Decisions support system exam

Decisions support system exam Decision support system hoorcollege 1

Three definitions of DSS

• Decision support system is an umbrella term to describe any computerized system that

supports decision/making in an organization

• Data driven decision-making
• Transforming data into meaningful information/knowledge to support business decision-

making.

So first you have data, like some numbers in a table. Then you transform it. Then we see that it is a social security number. Then you transform is again and you can link the social security number to an unique person.

Data

• Items that are the most elementary descriptions of things, events, activities, and

transactions. For example a customer in a sales database that makes an order.

• Internal or external. Internal is in a database that a company has for itself.
• Structured or unstructured.

Structured data is more important than unstructured data.

Information

• Organizational data that has meaning and value

Knowledge

• Processed data of information that is applicable to a business decision problem

Business analytics methods:

• Descriptive analytics; use data to understand past & present. Then you are working with

techniques like SQL programming. Or dashboarding, data wrap housing. So look at the current state of the data.

• Predictive analytics; predict future behavior based on past performance. Typically here we

have a historic data base. We work with techniques like regression or time series.

• Prescriptive analytics; make decisions or recommendations to achieve the best performance.

We have a different set of techniques. It is about organizational techniques.

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There are different ways to look at decision support systems. The previous is a toolkit approach to look at it. But now analytics is so developed that each business packet has created their own set of techniques. Then we have a different classification of analytics, and that is based on function. That

means the function inside the business process:

• Marketing analytics
• Sales analytics
• HR analytics
• Financial analytics
• Supply chain analytics
• Accounting analytics
• Etc.

All these different functions get their data from one source. This is what we call the business information system. The ERP system/database. All kinds of computer systems that are often related that support all the processes in an organization. If you open up this business information system, you will find in the heart the database structure.

Business intelligence = data warehousing + descriptive analytics Business analytics = predictive + prescriptive analytics

Our view: business intelligence (BI) = business analytics (BA)

Are all Decisions support systems.

Two business intelligence definitions:

• Process definition: BI is an umbrella term that combines the processes, technologies and

tools needed to transform data into information, information into knowledge, and knowledge into plans that drive profitable business action.

• Product definition: BI is information and knowledge that enables business decision-making.

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Part 2: introduction to business intelligence & analytics

The historic word for the things we are going to discuss in this course is decision support systems, then we have analytics and intelligence. They are fighting for dominance.

One view:

- Business intelligence: data warehousing + descriptive analytics

- Business analytics: predictive + prescriptive analytics

Data warehousing = organizing all the data in such a way that it can be used for decision making.Descriptive analytics = the more basic techniques that show about the current and the past situation.About reporting and dashboarding mainly.

Business analytics focus more on the advanced models. It is more quantitative.

The view of the course:

Business intelligence (BI) = business analytics (BA)

Business intelligence are in the working place common good. Companies spend a lot of money on it.This area became popular and mature. The universities missed this development partly. Only the last ten years they have picked up this topic. They focus only on certain parts of analytics. They rebrand it to data science. To give it the academic touch.

Two definitions of business intelligence:

• Process: BI is an umbrella term that combines the processes, technologies, and tools needed

to transform data into information, information into knowledge, and knowledge into plans that drive profitable business action.Here it is about transforming. You have raw data, and with some tools and methods and models you can transform it to something else and then it can be consumed by decision makers.

• Product: Business intelligence is information and knowledge that enables business decision-

making.The output of the transformation process. It focuses on the key intellectual output of the process and where it is indeed used by decision makers to improve their everyday work.

This illustrates these two definitions. On one end we have BI process. Raw data and information are the input and then business intelligence knowledge is the key intellectual output. And this knowledge enables decision making at the core organization.So one focuses on the transformation and the other one on the output.BI intelligence solution: support the business intelligence process by utilizing the business intelligence tools. So for example: AH uses a lot of business intelligence analytics. They have a complete solution for all their information requirements. They have many different processes that are supported by this solution. In the solution they use all kinds of business intelligence tools. 3 / 4

These are some solutions.All this different tools can be used together in one solution.All these things have to work together to do something meaningful with data.

In the bottom there is a lot of data, stored in all kinds of databases. The processing increases and then we have information. Eventually we have knowledge.Next to these different layers, we could connect it to different classes of techniques.So on a data level we have database management, operational database, transaction databases. That are used to store the everyday operations of an organization.If you transform it, it becomes information. We have data warehouse, OLAP, dashboards.

Knowledge: artificial intelligence, knowledge

discovery, neural networks.

The pyramid is from the left to the right now. At the production part we have data.All kinds of databases that collect and represent this data. Then you have the

middle part: assembly, logistics and

storage: the information part of the

pyramid.

Then we have the top: processing, analysis

and consumption.And then the knowledge part where the decision maker uses all kinds of techniques that plug into data warehouse and then he can answer his business question.

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