What are the characteristics of a data warehouse that are needed to support the analytical needs of an organization?

With the growing scope of modern-day business, you can pull massive amounts of multidimensional 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.

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 is a decision support system (DSS)?

DSS helps businesses make sense of data so they can undergo more informed management decision-making.

Essentially, 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. Another example would be a bank using data from past experiences to increase their chances of detecting fraud.

What is a decision support system used for?

A DSS can be used in several ways:

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

Characteristics of a decision support system

DSS frameworks typically consist of three main components or characteristics:

1. 1. The model management system: 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 or summarization of all information including raw data, documents, and personal knowledge

What is a data warehouse?

A data warehouse is a collection of multidimensional, 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

Data warehouses and decision support

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

The four stages of data warehousing

Data warehouse development typically involves the following steps and requirements:

• • 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 data warehousing.
  • 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 eCommerce company).
  • 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.

Data warehouse vs. database

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 data warehouses and decisions support systems (DSS) 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 decision support system (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 makes them well-suited for conducting organization-wide data analyses and more informed decision-making.
  • Enhanced business intelligence: Data warehouse DSS can hone in on all the specific factors affecting your business performance, beyond just a top-line summarization of revenue metrics.
  • Data consistency: Since data warehouses use standardized processes to transform data from across an organization, data meets certain qualifications and is more consistent, cohesive, and accurate across the board.
  • Speed: Having all your data and queries in one place makes organization and automation easy, removing the need for manual analysis and testing.
  • Efficiency: Decision makers can quickly access all of a corporation’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 the Sisu Decision Intelligence Engine, just a few quick clicks are all you need to parse through multidimensional 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 Gusto choose Sisu.

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