Agile Modeling and the Rational Unified Process (RUP) Agile Modeling (AM) is a practices-based software process whose scope is to describe how to model and document in an effective and agile manner. The practices of AM should be used, ideally in whole, to enhance other, more complete software process such as eXtreme Programming (XP), the Rational Unified Process (RUP), Disciplined Agile Delivery (DAD), and the Enterprise Unified Process (EUP) to name a few. These processes cover a wider scope than AM, in the first three cases the development process and in the fourth the full software process including both development and production. Although these processes all include modeling and documentation activities, in one form or the other, there is definitely room for improvement. With DAD the practices of AM are built right into the framework, with XP the modeling processes should be better defined, and with RUP modeling processes could definitely stand to be made more agile. How Modeling Works in the Unified Process Figure 2. Table 1.
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. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. 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, and fact-based management to drive decision making. It is therefore closely related to management science. In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Examples of application Types of analytics
Redirecting to Discover how PRINCE2 can help you achieve your goals and manage projects effectively. Use the quick links to scroll to a relevant section. PRINCE2 Definition PRINCE2 (an acronym for PRojects IN Controlled Environments) is a de facto process-based method for effective project management. Used extensively by the UK Government, PRINCE2 is also widely recognised and used in the private sector, both in the UK and internationally. The PRINCE2 method is in the public domain, and offers non-proprietorial best practice guidance on project management. Key features of PRINCE2: Focus on business justification Defined organisation structure for the project management team Product-based planning approach Emphasis on dividing the project into manageable and controllable stages Flexibility that can be applied at a level appropriate to the project. PRINCE2 History When PRINCE was launched in 1989, it effectively superseded PROMPT within Government projects. How PRINCE2 Can Benefit You or Your Organisation?
Digital Transformation of Business and Society Let’s explore our emerging future together at Frank Diana’s Blog Reimagine the future through video on Youtube Updated December 14, 2018: The anchor visual in this post continues to evolve. Considering the increased traffic to the post, the visual has been updated. Updated July 26, 2018: The anchor visual in this post has been updated several times to reflect new and emerging future scenarios, The current version can be found Here. 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”. 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. Gerd has been using the term “Hellven” to represent the two paths technology can take. Source: B.
Elements for a Successful Project Kickoff Meeting There are many elements to running a successful kickoff meeting. I’ve tried to capture those key elements that in my experience make the meeting a success. No surprises – how to preach to the choir The first element that I can’t emphasize enough is, “No surprises”. Key project members, especially project sponsors and stakeholders, should already know details. The Kickoff Agenda – don’t get derailed It is always advisable, for any scheduled meeting, to have an agenda. Following the theme of no surprises it is a good idea to review the agenda with key meeting participants before the kickoff begins. The detailed topic areas below are a great start to your kickoff meeting agenda: Introductions After a quick review of the kickoff meeting agenda I always go around the room and have everyone do introductions. Introductions carry special weight in larger organizations because teams and team members may work in different divisions or work groups that have little or no exposure to each other. Roles
Online analytical processing Online analytical processing, or OLAP (/ˈoʊlæp/), is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture. The term OLAP was created as a slight modification of the traditional database term online transaction processing (OLTP). OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives. Overview of OLAP systems The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. For example: Multidimensional databases Aggregations
Project Management for Today’s and Tomorrow’s Event Planner - Event Manager Blog Event professionals and industry associations have acknowledged it for many years. Project Management (PM) tools and methodologies are an asset to Event Management. Why? Planners, Listen Up! I proclaim that companies are beginning to catch-up on the trend. Step Into The Role Of The Modern Event Professional! A modern event professional does more than develop an agenda, secure a location, or select a caterer. Take On The Right Responsibilities! It’s the planner’s responsibility to follow event industry standards and best practices. Try These Project Management Tools For Event Success! Employing PM methodologies can have a big impact on event success. Statement of Work: Assures everyone is on the same page by defining the scope. In Conclusion Now, more than ever, planners need to integrate project management principles into their daily routines. The many advantages of applying project management methodologies to event planning are finally being realized. “Photo by Leo Reynolds“
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. The hypercube is the special case of a hyperrectangle (also called an n-orthotope). A unit hypercube is a hypercube whose side has length one unit. Construction A diagram showing how to create a tesseract from a point. An animation showing how to create a tesseract from a point. A hypercube can be defined by increasing the numbers of dimensions of a shape: 0 – A point is a hypercube of dimension zero. 1 – If one moves this point one unit length, it will sweep out a line segment, which is a unit hypercube of dimension one. 2 – If one moves this line segment its length in a perpendicular direction from itself; it sweeps out a 2-dimensional square. 3 – If one moves the square one unit length in the direction perpendicular to the plane it lies on, it will generate a 3-dimensional cube. This can be generalized to any number of dimensions. . .
MS Project 2013 Upload Ric French Loading... Working... ► Play all MS Project 2013 Ric French25 videos803 viewsLast updated on Jan 25, 2015 Play all Sign in to YouTube Sign in History Sign in to add this to Watch Later Add to Loading playlists... OLAP cube An example of an OLAP cube An OLAP cube is a term that typically refers to multi-dimensional array of data. OLAP is an acronym for online analytical processing, which is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3. Terminology A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. Slicer is a term for a dimension which is held constant for all cells so that multi-dimensional information can be shown in a two-dimensional physical space of a spreadsheet or pivot table. Each cell of the cube holds a number that represents some measure of the business, such as sales, profits, expenses, budget and forecast. OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Hierarchy
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. A database which was designed for OLTP will not function well as a ROLAP database. ROLAP vs. Advantages of ROLAP Disadvantages of ROLAP Performance of ROLAP OLAP Survey Trends
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. Advantages of MOLAP Disadvantages of MOLAP Within some MOLAP Solutions the processing step (data load) can be quite lengthy, especially on large data volumes. Trends Products See also
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. Vertical partitioning In this mode HOLAP stores aggregations in MOLAP for fast query performance, and detailed data in ROLAP to optimize time of cube processing. Horizontal partitioning In this mode HOLAP stores some slice of data, usually the more recent one (i.e. sliced by Time dimension) in MOLAP for fast query performance, and older data in ROLAP. Products  Jump up ^ Owen Kaser and Daniel Lemire, Attribute Value Reordering for Efficient Hybrid OLAP, Information Sciences, Volume 176, Issue 16, pages 2279-2438, 2006.