Geometric Mean in Power BI

 In my latest article, I walk through how to calculate the Geometric Mean in Power BI, including practical implementation steps and when you should use it instead of the traditional average.

Key takeaways:
🔹 Understand the difference between Arithmetic Mean and Geometric Mean.
🔹 Learn why growth and compounded values require a different approach.
🔹 Implement Geometric Mean calculations in Power BI with practical examples.
🔹 Improve the accuracy of your analytical dashboards and business reports.
Whether you're a Power BI developer, data analyst, or BI professional, understanding the right statistical measure can significantly improve decision-making.
📖 Read the full article here:
https://lnkd.in/gYwrP4za

I'd love to hear how you've handled compounded metrics or growth-rate calculations in your Power BI projects.
hashtag#PowerBI hashtag#BusinessIntelligence hashtag#DataAnalytics hashtag#DAX hashtag#DataVisualization hashtag#Statistics hashtag#DataScience hashtag hashtag#Analytics hashtag#BI hashtag#PowerBICommunity

Multi Language Support for SSAS

When you are asked to define the term data warehouse, You will say that it is a framework that will be used to analyse enterprise-level data If it is a framework, data warehouse user should have the option of using the data warehouse irrespective of the language that he is familiar with. 

This new article brings you how to analyse your data using multiple languages. This article explains, what are the modifications that you need to do for the SQL Server stack to incorporate multi-language into the data warehouse. 

There are multiple articles on SQL Server Analysis Service (SSAS). 

OLAP Cube in SQL Server

Implementing an SSAS Tabular Model for Data Analytics

SQL Server Analysis Services SSAS Hardware Configuration Recommendations

Create Key Performance Indicators KPI in a SQL Server Analysis Service SSAS Cube

Enhancing Data Analytics with SSAS Dimension Hierarchies

Improve readability with SSAS Perspectives

SSAS Database Management

Monitoring SQL Server Analysis Services with Extended Events

Migrating On-Premises Microsoft SQL Server Business Intelligence Solution to Azure

Some organizations are looking at the possibility of migrating their Microsoft Business Intelligence solutions to the cloud due to cost-effectiveness, easier maintenance, scalability, etc. However, they are running current solutions on-premises which have been servicing their stakeholders for years. Because of this, with the cost factor and the time factor, it might not be feasible to start a new solution from scratch. Although they would like to lift and shift the existing solution to the cloud as-is, there are a lot of issues with that migration. Mainly, some of the existing features may not be compatible with the cloud. This article looks at how on-premises Microsoft Business Intelligence solutions can be migrated to an Azure environment.

Read the full article https://www.mssqltips.com/sqlservertip/6163/migrating-onpremises-microsoft-sql-server-business-intelligence-solution-to-azure/

12 Microsoft Power BI Success Stories

Self-service BI is on the rise among forward-thinking organizations, enabling business users to obtain up-to-the-minute business information in graphical form on demand — with little to no IT intervention. Since the release of Microsoft Power BI, organizations around the world have been turning to the cloud-based service for self-service business intelligence tools that allow users to analyze, visualize and share data using the familiar Microsoft Excel spreadsheet. Here are 12 success stories built on Microsoft Power BI.

Read more https://www.cio.com.au/article/626101/12-microsoft-power-bi-success-stories/?fp=4&fpid=51241

Magic Quadrant for Data Management Solutions for Analytics

Microsoft was leading in the Gartner quarter for while and Oracle was not promising in this. However, with the 2017 results, landscape has changed.



You can read the details at https://www.gartner.com/doc/reprints?id=1-3T5J67S&ct=170209&st=sb

24 hours of SQLPass is coming

24 hours of SQL Pass is happening on Sept 17 & 18th.  These are short little 1-hour web casts, but may be of interest to someone.  Registration is FREEEEEEEE!!!  

Link to sessions:

http://www.sqlpass.org/24hours/2015/summitpreview/Schedule.aspx

What is Coming in SQL Server 2016 for Business Intelligence

Over the past few years we did not hear anything from Microsoft about SQL Server on-premises business intelligence enhancements. The extended cricket-chirping silence left us speculating about the future of Reporting Services, Analysis Services and other key technologies that are being used today to power critical decision making solutions. Good news, the waiting and wondering is over. Microsoft recently announced significant SQL Server on-premises upgrades in numerous presentations at both //build and Ignite conferences. Here are a few of my top take-away points from a BI professional perspective.

Read more http://sqlmag.com/blog/what-coming-sql-server-2016-business-intelligence

Data Mining Cluster Analysis in SQL Server

Grouping is something we naturally do in our day to day life. We group foods depending on taste, we group friends depending on their different attributes. 

Clustering is an algorithm which finds natural groupings inside your data when these groupings are not obvious. It finds the hidden variable that accurately classifies your data.

Read the article on Clustering here.

January 2014 Meet-up

Session #1

TITLE: Predictive Modeling with the Microsoft Naïve Bayes algorithm

Join this session where Dinesh showcases the capabilities of predictive analysis using the Microsoft Naïve Bayes algorithm. Naïve Bayes is a classification algorithm that ships with SQL Server Analysis Services and is used to mine for and predict outcomes based on selected parameters.

CATEGORY: Business Intelligence (Data Mining)

SPEAKER: Dinesh Asanka (MVP), Database Specialist (Pearson Lanka)
Linked.In | Blog | Facebook | @dineshasanka

40 minutes approx.

 

Session #2

TITLE: Writing Resilient T-SQL Code - Part II

Continuing from where he left off from November's session, Gogula will guide you through writing better T-SQL code that is more resilient to unexpected issues and common code failures. This session is based on the book Defensive Database Programming by Alex Kuznetsov.

CATEGORY: Development

SPEAKER: Gogula G. Aryalingam (MVP), Technical Architect (Navantis)
Linked.In | @gogula | Blog

40 minutes approx.

 

Time & Location

JANUARY 22, 2013 - 6:00 PM Onwards at MICROSOFT SRI LANKA

11th Floor, DHPL Building, No. 42, Nawam Mawatha, Colombo 2, SRI LANKA

An excellent opportunity to network and learn. Refreshments provided.

Entrance FREE

Data Model Capabilities

In SQL Server 2012, there is a new data model called Tabular which is an addition to the existing Multidimensional Model.

So what are the limits for Tabular data model?

Feature	Multidimensional	Tabular
Calculated measures	Yes	Yes
Distinct count	Yes	Yes (via DAX)
Hierarchies	Yes	Yes
KPIs	Yes	Yes (not trends)
Actions	Yes	No
Linked objects	Yes	No
Many-to-many relationships	Yes	No
Parent-child hierarchies	Yes	Yes (via DAX)
Partitions	Yes	Yes
Perspectives	Yes	Yes
Semi-additive measures	Yes	Yes
Translations	Yes	No
User-defined hierarchies	Yes	Yes
Custom assemblies	Yes	No
Custom rollups	Yes	No
Writeback	Yes	No
Data mining	Yes	No

If you require data mining, writeback, or translations within a data model, this can only be facilitated by multidimensional data models.

Getting Started with Data Mining in SQL Server

As database professionals, we typically work in a field of exact science. For example, a common practice in business intelligence (BI) solutions is creating duplicate copies of data sets, then comparing the results from the different sources to make sure they're the same. If you extract five years' worth of data from an application's database and put it into a data mart, the results in the data mart must be the same as the results in the application's database, even if the table structures were changed and older records were archived. You might build a cube or semantic model and again check to make sure the results are exactly the same as the source system. If the numbers don't add up, the results are rejected because you know that something is wrong and must be corrected. I have to confess that not getting a conclusive result when working on a tough data problem sometimes keeps me up at night.

Read more http://sqlmag.com/sql-server/sql-server-data-tools-data-mining-model-designer