
  • Efficiency: Decision makers can quickly access all of a company’s data from one single platform
  • Easy to use: All users can access critical data, even with little to no tech support

Schedule a demo with Sisu

Analyzing your data shouldn’t be hard nor time-consuming. With Sisu, just a few quick clicks are all you need to parse through cloud-scale data in near real-time. When you know not just the what, but also the why of key data changes, as they happen, you can make quicker and smarter business decisions.

Want to learn how Sisu can improve your decision-making process? Schedule a demo today and see for yourself why top analytics teams at companies like Samsung, Upwork, and Fractory choose Sisu.


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