5 Factors for a Successful Data Warehousing and BI Project

I'd like to discuss the factors for a successful data warehouse project and how business intelligence plays a role in it. I know there are many more factors than I will discuss here, but these are the ones from a business prospective.

The first and most important factor is the business itself. There is no need for a data warehouse when the business can make the right decisions to steer the company with what they already have.

The second factor is the commitment of the business to the project. It is easy to say “We need a data warehouse and we want it by the end of the year, so that we can achieve more business.” But what it means, is that you have to understand and support, that getting there, you will have to assign people from the business to the project, that it will take time and money without getting a return on investment directly.

The third factor is the alignment of the business. As your company starts to grow, go international or acquire companies, you will have to decide how the different organizations within your company have to deliver their data. You will have to set the definitions of the fields and measures you are going to use in the data warehouse.

The fourth factor is to know your organization. Who is going to analyze the data in the data warehouse and what are their capabilities. Can you get support from your IT department or supplier? How much time are you willing and going to spend on analyzing? Depending on the answers to these and other questions you will have to decide which tool will be the best for your needs and purposes.

The fifth factor is about doing it together. That means the project is going to be successful when business meets IT and vice versa. Make a goal for the future, set a deadline as you do need something to give an end to the project. Start a proof-of-concept or a pilot, define the business requirements, set the scope, set the period needed to a couple of months at the highest, decide which tools you will be using and evaluate at the end what has gone well, what can be done better and what you can leave out. If the evaluation says it is better to start over again then don’t be afraid to do so. Break up your project in small pieces and decide together, business and IT, what is going to be delivered and what the most important parts of a small piece will be. The business can then see what has been build in a short time and they are connected to the project and the acceptance level and therefore the success of the project will be greater.

As you can tell, the previous paragraph is quite big- but with a reason. The fifth factor is where the other four factors come together. Without these four factors there is no fifth factor and the success of doing a project is very small, or your IT department or supplier knows everything about your business, needs and strategy. I would find that rather strange.

Now we know all about the business, but what about the IT department? How are you going to sell and manage the data warehouse? Selling means that your customer, the business, has to been shown something periodically. If selling means more (re)work then don’t be afraid of it but regard it as a part of your planning. You will have to slice up the work you are going to do for a project. You can’t really show a data warehouse, so how are you going to sell? This is where business intelligence comes in, not directly as a analyze tool, but as a selling tool. As the business is able to work with the data, they can check the data and will be pleased.

Of course the method mentioned in the previous paragraph is the scrum method of the agile approach, but I won’t talk about this here any further. Using this method is quite unusual for people who are designing and developing data warehouses, as I am one of them. But I am sure this method will lead to better designed above all better used data warehouses and more successful data warehouse projects.

As this is a more general article explaining my own vision, I will discuss in the coming articles more practical cases on how to use data warehouses and business intelligence tools.



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