Back to basics: Design your Business Intelligence system to have lowest level data even if it’s not asked!

Here’s a scenario:

A Business Intelligence (BI) system for Sales is being developed at a company. Here are the events that occur:

1) Based on the requirements, It is documented that the Business needs to analyze Sales numbers by product, month, customer & employee

2) While designing the system IT learns that the data is stored at each Invoice Level but since the requirements document doesn’t say anything about having details down to invoice level, they decide to aggregate data before bringing in their system.

3) They develop the BI system within the time frame and sends it to business for data validation.

4) Business Analysts starts looking at the BI system and finds some numbers that don’t look right for a few products and need to see Invoices for those products to make sure that the data is right so they ask IT to give them invoice level data.

5) IT realizes that even though business had not requested Invoice Level data explicitly but they do NEED the lowest level data! They realize it’s crucial to pass data validation. Also, they talk with their business analysts and found out that they may  sometimes need to drill down to lowest level data to find insights that may be hidden at the aggregate level.

6) so IT decides to re-work on their solution. This increases the timeline & budget set for the project. Not only that they have lost the opportunity to gain the confidence of business by missing the budget and timeline.

7) They learn to “Design BI system to have the lowest level data even if it’s not asked!” and decides to never make this mistake again in the future!

This concludes the post and it’s important to include lowest level data in your BI system even if it’s not explicitly requested – this will save you time & build your credibility as a Business Intelligence developer/architect.

How does Internet of Things (#IoT) impact data professionals?

Internet enabled computers to be connected with each other.

Internet enabled Mobile Devices to be connected with each other.

Now, Internet will be used to enable physical things to be connected with each other. This is what is called “Internet of things” (IoT).

So what happens?

since more devices are connected with internet – we will able to generate more data! This is usually good if there’s a business vision around how to make sense of data to increase efficiency of all these things.

Here’s a nice case study from Microsoft (focus on the business case – the things in this case is “elevator” to drive reliability)

 

This is all good news for data professionals! There will be increased demand for professionals who can help businesses make sense of data generated via IoT.

Also beware of the “hype” around this technology. It’s important to take incremental steps to achieve the vision – Instead of trying to analyze data from ALL devices in your organization, start with one physical thing that matter the most for your organization or start with data that you have and take incremental steps to spread data culture in your organization!

Now that Big Data has become a mainstream word in IT and business, we have a new buzzword to learn/talk about IoT – but remember it’s all about making sense of data and your skills would be more valuable than ever!

PASS Business Analytics VC: Insider’s Introduction to Microsoft Azure Machine Learning (#AzureML). #sqlpass

RSVP: http://bit.ly/PASSBAVC091814


Session Abstract:
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider’s perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning.
Speaker BIO:
Mark is a consultant who provides enterprise data science analytics advice and solutions. He uses Microsoft Azure Machine Learning, Microsoft SQL Server Data Mining, SAS, SPSS, R, and Hadoop (among other tools). He works with Microsoft Business Intelligence (SSAS, SSIS, SSRS, SharePoint, Power BI, .NET). He is a SQL Server MVP and has a research doctorate (PhD) from Georgia Tech.

RSVP: http://bit.ly/PASSBAVC091814

Hope to see you there!

Paras Doshi
Business Analytics Virtual Chapter’s Co-Leader

 

Back to basics: Multi Class Classification vs Two class classification.

Classification algorithms are commonly used to build predictive models. Here’s what they do (simplified!):

Machine Learning Predictive Algorithms analytics Introduction

Now, here’s the difference between Multi Class and Two Class:

if your Test Data needs to be classified into two classes then you use a two-class classification model.

Examples:

1. Is it going to Rain today? YES or NO

2. Will the buyer renew his soon-to-expire subscription? YES or NO

3. What is the sentiment of this text? Positive OR Negative

As you can see from above examples the test data needs to be classified in two classes.

Now, look at example #3 – What is the sentiment of the text? What if you also want an additional class called “neutral” – so now there are three classes and we’ll need to use a multi-class classification model. So, If your test data needs to be classified into more than two classes then you use a multi-class classification model.

Examples:

1. Sentiment analysis of customer reviews? Positive, Negative, Neutral

2. What is the weather prediction for today? Sunny, Cloudy, Rainy, Snow

I hope the examples helped, so next time you have to choose between multi class and two class classification models, ask yourself – does the problem ask you to predict two classes or more? based on that, you’ll need to pick your model.

Example: Azure Machine Learning (AzureML) studio’s classifier list:

Azure Machine Learning classifiers list

I hope this helps!

How to Configure SQL Server Analysis services’s Action to Open an URL?

SSAS Actions are powerful! You can open web pages, open sql server reporting services, customize drill through reports among other things using actions. In this post, you will see a common requirement from users to navigate to a corporate intranet site from the cube – and usually it needs to be dynamic.

For example, user is interested in seeing the Order Entry Page hosted on the corporate intranet site by using the Order ID from the SSAS cube.

Here’s how you can set it up:

1. Open SSAS Cube in SQL Server Data Tools:

2. Navigate to Actions tab:

ssas url action analysis services sql server web page

3. Here you’ll see three types of action that you can configure

a. Standard (this have five subtypes including the URL action)

b. Drill Through

c. report action

4. For the purpose of this blog post, let’s focus on standard action:

ssas url action analysis services sql server web page5. Once you click on the “New Action” it will ask you to configure the action:

a. Name: Enter the desired name here

b. Target Type: In this case, Order ID is an attribute member but you will have to choose appropriate target type for your scenario

c. Target Object: In this case, it’s something like [Order].[Order ID] – in your case, you’ll have to choose an appropriate target object

d. Type: URL in this case (also don’t forget to check books online for what other types can do as well)

e. Action Expression: the format of the Action Expression if it’s driven by a parameter would go something like:

"http://servername/site/Pages/OrderRef.aspx?Search&ID="+[Order].[Order ID].currentmember.member_caption

f. Additional Properties: I like to set the Caption to clearly indicate the user that they are opening the “Order Form for Order ID 123999″. You can do that by setting the caption property. The format goes like this:

"Open Order Entry page for Order ID: "+[Order].[Order ID].currentmember.member_caption

Also set the caption is MDX to True if you are using above format.

That’s about it, don’t forget to test it (after deploying the cube) using excel or other end-user tool of your choice. In the Pivot Table, use the Order ID attribute in Row/Column labels > Right Click on any attribute member of Order ID attribute > Additional Actions > The caption with dynamic order id should show by here for users to click and navigate to the specified URL:

excel ssas url action analysis services sql server web page

How to create an Average Aggregation in SQL Server Analysis services?

Problem:

How do create a measure that does an average over a field from fact table? You can’t find it the “usage” property while trying to create a new measure:

SQL Server Analysis Services Average Aggregation

Solution:

Before i show you the solution, I want you to know that this is a Level 100 solution to get you started – so depending on the complexity of your cube the calculated measure that you are about to create may or may not perform well – if it does not perform well, you might have to dig a little deeper and here’s one blog post to get you started: URL

OK, back to topic! Here are the steps.

SCENARIO: you need average of Sales Amount.

1. Create a SUM OF SALES AMOUNT measure

Steps: Open cube > Cube Structure > Right click on Measure Group > New Measure > Usage: “SUM” > Source Table: Pick your Fact Table. In this case let’s say it’s Fact Sales > Source Column: In this case, lets say it’s SALES AMOUNT

2. Create a COUNT OF SALES measure (important: row count vs. non empty count – this is not a developer’s choice, a business user needs to define that)

Steps: Open cube > Cube Structure > Right click on Measure Group > New Measure > Usage: count of rows OR count of non empty values (again this is not developer’s choice, a business user needs to define this) > Source Table: Pick your Fact Table. In this case let’s say it’s Fact Sales > Source Column: In this case, lets say it’s SALES AMOUNT

3. Create a Calculated Measure that equals (SUM OF SALES/COUNT OF SALES)

3a. Switch to Calculations section > create a new calculated member:

SSAS Analysis services new calculated measure

3b. Complete Name, Format String & Associated Measure Group. For the Expression, use the following expression. Please use this as a starting point for your measure:

IIF([measures].[COUNT OF SALES]=0,0,[measures].[SUM OF SALES AMOUNT]/[measures].[COUNT OF SALES])

4. Before you test it, if you don’t need the SUM OF SALES AMOUNT and COUNT OF SALES measures than don’t forget to hide them!

Conclusion:

In this post, you saw how to define a measure with average aggregation is SSAS.

SQL Server Analysis services warning: “The name specified for the attribute relationship differs from the name of the related attribute”

In this post we will see how to address the SSAS warning message: “The name specified for the attribute relationship differs from the name of the related attribute”, it’s not a critical waning but it’s always good to make sure that these warnings are addressed before going to production.

Usually this happens because attribute names were renamed after the relationships between attributes had already been defined. 

To fix the warning messages:

1. Go to Attribute Relationships section for the dimension.

2. In the lower right corner, you should find list of relationships.The ones that cause the warning would have a blue squiggly line with a warning symbol on the arrow (example shown below):

ssas attribute relationships cube dimension3. Right Click on the Relationship > Go to Properties > Change the Name property to the new renamed name that you gave to the attribute – it should be what’s shown in the Attribute property.

ssas analysis services attribute relantionship propertiesThat’s it, this should fix the ssas warning message now since the name specified for attribute relationship would now match related attribute.

SQL Server Analysis services warning: “The name specified for the attribute relationship differs from the name of the related attribute”

In this post we will see how to address the SSAS warning message: “The name specified for the attribute relationship differs from the name of the related attribute”, it’s not a critical waning but it’s always good to make sure that these warnings are addressed before going to production.

Usually this happens because attribute names were renamed after the relationships between attributes had already been defined. 

To fix the warning messages:

1. Go to Attribute Relationships section for the dimension.

2. In the lower right corner, you should find list of relationships.The ones that cause the warning would have a blue squiggly line with a warning symbol on the arrow (example shown below):

ssas attribute relationships cube dimension3. Right Click on the Relationship > Go to Properties > Change the Name property to the new renamed name that you gave to the attribute – it should be what’s shown in the Attribute property.

ssas analysis services attribute relantionship propertiesThat’s it, this should fix the ssas warning message now since the name specified for attribute relationship would now match related attribute.

Business Intelligene Dashboard for Quality Managers

Business Intelligene Dashboard for Quality Managers

Business Goal:

Need to understand the patterns in Quality test results data across all plants.

Summary:

- The solution involved creating a Business Intelligence system that gathered data from multiple plants. I was involved in mentoring IT team, development and end-user training of a Business Intelligence Dashboard that used SQL server analysis services as it’s data source.

- Dashboard development involved multiple checkpoint meetings with business leaders since this was the first time they had a chance to visualize quality test results data consolidated from multiple plants. Since they were new to data visualization, I used to prepare in advance and create 3-4 relevant visualization templates to kick off meetings.

Mockup:

(it is intended to look generic since I can’t discuss details. Also, drill down capabilities had been added to the dashboard to go down to the lowest granularity if needed)

Quality Test Results Dashboard

Business Intelligence Dashboard for Plant Managers (operations focused):

Business Intelligence Dashboard for Plant Managers (operations focused):

Business goal:

Plant managers needed a centralized automated solution that helped them monitor key metrics (operations focused) to help them better manage manufacturing plants.

Technical Summary:

- Work with the plant managers to identify key metrics & calculations to be displayed on dashboard

- Work with the IT managers to identify data source systems.

- Develop the Dashboard using SQL Server Reporting Services. (Built iteratively by making sure to have three checkpoint meetings with plant managers while working with IT/Business-Analysts to ensure data integrity)

- Developed drill down reports see detailed data at plant and machine level.

Mockup:

Plant Managers dashboard operations manufacturing