Week 12

 In this lecture, we learn the factors that cause a project to be successful and failed, following is some additional information I found about project management,

The meaning of project

·        The entire process required to produce a new product, new system or other specific result.” [Archibald, 1992]

·        A narrowly defined activity which is planned for a finite duration with a specific goal to be achieved.” [General Electric]

5 types of projects

·        Derivitive Projects—involving small changes to existing products and systems (incremental innovation).

·        Breakthrough Projects—those which create new markets or products and require significant resources and a strategic view (e.g. digital camera).

·        Platform Projects —projects which involve significant incremental improvements but still linked to same basic platform (e.g. VCR)

·        R&D Projects—future oriented, speculative but exploring where the company might be in five years or more (NASA).

·        Alliances—cross-company projects, designed to share costs and risks, but also posing problems of cooperation and coordination (e.g. Concord)

Week 11

In the last lecture, we have been taught something about Balanced Scorecard, following is some more information that I found about Balanced Scorecard,

What is Balanced Scorecard

It was developed by Drs. Robert Kaplan in the early 1990’s, in order to provide a clear list of what companies should measure in order to have a balance from the financial point of view.

The balanced scorecard can be used to clarify the vision and strategy of the organization, and also provide feedback to both the internal business process and the outcomes. If the balance scorecard is properly applied, it can translate the academic management theory into real business world practice.

The balanced scorecard aim at viewing the organization according to four different perspectives,

o        The learning and growth perspective

o        The business process perspective

o        The customer perspective

o        The financial perspective

, and then develop a metric, and collect the data and analyze the data according to the above four perspectives,

Week 10

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions).

Stage 1: Exploration. This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") - performing some preliminary feature selection operations to bring the number of variables to a manageable range (depending on the statistical methods which are being considered). Then, depending on the nature of the analytic problem, this first stage of the process of data mining may involve anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyses using a wide variety of graphical and statistical methods (see Exploratory Data Analysis (EDA)) in order to identify the most relevant variables and determine the complexity and/or the general nature of models that can be taken into account in the next stage.

Stage 2: Model building and validation. This stage involves considering various models and choosing the best one based on their predictive performance (i.e., explaining the variability in question and producing stable results across samples). This may sound like a simple operation, but in fact, it sometimes involves a very elaborate process. There are a variety of techniques developed to achieve that goal - many of which are based on so-called "competitive evaluation of models," that is, applying different models to the same data set and then comparing their performance to choose the best. These techniques - which are often considered the core of predictive data mining - include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and Meta-Learning.

Stage 3: Deployment. That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

The concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty. Recently, there has been increased interest in developing new analytic techniques specifically designed to address the issues relevant to business Data Mining (e.g., Classification Trees), but Data Mining is still based on the conceptual principles of statistics including the traditional Exploratory Data Analysis (EDA) and modeling and it shares with them both some components of its general approaches and specific techniques.

However, an important general difference in the focus and purpose between Data Mining and the traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the basic nature of the underlying phenomena. In other words, Data Mining is relatively less concerned with identifying the specific relations between the involved variables. For example, uncovering the nature of the underlying functions or the specific types of interactive, multivariate dependencies between variables are not the main goal of Data Mining. Instead, the focus is on producing a solution that can generate useful predictions. Therefore, Data Mining accepts among others a "black box" approach to data exploration or knowledge discovery and uses not only the traditional Exploratory Data Analysis (EDA) techniques, but also such techniques as Neural Networks which can generate valid predictions but are not capable of identifying the specific nature of the interrelations between the variables on which the predictions are based.

Data Mining is often considered to be "a blend of statistics, AI [artificial intelligence], and data base research" (Pregibon, 1997, p. 8), which until very recently was not commonly recognized as a field of interest for statisticians, and was even considered by some "a dirty word in Statistics" (Pregibon, 1997, p. 8). Due to its applied importance, however, the field emerges as a rapidly growing and major area (also in statistics) where important theoretical advances are being made (see, for example, the recent annual International Conferences on Knowledge Discovery and Data Mining, co-hosted by the American Statistical Association).

Week 9

In the last lecture, Chris has shown us details on the Data mining. In the current business environment, management has to use the different data to do some statistical analysis, such on-line analytical processing (OLAP) can be performed with the Data-mining tools, which can help to identify some of different patterns from the data, which can hardly be identified with the simple SQL, and which can also classify data into different categories.

OLAP and Data mining will not modify the existing data, they will just make use of the existing data to do some analytical task. With the concept of Data warehousing, which integrate different data sources together in a manner that facilitate the query processing, OLAP and Data mining can provide the user with efficient way to analyszw the data.

Data warehousing can provide multi-dimensional view of the data, for 3-dimenionsioanl model, it is called data cubes, for data model of more than 3-dimensional, it is called hypercubes.

With the availability of different facilities, Data warehousing can facilitate the processing of the query,

• Pivot: Data cubes can be visualize in different ways.
• Roll-up Display: Data can be summarized and group along a dimension.
• Drill-Down Display: Data can be disaggregated along a dimension
• Slice and Dice: Smaller data cube can be created from a larger data cube.
• Selection: Similar to the SQL
• Sorting: Data can be order along a dimension
• Derived Attributes: Attributes can be specified for the data stored.

 Data mining & OLAP