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What I can offer

I have lots of experience with setting up new Business Intelligence (BI) systems for startup businesses using Open Source Software. I can recommend best practices and architect the entire system from the source data to the user reports. I understan scaling, security, stability, and most importantly data quality. I am familiar with handling Metadata, which enables me to focus on good design rather than getting cought up in contimous firefighting mode. I know how to speak the language of the user, the technical guy, the business man, and the management team. My previous contracts proved that I can work remotely.

In case you would like to start a new BI system, you would like to use publicly available software (no license fees), you have some questions about BI best prcatices, or you would like to chat about handling data quality, feel free to contact me. This is my passion. 

To get a better understanding of what I can do with computers, read more about my computer science backround.

 Some of my work that I am allowed to show

  • The reporting system I designed and created from scratch. This was the stage when I left At the time, this reporting system was the biggest competitive advantage of the company in the IVR industry.

  • A prezi that I created for showing a strategy for a customer who was hoping to start a BI initiative.


More about BI

Business Intelligence is a set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

BI technologies provide historical, current and predictive views of business operations. BI can be used to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions include priorities, goals and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a more complete picture which, in effect, creates an "intelligence" that cannot be derived by any singular set of data.

Often BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW sometimes combine as "BI/DW". A data warehouse contains a copy of analytical data that facilitates decision support. However, not all data warehouses serve for business intelligence, nor do all business intelligence applications require a data warehouse.

Success factors of implementation:

  • Business sponsorship - The commitment and sponsorship of senior management is the most important criteria for assessment. This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship.
  • Business needs - Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation.
  • Amount and quality of available data - Without proper data, or with too little quality data, any BI implementation fails; it does not matter how good the management sponsorship or business-driven motivation is. Before implementation it is a good idea to do data profiling. This analysis identifies the content, consistency and structure of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on hold temporarily while the IT department figures out how to properly collect data.
  • User acceptance - Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization. If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system. If the system does not add value to the users´ mission, they simply don't use it
  • Use of metadata - To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems already capture some metadata (e.g. filename, author, size, etc.), but more useful are metadata about the actual content – e.g. summaries, topics, people or companies mentioned.

I am familiar with the two major schools of Data Warehousing. I studied both the Inmon and the Kimball methodology, and in most of my work I ended up using Ralph Kimball's work. I have created a mindmap for capturing the methodology.  It is available here.