What are the most common challenges in dimensional modeling for business intelligence?

My personal habit for smaller companies is to go one level of granularity beyond what is recommended in two cases: 1. If performance allows 2. If there is potential for questions at that level of granularity This can be a great "above and beyond" move for those who are just discovering what's within their data. For longer-term, multi-phase enterprise projects, however, this article lays solid fundamentals for setting those checkpoints accurately to ensure consistent progress.

Each business needs certain insights to focus on its respective functional area! To create center of excellence in each functional area such as operations, technology, marketing, sales, and revenue etc. its imperative to know where we are at and where we want to go. Based on that certain data points are important instead of everything in that data set. Getting the SMEs in that area and actual users could be key actors to provide inputs in selection of those data elements. To that point overwhelming of data can be easily avoided and achieve desired results.

We have 2 approaches: 1) Design the data model with the granularity required to satisfy current requirement need. Could be a short term solutions. It takes less time to design and develop. But if we foresee lot of insights to be generated in future on this base data with different granularity then it could make things complex to satisfy those requirements. 2) Design tables with least granularity for more robust data model. It needs lot of thinking and well design to built a foundation data model which can contribute to any insights to be generated with different granularity on the same table in future. It might require us to process lot of data as we need to roll up on top of that. So we need to take a call on case-by-case scenarios.

In my experience, it's always important to ask why, how and what when we want to start anything or improve any existing processes. Critically thinking that allows to clean data for the final insights. Automating the monitoring after the clean state could continue to bring great results faster.

Data Quality is one of the most critical step, but mostly its overseen until we know the importance of it. As we are using these data to take some critical decisions, its highly recommend that we do have a check in place that insights are generated with clean and apt data. To do this we need to make sure that we have all the basic checks kept in place to modify or reject the records which doesn't meet the quality criteria. Most of the cases, we either replace with default or assumptions nor reject the records. But its also important to reconcile the rejected records back into the system after getting corrected from the upstream. This helps us to have more complete data and robust process in place.

With growing digital market we have enormous amount of data and variety of data. So its important we collect all those data either it required or not for our current insights. This is where big data and cloud helps us to store variety of data at much cheaper rate. As a first step we build a data lake to just source the heterogenous data from one place. Thus currently more ELT tools are coming into play than ETL. Once we have the datalake, we need to build the Data warehouses on top of it to support the BI & adhoc queries. Most common misconception is that datalake is replacement for data warehousing. But in my point of view, we need to have both to get the optimal performance and flexibility which also requires us to choose the right tools.

The complexity of data is a third challenge encountered in dimensional modeling. It involves managing data with diverse and intricate structures, including unstructured, semi-structured, or multi-dimensional formats like text, images, audio, video, JSON, XML, or OLAP cubes. These data types present challenges when it comes to extracting, loading, and modeling the data within a relational framework. To address this, it's essential to apply suitable tools and techniques designed for handling complex data. These may include ETL (Extract, Transform, Load) tools for data transformation, data lakes for storage, data marts for specialized data warehousing, or using databases tailored to handle specific data types.

The fourth challenge in dimensional modeling involves accommodating changes in data and the ever-evolving business landscape. This encompasses shifts in data sources, formats, values, definitions, or relationships, all of which can impact the integrity, applicability, and alignment of the data warehouse and business intelligence (BI) solutions. To address this challenge effectively, it's crucial to design the data warehouse and BI solutions with adaptability and scalability in focus. Employing techniques like surrogate keys, slowly changing dimensions, or bridge tables helps ensure that the system can seamlessly accommodate changes, maintaining the accuracy and relevance of the data as the business environment evolves.

Totally agree with this! In my experience assigning regular proactive auditing helps compared to reactive approaches. Also looking at threat analytics regularly could be huge value while running business.