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Decision support system
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These are all the lecture notes of the course Decision Support System. This also contains a file with practice questions.
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Decisions support system examDecision 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:
1. 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.
2. 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.
3. Prescriptive analytics; make decisions or recommendations to achieve the best performance.
We have a different set of techniques. It is about organizational techniques.
,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.
,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.
, 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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