Data Analysis & Modeling

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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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Have you ever been confused about why you were not allowed to do what you tried to do? Been judged or evaluated in a way you didn’t expect? Stumped by the result or decision a business system produced? If so you are a ‘why victim’.
 

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 Analytics drive key strategic decisions in major corporations every day. However most legacy tools and solutions that help companies make these critical strategic decisions, simply aren’t built to deal with the reality of today’s modern business environment. Below are some essential questions to ask as you assess the potential benefits and limitations of new strategic analytic platforms for your organization.
 

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Predictive Analytics can play a massive role in the simplification of Big Data. The role it plays comes down to getting the most out of this Big Data, and it starts with what some people refer to this as the fourth “V” of Big Data (Velocity, Variety, Volume), and that is Value. Predictive Analytics takes all of the data available and can pull the most important elements of that data, helping to create a predictive model or create an ideal profile to help organizations in many different aspects of the business.
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A missing ingredient in most current approaches to IT requirements and business rules is developing a standard business vocabulary, a concept model. Every business analyst should be familiar with the technique – it’s simply about clear thinking and unambiguous communication. What are basic constructs in developing a concept model? This article discusses four prefabricated elements of structure, ones that will enable you to build a complete and robust business vocabulary.

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All paths to organizational decision modeling encounter a common question: How do you introduce The Decision Model into an organization? More specifically, how do you gain management attention for delivering decision models as a standard practice? This month’s column addresses that question.
 

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