Cross-posted from the SQL Server Blog's PASS BAC Preview Series
By Marco Russo
We live in exciting times from the point of view of data analysis. Nowadays, the problem is no longer how to find the raw data, but how to handle the pressure of data coming in from so many places. At the end of the day, the goal is always the same, extracting useful knowledge from data. This has been the goal of Business Intelligence (BI) since 1958, when Hans Peter Luhn used this term for the first time. In more than 50 years, the technology evolved, increasing the manageable amount of data and lowering the related costs. However, it always required professionals who were able to create and refresh the data model, empowering end users with canned reports and data navigation tools. A common issue in this process was the gap between people who knew the business and BI developers. In the best case, this gap produced long development times. In the worst case, it led to project failure.
Today, this gap can be drastically reduced. Thanks to Self-Service BI products, if you know your business, you can create a data model without having to ask for a BI professional consultancy. However, a common mistake is thinking that these new technologies are meant to kill the traditional data warehouse approach, i.e. the Corporate BI. In reality, self-service BI is an opportunity to improve the ROI of a properly built data warehouse, even if an optimal architecture might require some adaptation to the data warehouse schema, in order to simplify and optimize the extraction of data for self-service BI purposes.
When we use the term Business Analytics we refer to the exploration and investigation of data. This requires the usage of statistical methods and data visualization, and oftentimes needs adapting the data model. The PASS Business Analytics Conference (PASS BAC) in Chicago this April is the right place to go to learn more about tools, methodology and best practices in the Business Analytics area.
I will speak at the PASS BAC in two sessions about the state of the art in self-service BI:
- In the session Self-Service Data Modeling, I discuss the challenges of creating a proper data model by using Excel 2013 and PowerPivot. Thanks to the DAX language, it is possible to apply few transformations to the raw data. However, preliminary data preparation might be necessary and users that do not have a knowledge of ETL and SQL need other tools and techniques to adapt their raw data to the required model. I will show how to solve these issues in common scenarios, using tools designed for end users and not for BI developers.
- The second session, Modern Data Warehousing Strategy, is about changes in the Data Warehouse architecture and modeling required to face the challenges of the self-service approach and the new demand caused by Big Data technologies such as Hadoop (HDInsight). A good data warehouse is still the optimal starting point for any analysis, but we need to update our strategy for data warehouse implementation to fit the requirements of this new era. What kind of data modeling should we use for the data warehouse? What is the role of data marts? Do technologies such as PowerPivot or Analysis Services Tabular affect the way we should model our data? Do columnstore indexes remove the need for an analytical server like Analysis Services? We will discuss these and other questions, offering an updated approach to the data warehouse modeling methodology.
Look at the many other sessions available in the program and join us in Chicago at the PASS Business Analytics Conference!
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