{"id":4455,"date":"2024-06-18T17:40:59","date_gmt":"2024-06-18T17:40:59","guid":{"rendered":"https:\/\/quebit.com\/askquebit\/?p=4455"},"modified":"2026-01-16T15:42:01","modified_gmt":"2026-01-16T15:42:01","slug":"data-modeler-or-data-analyst","status":"publish","type":"post","link":"https:\/\/quebit.com\/askquebit\/data-modeler-or-data-analyst\/","title":{"rendered":"What\u2019s the Difference Between a Data Modeler and a Data Analyst, and Which Role Do I Need?"},"content":{"rendered":"

Data Modeler or Data Analyst<\/strong><\/p>\n

The act of data modeling can be challenging for a variety of reasons.<\/p>\n

Rapidly Evolving Business Needs<\/strong> \u2013 it is an uncommon thing today for a business to have a firm grasp on all its needs and if it does, rapid growth (or contraction) of the business can quickly change those needs. A data modeler will need to first understand growth (strategic, internal, organic, and acquisition\/merger) \u00a0and have a \u201cfinger on the pulse\u201d of the business to assure the data model design can keep pace and evolve as the business grows.<\/p>\n

Complex data and data sources<\/strong> \u2013 often a data modeler will need to deal with data coming from numerous sources, each potentially having different structures, sizes, query languages and types. Delivering an appropriate data model design will require competency in each of the data types.<\/p>\n

Limitations of Current Data Systems<\/strong> \u2013 A data modeler is often asked to create a data model design using current technologies and systems but must be able to be applied to future technologies and systems that may be developed or that the organization is planning to adapt. The successful data modeler must be aware of trends in technology and data to be able to \u201cdesign with anticipation.\u201d<\/p>\n

Budget and Time restrictions<\/strong> \u2013 Many, many factors can affect a data model design and budget and time, unfortunately, are two of biggest. Simply put, the idea of creating a \u201cperfect,\u201d innovative data model design is seldom realistic. To be successful as a data modeler you will need to deliver a data model design on time and on budget and one that will meet the most important business requirements of the organization. Not a small task!<\/p>\n

Data Modeler or Data Analyst<\/strong><\/p>\n

It is somewhat easy to confuse the roles of a data analyst and data modeler, in fact many \u201cdata-oriented\u201d tasks are frequently interlaced and may be performed by either a data analyst or<\/em> a data modeler. \u00a0To illustrate this point, a RACI<\/a> (r<\/strong>esponsible, a<\/strong>ccountable, c<\/strong>onsulted, and i<\/strong>nformed) matrix can be used to see where responsibility lies for some common data-oriented tasks:<\/p>\n

\"\"<\/p>\n

RACI Matrix of Sample Data Tasks<\/em><\/p>\n

It is important to understand that although some of these tasks may be shared between the two roles,\u00a0 accurate data modeling is vital to any organization\u2019s data strategy and proceeds<\/em> any (effective) data analysis. The (data) model is the architecture<\/u> that makes analysis feasible while (data) analysis is about employing<\/u> data<\/u> (to find insights and drive better decisions).<\/p>\n

Another \u201ctake\u201d is that the data modeler is responsible for delivering an \u201catlas\u201d of sorts containing a grouped and sorted illustration of all the types of data the organization uses along with their source(s), formats, attributes, and relationships. The data analyst then uses<\/em> the atlas<\/em> to examine and interpret the organization\u2019s data to extract insights for internal and external business goals.<\/p>\n

Leverage the right Tool<\/strong><\/p>\n

Thankfully, there are things you can do to support your data modeling. You have most likely heard the adage, \u201cthe right tool for the right job\u201d \u2013 well, this is true with data modeling as well. The \u201cright tool\u201d can make modeling easier while using a tool not specifically designed for modeling, limited in functionality, or not well suited for your needs (sources, volume and complexity of your data, level of skills, or simply budget and resources available) can make modeling frustrating and ineffective (and costly as it may take much more time to perform routine tasks).<\/p>\n

IBM Planning Analytics Workspace<\/a> (PAW) has a modeling environment<\/strong> that can be used for data modeling.\u00a0In addition, PAW is also a comprehensive, collaborative, and centralized analysis environment<\/strong> combining data visualization capabilities with planning and forecasting functionalities, seamlessly integrated data sources, automated data collection, consolidation and more which eliminates complexities and inefficiencies found in more traditional analysis work.<\/p>\n

Within the Planning Analytics Workspace environment, user roles are designated as either Modeler<\/strong> or an Analyst<\/strong> (there are also Consumer and Administrator user roles<\/a>) but as a Modeler, you can use all of the capabilities of an Analyst plus full modeling capabilities (supporting the idea, of course, that data and modeling tasks are shared).<\/p>\n

Start with a Workbench<\/strong><\/p>\n

PAW includes a Modeling Workbench<\/a> which \u00a0is a dedicated modeling space<\/em> optimized for all activities required for modeling. You can use the workbench to evaluate and improve data and their sources, including meta data assets, finalize model requirements and create and manage the fundamental building blocks (dimensions, cubes, and links) of a model as well as secure and share assets developed in the form of books and views, visualizations, graphics, and videos.<\/p>\n

You can create a new workbench<\/a> from the\u00a0Planning Analytics Workspace\u00a0home page by click\u00a0 \u00a0>\u00a0New<\/strong>\u00a0>\u00a0Workbench<\/strong>, or from\u00a0Planning Analytics Workspace\u00a0home page, Click the\u00a0Data and Models<\/strong>\u00a0tile, then click\u00a0New<\/strong>\u00a0>\u00a0Workbench<\/strong>\u00a0on the Data and Models page.<\/p>\n

User preferences<\/strong> play a fundamental role in creating a positive user experience. In more recent versions of PAW, enhanced <\/em>user preferences<\/em><\/a> allow you to set options in for how the modeling workbench looks and behaves, including the database tree, dimension, process and rules editors, and the cube viewer.<\/p>\n

File Manager Feature<\/strong><\/p>\n

The\u00a0File manager<\/strong><\/a>, is a tool that the modeling workbench offers which works similar to Windows File Explorer, and shows a list of all the files and folders that have been uploaded to the\u00a0Planning Analytics\u00a0database and allows you to manage (cut, copy, paste, and even compress and expand) files and folders.<\/p>\n

Organizing the Workbench<\/strong><\/p>\n

When you open or create an asset, the editor is inserted as a new tab<\/em> on your workbench. When you have multiple editors (tabs) open, you can move and rearrange the tabs<\/strong><\/a> on the \u201ctabset\u201d<\/strong> (you can also maximize individual open editors to provide more working space).<\/p>\n

Floating windows<\/strong><\/a> can be moved and dragged to any position, including outside of the main application window and can help with multitasking by allowing users to work on another task or process multiple tasks simultaneously. PAW offers the ability to open any editor (cube, dimension, rule, process, or chore) in a separate window that is detached from the modeling workbench. This is helpful when you want to extend the size of the editor. To create a floating window, press the\u00a0Ctrl<\/strong>\u00a0key while right-clicking<\/strong> on the desired asset in the data tree. You will see options to open or create in a new window.<\/p>\n

Search Feature<\/strong><\/p>\n

Well-implemented search features<\/strong> (ways to easily find what you are looking for) can reduce frustration and increase productivity (even in smaller Planning Analytics models, there could be literally hundreds of assets).\u00a0 You can use the model-level search feature<\/strong><\/a> in the modeling workbench to search for any item (cubes, dimensions, views, sets, processes, chores, rules, and control objects) in a database. To search within a model, you can click the workbench Search<\/strong>\u00a0button, select the model in which you want to search and enter the name of the item to search for.<\/p>\n

Conclusion<\/strong><\/p>\n

IBM Planning Analytics Workspace is not only a viable but extremely efficient and exciting to use modeling tool. Above are only a few examples of its numerous (and growing number) of handy features. You can use this link<\/a> to identify when a new feature is introduced in Planning Analytics Workspace.<\/p>\n","protected":false},"excerpt":{"rendered":"

Data Modeler or Data Analyst The act of data modeling can be challenging for a variety of reasons. Rapidly Evolving Business Needs \u2013 it is an uncommon thing today for a business to have a firm grasp on all its needs and if it does, rapid growth (or contraction) of the business can quickly change…<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[24],"tags":[108,55],"class_list":["post-4455","post","type-post","status-publish","format-standard","hentry","category-ibm","tag-ibm-planning-analytics","tag-planning-analytics-tech-tips"],"acf":[],"yoast_head":"\nWhat\u2019s the Difference Between a Data Modeler and a Data Analyst, and Which Role Do I Need? - QueBIT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/quebit.com\/askquebit\/data-modeler-or-data-analyst\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What\u2019s the Difference Between a Data Modeler and a Data Analyst, and Which Role Do I Need? - QueBIT\" \/>\n<meta property=\"og:description\" content=\"Data Modeler or Data Analyst The act of data modeling can be challenging for a variety of reasons. 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