A data warehouse is a central database, which contains a collection of decision-related internal and external sources of data for analysis that is used for the entire company (Ahlemeyer-Stubbe & Coleman, 2014). The authors state that there are four main features to data warehouse content:
- Topic Orientation – data which affects the decisions of a company (i.e. customer, products, payments, ads, etc.)
- Logical Integration – the integration of company common data structures and unstructured big data that is relevant (i.e. social media data, social networks, log files, etc.)
- Presence of Reference Period – Time is an important part of the structural component to the data because there is a need in historical data, which should be maintained for a long time
- Low Volatility – data shouldn’t change once it is stored. However, amendments are still possible. Therefore, data shouldn’t be overridden, because this gives us additional information about our data.
Given the type of data stored in a data warehouse, it is designed to help support data-driven decisions. Making decisions from just a gut feeling can cost millions of dollars, and degrade your service. For continuous service improvements, decisions must be driven by data. Your non-profit can use this data warehouse to drive priorities, to improve services that would yield short-term wins as well as long-term wins. The question you need to be asking is “How should we be liberating key data from the esoteric systems and allowing them to help us?”
To do that you need to build a BI program. One where key stakeholders in each of the business levels agree on the logical integration of data, common data structures, is transparent in the metrics they would like to see, who will support the data, etc. We are looking for key stakeholders on the business level, process level and data level (Topaloglou & Barone, 2015). The reason why, is because we need to truly understand the business and its needs, from there we can understand the current data you have, and the data you will need to start collecting. Once the data is collected, we will prepare it before we enter it into the data warehouse, to ensure low volatility in the data, so that data modeling can be conducted reliable to enable your evaluation and data-driven decisions on how best to move forward (Padhy, Mishra, & Panigrahi,, 2012).
Another non-profit service organization that implemented a successful BI program through the creation of a data warehouse can be found by Topaloglou and Barone (2015). This hospital experienced positive effects towards implementing their BI program: end users can make strategic data based decisions and act on them, a shift in attitudes towards the use and usefulness of information, perception of data scientist from developers to problem solvers, data is an immediate action, continuous improvement is a byproduct of the BI system, real-time views with data details drill down features enabling more data-driven decisions and actions, the development of meaningful dashboards that support business queries, etc. (Topaloglou & Barone, 2015).
However, Topaloglou and Barone (2015) stressed multiple times in the study, which a common data structure and definition needs to be established, with defined stakeholders and accountable people to support the company’s goal based on of how the current processes are doing is key to realizing these benefits. This key to realizing these benefits exists with a data warehouse, your centralized location of external and internal data, which will give you insights to make data-driven decisions to support your company’s goal.
- Ahlemeyer-Stubbe, A, and Coleman, S. (2014). A Practical Guide to Data Mining for Business and Industry, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from https://bookshelf.vitalsource.com/#/books/9781118981863/
- Padhy, N., Mishra, D., & Panigrahi, R. (2012). The survey of data mining applications and feature scope. arXiv preprint arXiv:1211.5723. Retrieved from: https://arxiv.org/ftp/arxiv/papers/1211/1211.5723.pdf
- Topaloglou, T., & Barone, D. (2015) Lessons from a Hospital Business Intelligence Implementation. Retrieved from http://www.idi.ntnu.no/~krogstie/test/ceur/paper2.pdf