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What Is Decision Analytics?

In the world of big data and cloud computing, no matter what sector you work in, you need to “read” what you see. Because companies or institutions have all the tools, they can understand their customer and the market better to plan the future. Fortunately, thanks to the technology, it takes only a few clicks to store the data. However, the challenge here is to analyze and turn that data into something useful and to do it on time. At this point, decision analytics comes forward as it seeks out beneficial insights to realize decision-making and say what could happen. That’s why farsighted companies start to value analytics for decision-making and hire decision analysts to grab the industry’s trends and make the data work for their business. That data may be catchy on its own, but it means nothing if they are at talented hands and not used in the decision-making process. For the risks to be fully understood, the data behind the decision should be analyzed and used well.

As the decision analysis is based on research and systematic modeling, it mostly means the improvement of the quantitative value of risks and opportunities along with human judgment. Now, let’s see the types of decision analytics for a better understanding.

The Types of Decision Analytics

Now that we know the answer to “What is decision analytics?”, we can jump onto the types of it.

  • Research: This is the development stage before really making the decision. You gather the relevant information by analyzing the problem and possible solutions. After that, identifying the risks and making a good plan to overcome them will be your next step. That’s why research is quite important for decision analytics and should not be avoided.
  • Risk Analysis: Every decision has its own risks. The point is to see and analyze them properly. Risk analysis is essential to determine a way to reduce those risks. Individually or as a team, you may even model risk triggers in some cases to better understand the hazard and be prepared. Remember that working as a team on a complex decision generally works. Thumbs up for Wisdom of crowds!
  • Decision Modeling: As the name implies, decision modeling is the process of modeling the structure of the decision and has four steps: identifying, describing, specifying, and refining.
    1. Identifying: Determining the decision that is the focus of the project.
    2. Describing: Explaining the decision and its impacts on the business objectives.
    3. Specifying: Stating all the requirements and the necessary knowledge crystal-clear to make a solid decision.
    4. Refining: Fine-tune the qualifications of the decision using an easy-to-understand diagram. After identifying some other decisions that need to be described, you can proceed.
  • Systems: The use of certain software to process the decision-related data. For example, you may benefit from decision-making tools to help you and the whole team you are a part of.

The Methods Used in Decision Analytics

In this part, you will hear about the most known and applicable decision analytics techniques, which are predictive modeling, simulation modeling, optimization modeling, prescriptive modeling, and business intelligence. Let’s see them a bit closer.

  • Predictive Modeling: Predictive modeling is a statistical technique used to predict future behavior by identifying risks and opportunities via data patterns. Here, the talent is to read the data at hand properly and deduce from them relevantly.
  • Simulation Modeling: Simulation modeling uses the creation and analysis of several options before the implementation stage. Decision Analytics promotes this decision analytics technique in the organizational context for examining and comparing alternative decisions so that the solution will be seen clearly.
  • Optimization Modeling: This term is also used in mathematics to explain a complex equation’s optimal maximum or minimum value. Due to decision analytics, it refers to the research that supports decision-makers employing various optimization models.
  • Prescriptive Modeling: Decision analytics accepts the combination of optimization technology and prescriptive models to reveal beneficial solutions for decision-makers. For example, it is well known that marketing and sales staff have access to large amounts of data that can help them determine a certain strategy. What types of products pair well together? How to price these products? Prescriptive analytics comes to the scene as it allows marketers and sales teams to be more precise with their campaigns and customer reach. Because they no longer have to behave on simply intuition and experience but the facts.
  • Business Intelligence: Business intelligence leverages software and services to transform data into actionable insights that inform an organization’s business decisions.

How Can You Form a Decision Tree Analysis?

Decision tree analysis typically starts with a single node which branches into possible outcomes. Every outcome branches again into additional nodes, and those nodes reveal other possibilities. These all give a tree shape to the diagram.

In this decision analysis tree, there must be three types of nodes: chance nodes, decision nodes, end nodes.

  • Chance node shows the probabilities of specific results and is represented by a circle.
  • Decision node shows a decision to be made and is represented by a square.
  • End node shows the final step of the decision path.

Whenever you have something complicated in mind, you can draw this decision analysis tree. To start, you need to pick a medium. You can either draw it by hand on a paper or you can use special software. Here is a quite simple example:

Let’s say that you are thinking about selling leather wristbands online. There are two alternatives to select from, “self-manufacturing the leather wristband” and “finding a manufacturer and outsourcing it”. The square part of the decision analysis tree will have these two branches. The development costs of these two alternatives will be shown on the branches for those alternatives. At the right of the development costs are small circles which represent the uncertainty about whether the outcome will be a success or a failure. The branches to the right of each circle will show the possible outcomes and, on the branches, the sales revenue will be shown for each alternative (assuming either success or failure for this case). Finally, the net profit will be shown at the far right of the tree for each possible combination of alternatives.

As you can see, the decision analysis tree is easy-to-use and beneficial to see the risks and possibilities of a complex decision.

Decision Analysts vs. Decision-Makers

Before closing up, it will be good to hear about what the main difference between the thoughts of decision-makers and decision analysts is.

Decision Analytics Affinity Group (DAAG) sets conferences annually in the United States, and many decision professionals attend the event every year. In one of those conferences, a group of 90 decision professionals pitched at three commandments on decision analytics for decision analysts: embed decision analysis, focus on results, incorporate human judgment. Generally, while decision analysts care more about the quality of a decision, the decision-makers emphasize the outcome. Decision-makers can rely on analytics and facts. However, they can overcome this trap and leverage the ability of synthesis by incorporating human judgment. What we get, as a result, these three commandments say that decision analysts have
a vital role in the world for the sake of decision-making. If you want to know more about DAAG conferences, you may click here and see all the presentations done in previous events held by worldwide decision professionals.