Techniques to Boost Your Data Modeling

Techniques to Boost Your Data Modeling:

·         Understand the Business Requirements and Results Needed - get a clear understanding of the requirements by asking people about the results they need from the data. Then start organizing your data with those ends in mind.

·         Visualize the Data to Be Modeled - Most people are far more comfortable looking at graphical representations of data that make it quick to see any anomalies or using intuitive drag-and-drop screen interfaces to rapidly inspect and join data tables. Data visualization approaches like these help you clean your data to make it complete, consistent, and free from error and redundancy.

·         Start with Simple Data Modeling and Extend Afterwards: Keeping data models small and simple at the start makes it easier to correct any problems or wrong turns. When you are sure your initial models are accurate and meaningful you can bring in more datasets, eliminating any inconsistencies as you go.

·         Break Business Enquiries Down into Facts, Dimensions, Filters, and Order: organizing your data using individual tables for facts and for dimensions, you facilitate the analysis for finding the top sales performers per sales period, and for answering other business intelligence questions as well.

·         Use Just the Data You Need, Rather Than All the Data Available: Portions of the data are needed to answer business questions. Ideally, you should be able to simply check boxes on-screen to indicate which parts of datasets are to be used, letting you avoid data modeling waste and performance issues.

·         Make Calculations in Advance to Prevent End User Disagreements: A key goal of data modeling is to establish one version of the truth, against which users can ask their business questions

·         Verify Each Stage of Your Data Modeling Before Continuing : Each action should be checked before moving to the next step, starting with the data modeling priorities from the business requirements.

·         Look for Causation, Not Just Correlation: Data modeling includes guidance in the way the modeled data is used. While empowering end users to access business intelligence for themselves is a big step forwards, it is also important that they avoid jumping to wrong conclusions. For example, perhaps they see that sales of two different products appear to rise and fall together. Are sales of one product driving sales of the other one (a cause and effect relationship), or do they just happen to rise and fall together (simple correlation) because of another factor such as the economy or the weather? Confusing causation and correlation here could lead to targeting wrong or non-existent opportunities, and thus wasting business resources.

·         Use Smart Tools to Do the Heavy Lifting: complex data modeling may require coding or other actions to process data before analysis begins. However, if such “heavy lifting” can be done for you by a software application A suitable software product can facilitate or automate all the different stages of data ETL (extracting, transforming, and loading). Data can be accessed visually without any coding required, different data sources can be brought together using a simple drag-and-drop interface, and data modeling can even be done automatically based on the query type.

·         Make Your Data Models Evolve:  Data models in business are never carved in stone because data sources and business priorities change continually. Therefore, you must plan on updating or changing them over time. For this, store your data models in a repository that makes them easy to access for expansion and modification, and use a data dictionary or “ready reference” with clear, up-to-date information about the purpose and format of each type of data.