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In more traditional IT projects, when a successful system is tested, deployed and in daily operation, its developers can usually sit back and take a well-deserved rest as users come on-board, and leave ongoing maintenance to a small team of bug-fixers and providers of minor enhancements. At least until the start of the next major release cycle.

The measure of success of a data warehouse is only partly defined by the number and satisfaction level of active users. The nature of creative decision-making support is that users are continuously discovering new business requirements, changing their mind about what data they need, and thus demanding new data elements and structures on a weekly or monthly basis. Indeed, in some cases, the demands may arrive daily! This need for agility in regularly delivering new and updated data to the business through the data warehouse has long been recognized by vendors and practitioners in the space.

Unfortunately, such agility has proven difficult to achieve in the past.

Creating and maintaining the data warehouse

Now, ongoing digitalization of business is driving ever higher demands for new and fresh data. Current—and, in my view, short-sighted—market thinking is that a data lake filled with every conceivable sort of raw, loosely managed data will address these needs. That approach may work for non-critical, externally sourced social media and Internet of Things data. For example, ETL performance is dramatically improved when using stored procedures in a database to create new business analytics data as opposed to extracting and processing the data outside the database using Python or SSIS.

My intention is merely to promote the importance of being mindful in justifying any decisions to tightly couple your platform to its tools. Another potential sinkhole is in the integration layer.

Why Use A Data Warehouse?

However, migrating hundreds of SSIS packages to another tool would become a very expensive project. Using a programming language like Python or Java to write one generic loader to load your staging layer will help to cut down on individual SSIS packages you would have required otherwise. This approach not only helps reduce maintenance and future migration costs but also helps automate more aspects of the data onboarding process with not having to write new individual packages tying in with Principle 2. There are many reasons why a certain business intelligence system may fail, and there are also some common oversights that can lead to eventual failure.

The ever-changing technology landscape, limited budget for data systems because of misconceived secondary priority to operational systems, and the sheer complexity and difficulty of working with data means that careful consideration of not only immediate goals but also future plans needs to happen when designing and building the components of a data warehouse. The data warehousing fundamentals outlined in this article are intended to help guide you when making these important considerations.

Of course, taking into account these principles does not guarantee success, but they will certainly go a long way toward helping you avoid failure. Data warehouse developers or more commonly referred to now as data engineers are responsible for the overall development and maintenance of the data warehouse.


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It would be up to them to decide on the technology stack as well as any custom frameworks and processing and to make data ready for consumers. The use of various technologies means that most data warehouses are very different from one another. A data warehouse is formed by myriad tools and frameworks working holistically together to make data ready for deriving insights.

At the heart of a data warehouse is a database or a logical meta store of data with a data integration framework making up the backbone. Data warehouses provide the mechanism for an organization to store and model all of its data from different departments into one cohesive structure. A data warehouse is capable of being the one single source of truth.

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Creating and maintaining the data warehouse

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Engineering All Blogs Icon Chevron. Filter by. View all results. Data Science and Databases. Chamitha Wanaguru. Chamitha is an IT veteran specializing in data warehouse system architecture, data engineering, business analysis, and project management.

About Barbara Lewis

Business Intelligence Data Warehouse Implementation While the criteria for a successful business intelligence data warehouse would vary by project, certain minimums are expected and required across all projects. However, it is important to show the benefits of a data warehouse to your business stakeholders very early on in the project to ensure continued funding and interest. Ideally, stakeholders should be shown some meaningful business value out of the new system within the first three weeks of a project.

Drove user adoption of the enterprise reporting platform and helped change culture to be an empowered self-service reporting organization — distinct daily user logon count doubled from 40 to 80 and interactive report runs scaled up from to per month. Ability to turn off 49 cubes across 7 divisions ; No need to build and maintain cubes, thus freeing up the IT team to focus on delivering enhancements and integrating additional data sources. Ironside helps companies translate business goals and challenges into technology solutions that enable insightful analysis, data-driven decision making and continued success.

We help you structure, integrate and augment your data, while transforming your analytic environment and improving governance. Assess the Architecture. Designed and built business layer on top of the data layer for efficient reporting. Smooth transition by retiring legacy BI systems and using the Enterprise reporting tool.

Average performance improved by times.