Data Analysis & Modeling

Mar 26, 2017
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There are various schools of thought about how to define terms, some arising from professional terminologists and academia. But those approaches are often relatively arcane and not well-suited to everyday business practice.

Definitions with subtle IT or ‘data’ bias are an anathema to effective communication with business partners. Good business definitions are oriented to what words mean when used by real business people talking directly about real business things.
Jan 29, 2017
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Business Data Diagrams are one of those MUST HAVE models for any product that is dealing with data. The exercise of creating the model itself creates a powerful, shared understanding of the underlying data constructs as users understand it. Instrumental in helping to identify additional, more detailed models that might be needed, the BDD can also help you get to a complete set of user stories around users interacting with the data following the create, use, edit, delete, move and copy actions.
Jul 27, 2016
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Opportunities abound for new and experienced analysts to advance their careers if they are willing to increase their knowledge of analytics, metrics, and Business Intelligence. In the meantime, following these five steps will help get you and your organization on your way to being a well-measured one.
Jul 24, 2016
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Business analysts’ performance and how we could measure it appeared to be a hot topic of interest for the Business Analysis community.   “If you can’t measure it, you can’t manage it” (Peter Drucker) – this statement hides lots of questions for the Business Analysis managers, like what to measure, how to assess, quantifiable or qualifiable metrics to use? 
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This article explores strategy mapping as discussed within Business Architecture Guild BIZBOK, and attempts to extend the discussion by defining a set of information and graphical principles that allows strategy to be represented graphically.
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Data migration is typically the most forgotten or underestimated component of an IT project which is the process of making a copy of data and moving it from one system to another, preferably without disrupting or disabling active business processes. On some occasions, it is not easy to understand that a data migration is needed in the project and ...
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Adoption of The Decision Model (TDM) is growing and includes major corporations, such as those in the financial industry. As a result, decision models based on TDM are operating in production worldwide on behalf of critical business transactions and some with huge transaction volumes.  This means there are organizations and people with several years of TDM experience. However, there are also people and organizations interested in TDM and contemplating how to get started. This article provides insights by which business analysts can plant the seed for TDM.

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What is a data normalization? Normalization ensures that each attribute is assigned correctly to its proper entity. And that the attribute does not repeat within an entity and is fully dependent on its primary key and nothing but the key. Make sense? If not, let’s translate the gibberish into business language.
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This is a complete example of a Business/Systems Analysis Model using UML and including: Use Cases, Activity Diagrams, Context Diagram, and more.
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Of all the reasons for data quality problems, I find user interface design issues to be the most interesting. This may be because it is tied to the software requirements, and I have worked as a requirements analyst for a number of years.
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The TDM business glossary is the home for naming conventions, data types, and domain values that are meaningful to, and defined by, the business audience. Best of all is that resulting decision models are exactly as the business community wants and in the business’s own terminology.

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Now we delve into data modeling, one of the core model types. We choose to start here because data requirements are an important foundation for most information technology projects. If you are a business analyst and not doing data modeling today, you should be able to at least read them to validate requirements against what a data modeler has created and our bias is that business analysts can and should be doing “functional” data modeling.

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In the real world, good decision modeling is always a balance between science and art. The science is systematic decomposition of a structure (of data or logic) into a set of smaller structures based on the definitions of successive normal forms. The art, on the other hand, is a general decomposition into a set of smaller structures based on factors not related to detecting and correcting normalization errors.

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The purpose of this brief article is to provide a simple example on how to link and verify four models: use case, data flow diagrams, entity relationship diagrams, and state diagrams. Note the word verify, not validate. Verify in this context means that the technique is consistent and complete, not that it reflects correct requirements.

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Today, it is very common for organizations to use The Decision Model for managing DQ logic. The results are impressive and also deliver unique advantages over other approaches. In some cases, organizations represent DQ logic in The Decision Model as part of requirements deliverables. In other cases, organizations create DQ logic in TDM-compliant software which validates the logic against TDM principles, generates and executes test cases, and sometimes deploys to target technology.

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