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Samenvatting tentamenstof Business Analytics
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By: majamilanovic • 7 months ago
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BA samenvattingWeek 1
Hoofdstuk 1
Watson technology how the vast amounts of data now available on
the internet can be used to make more data-driven, smarter decisions.
Example of AI.
AI the use of data and computers to make decisions that would have in
the past required human intelligence.
Three developments spurred explosive growth in the use of analytical
methods
1. Technological advances
2. Ongoing research has resulted in numerous methodological
developments
3. Methodological developments were paired with an explosion in
computing power and storage capability
solving big problems more quickly and accurately than ever before
BA is objective because it is data driven it has a certain amount of
subjectiveness because of the analyst.
1.1
Managers make strategic, tactical, or operational decisions
strategic decisions involve higher-level issues concerned with the
overall direction of the organization.
- Responsibility of higher-level executives.
- They have a time horizon of three to five years. In the TRC case also
establishing retail stores in malls and downtown areas of major
cities.
Tactical decisions Mid-level, concern how the organization should
achieve the goals and objectives set by its strategy
- Responsibility of midlevel management. How many new stores will
have to be opened, where to open these stores etc.
Operational decisions how the firm is run from day to day
- Responsibility of operations managers. Make day to day decisions
like how many pairs of each model and size of shoes to order from
the distribution centers etc.
Decision making:
Identify and define problem determine the criteria that will be used to
evaluate alternative solutions determine the set of alternative solutions
evaluate the alternatives choose alternative.
1.2
What makes decision making difficult and challenging?
1. Uncertainty
2. Facing enormous number of alternatives that we cannot evaluate all
,Analytics a broader category than business analytics, encompassing
the use of analytical techniques in the sciences and engineering as well.
Business analytics the scientific process of transforming data into
insight for making better decisions. Used for data-driven of fact-based
decision making BA can help us make better informed decisions.
Researchers concluded that firms guided by data-driven decision making
have higher productivity and market value and increased output and
profitability.
1.3
Analytics comprises three broad categories of techniques
1. Descriptive analytics encompasses the set of techniques that
describes what has happened in the past. Data queries, reports
a. Data query: a request for information with certain
characteristics from a database -> can be used to find
patterns of relationships in a large database.
b. Data dashboards: are collections of tables, charts, maps, and
summary statistics that are updated as new data become
available -> they help the management monitor specific
aspects of the company’s performance.
c. Data mining: the use of analytical techniques for better
understanding patterns and relationships that exist in large
data sets. For example, by categorizing certain words as
positive or negative.
2. Predictive analytics techniques that use models constructed
from past data to predict the future or ascertain the impact of one
variable on another.
a. Data mining -> studying historical point-of-sale data to predict
future sells
b. Regression
c. forecasting
d. Simulation -> involves the use of probability and statistics to
construct a computer model to study the impact of uncertainty
on a decision.
3. Prescriptive analytics indicates a course of action to take, the
output of this model is a decision. Predictive models provide a
forecast or prediction but not a decision. However, a prediction +
rule = prescriptive model these are often referred to as rule-
based models.
a. Portfolio models in finance -> use historical investment return
data to determine which mix of investments will yield the
highest expected return while controlling or limiting exposure
to risk.
b. Supply network design models in retailing -> plant and
distribution center locations that will minimize costs while still
meeting customer service requirements.
c. Price-markdown models in retailing -> yield revenue-
maximizing discount levels and the timing of discount offers
when goods have not sold as planned.
, all these models are called optimization models: models that give
the best decision subject to the constraints of the situation.
Another type of modeling is
Simulation optimization -> combines the use of probability and
statistics to model uncertainty with optimization techniques to find
good decisions in highly complex and highly uncertain settings.
Decision analysis -> used to develop an optimal strategy when a
decision maker is faced with several decision alternatives and an
uncertain set of future events.
Utility theory -> assigns values to outcomes based on the decision
maker’s attitude toward risk, loss, and other factors.
Predictive + prescriptive analytics = advanced analytics.
1.4
Big data any set of data that is too large or too complex to be handled
by standard data-processing techniques and typical desktop software.
The four Vs of Big Data
- Volume hoeveelheid en bestandsgrootte (data at rest)
- Velocity de snelheid waarmee data beschikbaar komen en
geanalyseerd worden (data is motion)
- Variety verschillende vormen van data zoals tekstdata,
audiodata, videodata, GPS-data, socialmediadata, zowel
gestructureerd als ongesctructureerd (data in many forms)
- Veracity onzekerheid in de data, bijvoorbeeld met betrekking tot
missende waarnemingen, inconsistentie en betrouwbaarheid van de
gegevens (data in doubt)
- Value (zou kunnen worden toegevoegd)
The four Vs indicate that big data creates challenges in terms of how these
complex data can be captured, stored, processed, secured and then
analyzed. Because of the volume it is not always possible to store all the
data on one single computer this led to Hadoop: an open-source
programming environment that supports big data processing through
distributed storage and distributed processing on clusters of computers.
MapReduce is a programming model used within Hadoop, it performs two
major steps:
- the map: divides data into manageable subsets and distributes it to
the computer in the cluster for storing and processing.
- the reduce steps: collects answers form the nodes and combines
them into an answer to the original problem.
Technologies like these enable cost-effective processing of big data.
Sources of Big Data are publicly available but much of it is private
information.
Data security the protection of stored data from destructive forces or
unauthorized users, is of critical importance to companies.
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