Data warehousing has been a hot topic for a couple of years now. In the beginning this was especially something for larger organisations with vast amounts of data spread all over the company. But over time we have seen a shift in type of organisations needing data warehouses solutions. Also smaller organisations are seeing the benefits of having data stored centrally and integrated.
As we see a shift in type of organisation, we also see a shift in type of project. In the past a lot of business owners of the bigger organisations budgeted large data warehouse projects that were executed by external experts. A bus of experts were put on those projects and large data warehouses were build. With organisations smaller in size, budgets were also becoming smaller. And there was no longer need for large teams of experts working on projects that would take months or years to finish. Now we needed to make balanced teams of data warehouse experts and technical implementers.
This is where Kohera has a proven track of excellence. In the last 10 years we have been helping customers implement their data warehousing and analytical needs, by combining budget efficiency and matter expert knowledge. Putting the right person, in the right position to make all projects a big success for the customer. But what about the self-service customers? The customers that have the policy to do all development in-house? Can we also help these kind of customers?
Before we can answer that question, we first need to define the required knowledge to perform a data warehouse project. The three major pillars in data warehousing are:
- Data warehouse specific project knowledge
- Technical implementation knowledge
Data warehouse specific project knowledge
Most companies have experience in performing and managing projects. Although some of that expertise can be used in data warehousing projects, they are somewhat different in execution. As with most projects, there are the usual steps of analysis, development, testing and deploy. Although these steps have the same name as in regular projects, they are executed and documented in a way specific for data warehousing. We need documentation shedding light on:
- Dimensional model needed
- ETL mapping to fill the data warehouse
- Functional and non-functional requirements
- Report design
Next to these usual steps in data warehousing we also have an additional step of data discovery. During data discovery we try to see what data is available. We try to award a value to the data, and that value in combination with the quality of the data will make up the decision to also incorporate the data in the data warehouse.
Technical implementation knowledge
When looking at the Microsoft stack in data warehousing projects we typically use SQL Server components like:
- SQL Server Integration Services (SSIS)
- SQL Server Analysis Services (SSAS)
- SQL Server Reporting Services (SSRS)
- Power BI
Unless you have had the need to load and transform data, or report upon data, you can be a seasoned Microsoft developer but haven’t encountered these tools in the past since most of these tools are so specific for data warehousing.
As with most things, you get better at them after having used them a number of times. To implement a good data warehouse, experience is important. A lot of information can be found online, in books or blogs, but hands-on experience will help you in getting it right. Unfortunately, creating a data warehouse and dimensional model isn’t an exact science where you follow a set of rules and always get it right. It is not enough to read all the books on modelling to make you an expert. There are just to many variables in creating a good model, to many it depends.
Having the knowledge and experience described above will make it more likely for a data warehouse project to be successful. You can get the skills by hiring the correct people. And this brings us back to the earlier question: “But what about the self-service customers? The customers that have the policy to do all development in house? Can we also help these kind of customers?” The answer is “YES, we can”. For those customer a coaching track is available.
During a coaching track the customer will be guided through all different steps of the data warehouse project. We execute the different steps together, where we, as coaches, provide all the necessary information and templates. We explain how to use the templates, provide a basic understanding of dimensional modelling, and go more into detail on the topics that are interesting for the customer’s situation. Next to general project approach and explaining all the functional topics, we also elaborate on how to use technical tools to implement data warehouse according tool best practices and industry standards.
Looking back at the three pillars, coaching will make sure that you will be able to fulfil these needs:
- Data warehouse specific knowledge is provided in the form of the experience of the coaches, and also the templates and project structure that we use. The coach will explain, but more importantly, guide you through all different stages of a project, providing you with the necessary tools. We help you document requirements, track development and testing results and log issues that are encountered.
- Technical knowledge is provided by explaining how tools can be used by your organisation. You won’t get a standard textbook explanation of different tools with basic exercises on meaningless data sets. We give you an explanation on how we standardly use the tools based upon your data. Exercises consist in building the actual packages and projects that will be part of your data warehouse project.
- To coach someone in any form of project requires for the coach to have several years of experience. And this is what our coaches bring, be it in developing ETL or reports, or gathering requirements and documenting them, in project management and setting up Business Intelligence Competence Centers.
So coaching is the perfect solution for all organisations wanting to create a data warehouse but lacking the experience. As opposed to following a training we won’t overload you with all features of a tool or methodology. We go in detail on the parts that are interesting for you, and we will be working on your data.