Microsoft Intelligence (edited, previously Cortana Intelligence Suite) is a powerful combination of cloud based tools that manage data, provide analysis capability and store or visualize results. Together these tools represent the six elements of data analytics.
According to Gartner analytics projects include 6 elements and there are common challenges for each of these elements that must be addressed in any analytics project:
1. Data sources - volume alone and the need to move large data sets
2. Data models - complexity of setup as they simulate business processes
3. Processing applications - data cleansing, remediation of bad data
4. Computing power - analysis requires considerable power
5. Analytics models - design of experiments and management of the models, model complexity
6. Sharing and storage of results - informative visualizations and results embedded in applications
Tools provided by cloud vendors provide assistance and in some cases, with far superior solutions to these issues.
The Cloud analytics tools are designed to address the challenges we’ve been discussing:
1) Scalability: Agility/ Flexibility
-large data sources –power them off when not in use
- heavy computing capability – pay only for what you use
- Stand up a new environment – pilot a new capability
- integrate data sources with multiple visualization and analytics environment
2) Economy –
- less expensive to stand up than hardware and software,
- fewer skills necessary in house for implementation, configuration, integration, use,
- management and maintenance of systems
- Azure is designed for security from the ground up
- Microsoft spends $1B a year on security research – more than most security firms
- identity based security throughout the stack
- protection for disaster recovery, regulatory specific platforms to address security needs
- Integration with desktop tools for embedding visualizations, integrating predictive analytics
- Consumer technology style interfaces enable faster learning and require fewer skills than script based on premises tools
- Collaboration enabled by ties across Office 365 tools and Mobile capabilities
- Powerful servers can simply process far more data and analysis than most local servers
Data, provided and used as a valuable asset of the firm provides leverage employees apply to problem solving activities. Fact based trend prediction leads to insights that provide business value.
Businesses taking advantage of the cloud to benefit from increased cloud computing power, the ability to handle large data sets and for the mobility provided by access to the analytics platform from any location.
Power BI is a great starting point for moving analytics to the cloud
Storage in the cloud
Predictive Analytics with Azure Machine Learning
In creating the External Table the columns must be of the same data type.
However, when querying the external table to use CTAS to populate a SQL DW table I found that using cast to change the datatype of the column was faster to complete the new table creation. This was true for two cases, one where I converted in the input column varchar2(20) to an Int and another where I converted the input varchar(100) to a Date data type. I suspect this is due to the increased speed of input for Int and Date data types over varchars, but it indicates that the original query time isn't different.
In regions where these settings have been enabled (not East yet), they can be changed in the Configuration blade of the Storage Account.