Decision Support Systems Applications and Uses

What is a decision support system?

A decision support system (DSS) is a computer-based information system that organizes, collects and analyzes business data. Decision-makers use this data to help them better manage and plan their organization or business.

A DSS typically gathers information such as sales figures, projected revenue and inventory data, which are organized into relational databases. The information it analyzes can come from multiple sources, such as documents, raw data, management, business models and employees’ personal knowledge.

“They take the guesswork out of decision-making by turning raw data into actionable insights,” said Arias WebsterBerry, CEO of WebsterBerry Marketing. “They streamline workflows, improve accuracy, and allow businesses to anticipate market shifts or customer behaviors with predictive modeling. For us, DSS has been a cornerstone in optimizing marketing campaigns and maximizing ROI.”

DSS applications can be used in various fields, including credit loan verification, medical diagnosis, various types of business management, and evaluating bids on engineering, agricultural, and rail projects. Make sure the data you feed into a DSS is clean and up-to-date as its accuracy will affect the quality of the results it delivers.

Types of decision support systems

While there is a DSS application for nearly every decision-making process, most of these tools fall into one of five categories.

Document-driven DSSs

Document-driven DSSs are widely used and allow users to search for information in internal and external databases — including the internet — using keywords. They sift through structured and unstructured data in documents, like profiles, ratings and financial spreadsheets. These systems are typically found online and in electronic files. [Read related article: The Best Spreadsheet Software]

Data-driven DSSs

Like document-driven DSSs, data-driven DSSs use quality data to determine a course of action based on a systematic process. They strategically break down questions and goals into pieces based on data.

“Decision support systems are designed to help businesses make informed decisions by taking large amounts of data and analyzing trends to provide insights and make recommendations to bring the highest value decisions to the forefront,” explained Brittany Hart, CEO of Salesforce consulting firm Platinum Cubed. This includes tasks such as analyzing sales trends to predict future demand or identifying high-risk customers, opportunities or employees.

For example, a business owner wanting to purchase additional equipment to expand capacity could use one of these systems by looking at any data that supports this decision. Revenue, how frequently current equipment is used and the efficiency of current operations are some factors the owner could consider. By using a data-driven DSS, the owner can analyze ways to collect data to assess these factors and use the findings to make a decision on purchasing additional equipment.

Hardik Chawla, senior product manager in technical at Amazon, told business.com that DSS tools “excel at uncovering subtle correlations that traditional analysis might miss, such as links between supplier delivery times and production bottlenecks.” He stressed that layering analytical capabilities — real-time monitoring for daily decisions and statistical tools for process improvements — unlocks the full potential of DSS.

As you build familiarity with your DSS systems, consider integrating data from multiple departments like finance, marketing and procurement. Adjoining databases in this way and analyzing them can uncover even richer insights.

Knowledge-driven DSSs

Knowledge-driven DSSs are mainly used by managers to find recommendations or suggestions for detailed problem-solving. These computer-based systems use artificial intelligence (AI) and human intellect to examine how issues of a problem are connected. They’re capable of making suggestions regarding how to act and recommending supporting material on a particular issue to users. They can also employ data-mining methods to make predictions for tests or studies and look at patterns to use for marketing plans.

Hart also pointed out the importance of DSS tools when creating personalized customer experiences: “Crafting a personable customer experience based on previous customer behavior or preferences” is a key application of these systems, especially in industries like financial services, where understanding client profiles is crucial.

Model-driven DSSs

Model-driven DSSs help users make choices and analyze decisions. They use models in areas like finances, simulations and statistics to present possible options. Managers and staff use these tools to better understand the potential outcomes of a particular decision.

These systems use databases, but they are typically smaller than the ones used in data-driven DSSs. Simple processes use one model to look at basic decisions.

By combining these models with historical data and real-time inputs, you can spot likely outcomes and find the most efficient way to reach them — even on complex decisions.

Communication-driven DSSs

Teams use communication-driven DSSs to work together better. They facilitate communication and information sharing during the decision-making process.

Software and technology — such as video conferencing, internal communication apps, and other network and online platforms — are examples of communication-driven DSSs. They allow teams to consider choices and select options while meeting virtually and receiving quick responses from team members.

Specific uses for DSSs in business

Managers can use DSS software in different ways. Typically, business planners will build custom DSSs to evaluate specific operations. These include inventory management, in which DSS applications can provide guidance on establishing supply chain movement. Also included are sales, in which DSS software helps managers predict how changes may affect results.

To manage inventory

DSSs are helpful in evaluating inventory. This process can increase a business’s cash flow and profitability by predicting demand for particular products and itemizing assets.

To aid sales optimization and sales projections

Decision support technology can also analyze sales data, make predictions and monitor existing revenue patterns. Planners can use the technology to tackle sales numbers using a variety of decision-support resources.

Hart recommends businesses outline their decision-making processes to get the most out of DSS tools. “To effectively leverage and integrate a DSS into their business, companies should first focus on outlining and engineering their business processes and what daily decisions bring the most value to their customers or employees,” she said.

To optimize industry-specific systems

Other uses for DSSs include projecting a business’s future or gaining a bird’s-eye view of its performance. These insights help owners and managers navigate difficult situations better, especially when they have reliable information to predict expenditures and revenues.

For example, in the financial services sector, DSSs can use preference data from similar clients and match them with the best investment strategies to help them reach their goals, said Hart.

Examples of DSSs

We all use DSSs in our personal and business lives every day. For example, every time you use Google, you’re using a highly sophisticated DSS that organizes a massive amount of information in a searchable, retrievable format. It can locate the specific images, videos and text files you need to help your business achieve more.

GPS tracking is another type of DSS. As you can see in our Verizon Connect Fleet Management review, its software allows drivers to determine the best and quickest route between two points; plus, it does so while monitoring traffic conditions and helping them avoid congestion.

These are some other uses of DSS, including:

• Agriculture: Farmers use DSS tools for crop planning to help them determine the best times for planting, fertilization and harvesting.
• Medicine: Clinical DSS technology has many uses: maintaining research information about chemotherapy protocols, preventive and follow-up care, and monitoring medication orders. DSSs are also used with medical diagnosis software.
• Weather forecasting: Some states use DSSs to provide information about potential future hazards, such as floods. To do this, they factor in real-time weather conditions, floodplain boundaries information and historic county flood data.
• Real estate: Real estate companies use DSSs to manage data on comparable home prices and acreage.
• Education: Universities and colleges use DSSs to determine the number of students enrolled. This information helps them predict the number of students who will register for particular courses or whether the student population is sufficient to meet the university’s costs.

Future trends for DSS

Hart said AI is revolutionizing DSS tools. “Technologies are constantly evolving to incorporate AI to offer prebuilt models to train and surface key insights to users without having to build these decision models from scratch,” she explained.

WebsterBerry offered that deep AI integration at the API level could be the next wave. “Making data actionable and easy to understand is the key to proliferation,” he said. “Cloud-based platforms that offer native integrations to DSS systems will own tomorrow. Add to that user-friendly interfaces, and you have systems that are accessible and impactful for businesses of all sizes.”

Chawla outlined three key trends shaping the future of DSS: “First, the integration of large language models for natural interaction with systems. Second, enhanced contextual awareness through multi-modal data processing. Last, responsible AI principles built directly into decision frameworks.”

He added, “We’re seeing systems that can now understand operational context, process multiple data types (text, images, sensor data), and provide natural language interfaces to complex analytics.”

How to choose the right DSS for your business

DSS implementation success lies in aligning it with your business goals, according to WebsterBerry. “Start with clear objectives, provide training for your team and choose a system that grows with your business,” he suggested. “A well-integrated DSS isn’t just a tool — it’s more like a second set of eyes in strategic decision-making.”

When selecting or customizing a DSS, Hart said a business should compare “each DSS’s ability to integrate, customize, and automate according to their goals and needs.” Firms should pay particular attention to integration capabilities, flexibility, automation features and the user experience.

Shayna Waltower contributed to this article.