MDX Studion Online
Mosha Pasumansky, father of OLAP and MDX
Mosha Pasumansky is one of the inventors of the MultiDimensional eXpressions (MDX) language, a query language for online analytical processing (OLAP) databases. Pasumansky is also one of the architects of the Microsoft Analysis Services, and an OLAP expert. Mosha Pasumansky is well known in the OLAP community for his Microsoft OLAP information website which contains a collection of technical articles and other resources related to Microsoft OLAP and Analysis Services. He also has a blog dedicated to MDX and Analysis Services. He spoke at Microsoft conferences such as TechEd and PASS, and he published the book Fast Track to MDX. As of 29 December 2009, Mr. Pasumansky had shifted his focus to Bing, the Microsoft Search Engine, and is no longer maintaining his active stewardship of the BI Community. We are going to miss him and his articles regarding OLAP, MDX and Business Intelligence in general.
This is an online version of the MDX Studio product build by Mosha. The full version can be downloaded from http://www.mosha.com/msolap/mdxstudio.htm For discussion, bug reports, feature suggestions etc - please visit our blogg here. Here is the link to MDX Studio Online: http://mdx.mosha.com/default.aspx
Friday, November 14, 2014
These tips are provided by , who brings 20 years of varied data-intensive experience working with successful start-ups, small companies across various industries, and eBay, Visa, Microsoft, GE and Wells Fargo.
1. Leverage external data sources: tweets about your company or your competitors, or data from your vendors (for instance, customizable newsletter eBlast statistics available via vendor dashboards, or via submitting a ticket)
2. Nuclear physicists, mechanical engineers, and bioinformatics experts can make great data scientists.
3. State your problem correctly, and use sound metrics to measure yield provided by data science initiatives.
4. Use the right KPIs (key metrics) and the right data from the beginning, in any project. Changes due to bad foundations are very costly. This requires careful analysis of your data to create useful databases.
5. Fast delivery is better than extreme accuracy. All data sets are dirty anyway. Find the perfect compromise between perfection and fast return.
7. Big data has less value than useful data.
8. Use big data from third party vendors, for competitive intelligence.
12. You don't have to store all your data permanently. Use smart compression techniques, and keep statistical summaries only, for old data. Don't forget to adjust your metrics when your data changes,.
14. Always include EDA and DOE (exploratory analysis / design of experiment) early on in any data science projects. Always create a . And follow the traditional .
15. Data can be used for many purposes:
· quality assurance
· to find actionable patterns (stock trading, fraud detection)
· for resale to your business clients
· to optimize decisions and processes (operations research)
· for investigation and discovery (IRS, litigation, fraud detection, root cause analysis)
· machine-to-machine communication (automated bidding systems, automated driving)
· predictions (sales forecasts, growth and financial predictions, weather)
17. Data + models + gut feelings + intuition is the perfect mix. Don't remove any of these ingredients in your decision process.
18. Leverage the power of compound metrics: KPIs derived from database fields, that have a far better than the original database metrics. For instance your database might include a single keyword field but does not discriminate between user query and search category (sometimes because data comes from various sources and is blended together). Detect the issue, and create a new metric called keyword type - or data source. Another example is , a fundamental metric that should be created and added to all digital analytics projects.
19. When do you need true real time processing? When fraud detection is critical, or when processing sensitive transactional data (credit card fraud detection, 911 calls). Other than that, (with a latency of a few seconds to 24 hours) is good enough.
20. Make sure your sensitive data is well protected. Make sure your algorithms can not be tampered by criminal hackers or business hackers (spying on your business and stealing everything they can, legally or illegally, and jeopardizing your algorithms - which translates in severe revenue loss). An example of business hacking can be found in section 3 i.
Wednesday, November 5, 2014