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EAE MIB - Master International Business

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Structure for 6 sessions. Functional Database Model. The functional database model is used to support analytics applications such as Financial Planning and Performance Management.

Functional Database Model

The functional database model, or the functional model for short, is different from but complementary to, the relational model. The functional model is also distinct from other similarly named concepts, including the DAPLEX functional database model,[1] and functional language databases. The functional model is part of the online analytical processing (OLAP) category since it comprises multidimensional hierarchical consolidation. But it goes beyond OLAP by requiring a spreadsheet-like cell orientation, where cells can be input or calculated as functions of other cells. Also as in spreadsheets, it supports interactive calculations where the values of all dependent cells are automatically up to date whenever the value of a cell is changed. Predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.

Predictive analytics

Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Data mining. Data mining is an interdisciplinary subfield of computer science.[1][2][3] It is the computational process of discovering patterns in large data sets ("big data") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries.

Data mining

Business analytics. Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.[1] Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods.

Business analytics

In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling,[2] and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. HOLAP. HOLAP (hybrid online analytical processing) is a combination of ROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) which are other possible implementations of OLAP.


HOLAP allows storing part of the data in a MOLAP store and another part of the data in a ROLAP store, allowing a tradeoff of the advantages of each. The degree of control that the cube designer has over this partitioning varies from product to product. MOLAP. MOLAP (multidimensional online analytical processing) is an alternative to the ROLAP (Relational OLAP) technology.


While both ROLAP and MOLAP analytic tools are designed to allow analysis of data through the use of a multidimensional data model, MOLAP differs significantly in that (in some software) it requires the pre-computation and storage of information in the cube — the operation known as processing. Most MOLAP solutions store these data in an optimized multidimensional array storage, rather than in a relational database (i.e. in ROLAP).

There are many methodologies and algorithms for efficient data storage, aggregation and implementation specific business logic with a MOLAP Solution. As a result there are many misconceptions to what the term specifically implies. MOLAP vs. ROLAP. ROLAP (relational online analytical processing) is an alternative to the MOLAP (Multidimensional OLAP) technology.


While both ROLAP and MOLAP analytic tools are designed to allow analysis of data through the use of a multidimensional data model, ROLAP differs significantly in that it does not require the pre-computation and storage of information. Instead, ROLAP tools access the data in a relational database and generate SQL queries to calculate information at the appropriate level when an end user requests it.

With ROLAP, it is possible to create additional database tables (summary tables or aggregations) which summarize the data at any desired combination of dimensions. While ROLAP uses a relational database source, generally the database must be carefully designed for ROLAP use. OLAP cube. Hypercube. An n-dimensional hypercube is also called an n-cube or an n-dimensional cube.


The term "measure polytope" is also used, notably in the work of H. S. M. Coxeter (originally from Elte, 1912),[1] but it has now been superseded. Online analytical processing. In computing, online analytical processing, or OLAP (/ˈoʊlæp/), is an approach to answering multi-dimensional analytical (MDA) queries swiftly.[1] OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining.[2] Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM),[3] budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture.[4] The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP).[5] OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives.

Online analytical processing

Digital Transformation of Business and Society. Digital Transformation of Business and Society At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”.

Digital Transformation of Business and Society

I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner.

He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. Source: B. Lean vs 6Sigma. Methodology. What Is Six Sigma? Six Sigma – what does it mean? “Six Sigma is a quality program that, when all is said and done, improves your customer’s experience, lowers your costs, and builds better leaders. — Jack Welch Six Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving toward six standard deviations between the mean and the nearest specification limit) in any process – from manufacturing to transactional and from product to service. The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities.

The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. What is business analytics (BA)? - Definition from Business analytics (BA) is the practice of iterative, methodical exploration of an organization’s data with emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision making.

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