Monthly Archives: May, 2014

Interview Question #6: Warehouse Tables Options

Question

Which of the following options in the Warehouse Tables pane would you use to view the first 100 rows of data in a table?

A. Show Table Structure

B. Show Top 100 Rows

C. Show Sample Data

D. Select Database Instance

E. You cannot accomplish this in the Warehouse Tables pane.

Answer

C. Show Sample Data

The Warehouse Tables pane, shown below on the left side, displays a list of the data sources available for the project.

Interview 6-2

Note: If the Warehouse Tables pane is not displayed, from the Home tab, in the Panels area,

click Show the Warehouse tables section:

Interview 6-1

 

You can right-click a data source and select from the tasks listed below.

  • Select Mapping Color: Defines the color associated with the data source. A table that is included in a project display this color to distinguish what data source it is from.
  • Update: Updates all the tables for the data source to reflect their definitions in the data source.
  • Warehouse Catalog Options: Opens the Warehouse Catalog options dialog box to define various settings for data warehouse connection and operation defaults using Architect, including:
    • Warehouse Connection: These options allow you to modify the database instance and database login used to connect the data warehouse to a project.
    • Read Settings: These options allow you to customize the SQL that reads the Warehouse Catalog for every platform except Microsoft Access.
    • Table Prefixes: These options allow you to specify whether table prefixes are displayed in table names and how prefixes are automatically defined for tables that are added to the project.
  • Select Database Instance: Opens the Select Database Instance dialog box to select data sources to display in Architect.

Interview 6-3

 

You can expand a data source to view the tables available in the data source (see screenshot above). You can right-click a table and select from the tasks listed below:

  • Add Table to Project: The table is included in the project schema. You can then create attributes and facts on the columns of the table. This option is not available if the table is already included in the project.
  • Show Element: Displays the table included in the project in the Project Tables View. This option is only available if the table is already included in the project.
  • Show Sample Data: Displays a subset of the data available in the table. This lets you determine the type of data that is available in the table.
  • Update Structure: Updates the table to reflect its definition in the data source.

The Warehouse Tables pane also allows you to add tables from multiple data sources to your project.

MicroStrategy Course Where You Will Learn About This Topic

MicroStrategy Architect: Project Design Essentials Course

MicroStrategy Hires Marcus Starke as Chief Marketing Officer

TYSONS CORNER, Va., April 22, 2014 /PRNewswire/ —

Marcus-StarkeMicroStrategy ® Incorporated, a leading worldwide provider of enterprise software platforms, recently announced that Marcus Starke has been hired as the company’s new Chief Marketing Officer.  Starke comes to MicroStrategy with more than 25 years of marketing and general business experience, including senior leadership roles with some of the world’s best-known software and media companies. Prior to joining MicroStrategy, Starke served as SAP’s Senior Vice President of Worldwide Marketing & Communications Regions, where he oversaw all marketing strategy and execution and was a member of SAP’s global leadership team. Immediately preceding his five-year tenure at SAP, Starke was President and CEO (EMEA) of Wunderman, a division of the WPP Group.

“We are extremely pleased to have a recognized marketing innovator like Marcus join us at a very exciting time for the company,” said Michael Saylor, CEO, MicroStrategy Incorporated. “Marcus brings global marketing expertise, not only from his time with SAP but also from leading marketing communications agencies.  Marcus is a thought-leader who’s passionate about innovation, and his track record as a disruptive force in strategy, marketing and communications couldn’t be better suited to our plans to transform the market with our offerings.  His hire represents the latest in a series of concrete steps that MicroStrategy has taken to enhance the customer experience and capture market share in the global market place.””Telling the story of such an important global technology and innovation leader — boasting thousands of successful customers, including hundreds of major global brands — is a terrific opportunity,” said Starke. “This is a pivotal time for MicroStrategy as it leads the market with the world’s number #1 mobile BI platform, the leading cloud BI solution and what I believe is the most comprehensive analytics platform. There’s also a powerful opportunity ahead in its new mobile identity and loyalty platforms. I am very excited about the opportunity to help customers significantly accelerate business innovation, simplify their businesses, and dramatically enhance the experience of their customers through the amazing technologies MicroStrategy has developed.”

Earlier in his career, Starke served as the Chief Executive Officer and Chairman of Publicis Frankfurt.  He also managed his own consulting and marketing agency. A strong advocate of “social business,” in navigating the social media/social community landscape, Starke pens a widely-read blog and is a passionate proponent of customer engagement through world-class marketing and communications.

About MicroStrategy Incorporated Founded in 1989, MicroStrategy is a leading worldwide provider of enterprise software platforms.  The Company’s mission is to provide the most flexible, powerful, scalable and user-friendly platforms for analytics, mobile, identity and loyalty, offered either on premises or in the cloud.

The MicroStrategy Analytics Platform™ enables leading organizations to analyze vast amounts of data and distribute actionable business insight throughout the enterprise.  Our analytics platform delivers reports and dashboards, and enables users to conduct ad hoc analysis and share their insights anywhere, anytime.  MicroStrategy Mobile™ lets organizations rapidly build information-rich applications that combine multimedia, transactions, analytics, and custom workflows.  The MicroStrategy Identity Platform™ (branded as MicroStrategy Usher™) provides organizations the ability to develop a secure mobile app for identity and credentials. The MicroStrategy Loyalty Platform™ (branded as MicroStrategy Alert) is a next-generation, mobile customer loyalty and engagement solution.  To learn more about MicroStrategy, visit www.microstrategy.com and follow them on Facebook ( http://www.facebook.com/microstrategy ) and Twitter ( http://www.twitter.com/microstrategy ).

MicroStrategy, MicroStrategy Analytics Platform, MicroStrategy Mobile, MicroStrategy Identity Platform, MicroStrategy Loyalty Platform, and MicroStrategy Usher are either trademarks or registered trademarks of MicroStrategy Incorporated in the United States and certain other countries.  Other product and company names mentioned herein may be the trademarks of their respective owners.

Bryan’s BI Blog: MicroStrategy vs Tableau

Readers:

Bryan BrandowBryan Brandow, has posted his second new post on his new blog, Bryan’s BI Blog and it is a doozy. Bryan does an in-depth comparison of MicroStrategy vs. Tableau.

Here is a link to the MicroStrategy vs. Tableau post.

Best Regards,

Michael

 

Data Blending: Why are Some Metric Values Blank in Documents Using Multiple Datasets in MicroStrategy Analytics Enterprise 9.4.1 (Part 8)

MicroStrategy Analytics Enterprise

Introduction

Starting with MicroStrategy Analytics Enterprise 9.4.1, Report Services documents can contain grids with objects coming from more than one dataset.

Multiple Datasets in a Single Grid/Graph/Widget Object in MicroStrategy Web 9.4 [2]

Users now have the ability to add attributes and/or metrics from multiple datasets to a single grid, graph, or widget. For example, if Dataset #1 contains Category and Revenue and Dataset #2 contains Category and Profit, a grid can be created which contains Category, Revenue, and Profit.

Part 8 - 1

Administrators can control the use of multiple datasets in a single grid, graph, or widget through the Analytical Engine VLDB properties window at the project level.

  1. Right mouse click (RMC) on the project name.
  2. Select Project Configuration.
  3. Click on Project Definition.
  4. Select ‘Advanced’.
  5. Click “Configure” under the Analytical engine VLDB.

Part 8 - 2

NOTE:

The default value is set to: “Objects in document grids must come from the grid’s source dataset only”.

Users can set the set the source of the grid to a particular dataset or choose no dataset (in which case, the MicroStrategy engine will determine the best suited dataset). [1]

The MicroStrategy Analytical Engine displays no data for metrics in ambiguous cases or when there is a conflict. Ambiguous cases can arise in cases where multiple datasets contain the same objects.  Examples based on the MicroStrategy Tutorial project have been provided to explain this information.

Note: When the MicroStrategy Analytical Engine cannot resolve the correct datatset as explained in the cases below, the data displayed for these will correspond to the value chosen for the missing object display under Project Configuration > Report definition > Null values > Missing Object Display. The default value for this blank.

Case1:

Multiple datasets have the same metric. Only one dataset does not contain this metric and this dataset is set as the source of the grid.

This case is explained with an example based on the MicroStrategy Tutorial project.

1. Create the following objects:

a. Dataset DS1 with the attribute ‘Year’ and metric ‘Profit’.

b. Dataset DS2 with the attribute ‘Year’ and metrics ‘Profit’, ‘Revenue’.

c. Dataset DS3 with the attribute ‘Quarter’ and metric ‘Cost’.

2.  Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Quarter’, ‘Profit’. Set the source of this grid to be the dataset ‘DS3’.

3. In the executed document, no data is displayed for the metric ‘Profit’ as shown below.

Part 8 - 3

In the above example, the metric ‘Profit’ does not exist in the source dataset ‘DS3’ and exists in more than dataset which are in the document i.e., it exists in both ‘DS1’ and ‘DS2’. Since the engine cannot just randomly pick one of the two available datasets, it chooses not to display any data for this metric. If users do not want such blank columns to be displayed, set the source dataset so that such ambiguity does not arise.

Case 2:

The same metric exists multiple times on the grid. For example, users can have a smart compound metric and a component metric of this compound smart metric on the grid in the document. The smart metric and the component metric are from different datasets.

This case is also explained with an example based on the MicroStrategy Tutorial project.

1. Create the following objects:

a. Dataset DS1 with attribute ‘Year’ and metric ‘Profit’.

b. Dataset DS2 with attribute ‘Year’ and metrics ‘Revenue’, ‘Profit’, ‘Profit Margin’ (this is a compound smart metric calculated from metrics Revenue and Profit).

2. Create a document based on the above datasets and create a grid object on the document with the following objects: ‘Year’, ‘Revenue’, ‘Profit’ and ‘Profit Margin’. The source of this grid object is set to DS1.

3. In the executed document, no data is displayed for the metric ‘Profit Margin’, as shown below.

Part 8 - 4

In the above example, since the source of the dataset is set to ‘DS1’, the ‘Profit’ metric is sourced from this dataset and the metric ‘Revenue’ is sourced from the dataset ‘DS2’ (as this is the ONLY datatset with this metric). However, for the metric ‘Profit Margin’, the component metric ‘Profit’ exists on dataset ‘DS1’, so this becomes a conflict metric and is not displayed. If the source of the grid is changed to ‘DS2’, the data is displayed correctly as shown below.

Part 8 - 5

References:

[1] MicroStrategy Knowledgebase, Why are some metric values blank in documents using multiple datasets in MicroStrategy Analytics Enterprise 9.4.1, TN Key: 44517, 12/16/2013, https://resource.microstrategy.com/support/mainsearch.aspx.

[2] MicroStrategy Knowledgebase, Multiple datasets in a single grid/graph/widget object in MicroStrategy Web 9.4, TN Key: 44944, 09/30/2013, https://resource.microstrategy.com/support/mainsearch.aspx.

NOTE: You may need to register to view MicroStrategy’s Knowledgebase.

Interview Question #5: Analysis Using Enterprise Manager

Question

If you want to analyze the average length of time users must wait for their documents or reports to process, as well as the number of errors that were received in Enterprise Manager, which area of analysis would enable you to track this information?

A.   Performance Analysis

B.   Operations Analysis

C.   Real-Time Analysis

D.   Project Analysis

E.   You cannot accomplish this with Enterprise Manager

Answer

B. Operations Analysis

The Operations Analysis folder in Enterprise Manager contains the following analysis areas, each with its own reports:

  • Concurrency analysis (including user/session analysis)
  • Data load
  • Delivery processing analysis
  • Inbox Message Analysis
  • Report processing analysis
  • Resource utilization analysis (including top consumers)

MicroStrategy Course Where You Will Learn About This Topic

MicroStrategy Administration: Application Management Course

How Data Blending Affects the Analytical Engine’s Behavior in MicroStrategy (Part 7)

MicroStrategy Analytics PlatformWith the release of MicroStrategy Analytics Enterprise 9.4.1, the Analytical Engine logic has been enhanced with respect to joining data from multiple datasets in a Report Services Document. One of the features that is available with this release is the ability to use objects (e.g., attributes, metrics) from multiple datasets in a single grid in a document.

If an attribute on a grid has elements that can be obtained from multiple datasets used in the document, the elements displayed will be from the global lookup table. Additionally, if one or more of the datasets containing the attribute has missing attribute form data or has different attribute form from the other datasets, the Analytical Engine will follow the rules noted below to compose the final output:

Rule 1:

If there is attribute form with null value, the Analytical Engine will use the non-null form value from other datasets instead of the null form.

Rule 2:

If several datasets have different attribute form information for the attribute element, the Analytical Engine will use the attribute form from the biggest dataset.

Rule 3:

If several datasets have different attribute form information for the attribute element, and those datasets have same number of rows, the Analytical Engine will use the first dataset in the document for the attribute form value (according to the dataset adding sequence).

NOTE: Users should note that the rules are applied for each individual attribute element in the result at the row level rather than at the dataset level.

Example 1:

Users may consider the following datasets – C01 is a dataset with Customer City, Customer and Order:

Part 7 - 1a

 

C02 is a dataset with Customer, Order and a profit metric. Users may note that the Customer attribute is missing the DESC form in the second dataset:

Part 7 - 2a

If a Report Services Document is built with both these datasets, and the attributes are placed on a grid, the following results may be seen. As noted in Rule 1, the Analytical Engine will display the non-Null values from C01 for the Customer attribute elements:

Part 7 - 3a

Example 2:

Now users may consider a different dataset as C02 – similar to the initial dataset, but here the Customer name (DESC) form contains values instead of NULLs. This time the values for the attributes are not consistent – see that Customer ID ‘1’ has different values for the DESC form for different Orders (1 & 6).

Customer Name Customer ID Order Profit
Customer D 1 1 100
Customer B 2 2 200
Customer C 3 3 300
Xia D 4 4 400
Kris Du 5 5 500
Customer A 1 6 610
Customer E 2 7 720
Customer F 6 8 860
Customer G 7 9 970
Customer H 8 10 1080

If a report is built for this dataset users will observe that the first attribute element value in the dataset is used as as the DESC form for the Orders 1 & 6 even if the value is different in subsequent rows (this is the same as previous Analytical Engine behavior). Part 7 - 5

When these datasets are used in the grid in a Report Services Document, the Analytical Engine will choose the attribute element values from dataset C02 to display in the attribute element values from. This is because of Rule 2 explained above.

Part 7 - 6a

Example 3:

Consider the following dataset:

Customer Name Customer ID Order Profit
Customer D 1 1 100
Customer E 2 7 720
Xia D 4 4 400
Kris Du 5 5 500
Customer G 7 9 970

A report built off this dataset appears as follows:

Part 7 - 8a

After replacing the dataset ‘C02‘ from the previous example with the new dataset, the following results are seen. As noted in Rule 3, because both C01 an C02 have the same number of rows, the elements displayed for the Customer attribute will be filled from from the first dataset to be added to the document – in this case C01. However for the first row in the results, where there is no corresponding customer in the dataset C01, Rule 1 will be applied and instead of a NULL value, the non-null Customer Name field ‘Customer G’ is picked from C02. (Rules are applied at the individual element level).

Part 7 - 9a

Next: Why are some metric values blank in documents using multiple datasets in MicroStrategy Analytics Enterprise 9.4.1

———————————————————————————-

References:

[1] MicroStrategy Knowledgebase, Engine behavior for grids on a Report Services Document or dashboard with multiple datasets where some attribute forms are missing or have different values the datasets in MicroStrategy Analytics Enterprise 9.4.1 and newer releases, TN Key: 45463, 03/13/2014, https://resource.microstrategy.com/support/mainsearch.aspx.

NOTE: You may need to register to view MicroStrategy’s Knowledgebase.

Interview Question #4: Slowly Changing Dimensions

Question

Which type of Slowly Changing Dimension (SCD) would you use if your data was time dependent and you do not require historical comparisons?

A.  Like vs. Like

B.  As Is vs. As Was (Type II)

C.  As Is vs. As Is (Type I)

D.  As Is vs. As Was

Answer

C.   As Is vs. As Is (Type I) involves analyzing all data in accordance with the attribute relationships as they currently exist.

Regardless of how relationships change over time, you aggregate and qualify all data (current and historical) based on the current values in the lookup and relationship tables. If aggregate tables exist, you either have to modify how the values roll up to reflect the current attribute relationships, or you have to ignore the tables when you perform this type of analysis.

MicroStrategy Course Where You Will Learn About This Topic

MicroStrategy Advanced Data Warehousing Course

 

Forbes: Data Visualization Is The Future – Here’s Why

Readers:

Dorie ClarkI read this blog post from Dorie Clark back in March. I keep notes on interesting blogs and articles I come across and wanted to share this one with you regards the importance of data visualization.

Dorie Clark is a marketing strategist and professional speaker who teaches at Duke University’s Fuqua School of Business. Learn more about her new book Reinventing You: Define Your Brand, Imagine Your Future (Harvard Business Review Press) and follow her on Twitter.

I hope you find this helpful in your data visualization endeavors.

Best regards,

Michael

Data Visualization Is The Future – Here’s Why

We’ve all heard that Big Data is the future. But according to Phil Simon’s new book The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions, that may not be quite right. Big Data is a powerful discovery tool for companies seeking to glean new insights. But without the right framework for understanding it, much of that knowledge may go unrecognized. Oftentimes, it’s data visualization that allows Big Data to unleash its true impact.

The Visual Organization is fundamentally about how progressive organizations today are using a wide array of data visualization (dataviz) tools to ask better questions of their data – and make better business decisions,” says Simon, citing the example of companies such as Amazon, Apple , Facebook, Google, Twitter, and Netflix, among others.

Phil Simon
Data visualization allows Big Data to unleash its true impact, as author Phil Simon explains.

Two recent factors have conspired to make this the moment for data visualization. First, says Simon, is the rise of Big Data and the growing public awareness of its power. “Today more than ever, professionals are being asked to argue their cases and make their decisions based on data,” he says. “A new, data-oriented mind-set is permeating the business world.”

But that push outside IT circles means that many non-technical professionals must now produce and comprehend insights from Big Data. Visualization can help, and a raft of new tools makes that possible. “IBM, Cognos, SAS, and other enterprise BI (business intelligence) stalwarts are still around, but they are no longer the only game in town,” he says. “Today, an organization need not spend hundreds of thousands or millions of dollars to get going with dataviz. These new tools have become progressively more powerful and democratic over the last decade. Long gone are the days in which IT needed to generate reports for non-technical employees. They have made it easier than ever to for employees to quickly discover new things in increasingly large datasets. Examples include Visual.ly, Tableau, Vizify, D3.js, R, and myriad others.”

———————————————————————–
Source: Dorie Clark, Data Visualization Is The Future – Here’s Why, Forbes, March 10, 2014, http://www.forbes.com/sites/dorieclark/2014/03/10/data-visualization-is-the-future-heres-why/.

An Introduction to Data Blending – Part 6 (Data Blending using MicroStrategy)

Readers:

In Part 5 of this series on data blending, we reviewed Tableau’s Data Blending Architecture. With Part 5, I have wrapped up the Tableau portion of this series.

I am now going to post, over the next week or so, several parts discussing how we do data blending using MicroStrategy. Fortunately, MicroStrategy just publish a nice technical note on their Knowledgebase (TN Key: 46940) [1] discussing this. Most of what I am sharing today is derived from that technical note.

I probably will have 2-4 parts for this topic in my Data Blending series including how the MicroStrategy Analytical Engine deals with multiple datasets.

I want to thank Kristi Morton (et al) for the wonderful research paper she wrote at The University of Washington [2]. It helped me provide some real insight into the topic and mechanics of data blending, particularly with Tableau. You can learn more about Ms. Morton’s research as well as other resources used to create this blog post by referring to the References at the end of the blog post.

So, let’s now dig into how MicroStrategy provides us data blending capabilities.

Best Regards,

Michael

Data Blending using MicroStrategy

In Part 6, we will begin examining using data blending in MicroStrategy. We will first look at how to use attributes from multiple datasets in the same Visual Insight dashboard and link them to existing attributes using the Data Blend feature in MicroStrategy Analytics Enterprise Web 9.4.1.

Prior to v9.4.1 of MicroStrategy, data blending was referred to as Cube Joining.

In MicroStrategy Analytics Enterprise Web 9.4.1, the new Report Services Documents Engine automatically links common attributes using the modeled schema whenever possible. The manual linking is not allowed between different modeled attributes. Just in case the requirement needs to link different attributes, this can be done by using MicroStrategy Architect at the schema level. The join behavior by default for linking related attributes is done using a full outer join. In case there is no relationship between the attributes, then a cross join is used.

The manual attribute linking can be done as shown in the images below.

Part 6 - 1

 

2. Browse the file to match the existing data and select Continue.

Part 6 - 2

 

3. Set the attribute forms if needed. MicroStrategy will automatically assign the detected ones.

Part 6 - 3

4. The attributes can be mapped manually by selecting Link to Project Attribute.

Part 6 - 4

5. Select the attribute form that matches the desired join:

Part 6 - 5

6. The attribute should appear similar to the ones existing in the schema as shown below.

Part 6 - 6

 

7. Save the recently created dataset.

Part 6 - 7

8. Now there are two cubes used as datasets in the same Visual Insight dashboard, as shown below.

Part 6 - 7a

Automatic Linking

The attributes icons now have a blue link, as shown below. This indicates that MicroStrategy has automatically linked them to elements in the Information dataset.

Part 6 - 8

Next: How Data Blending Affects the Analytical Engine’s Behavior in MicroStrategy

———————————————————————————-

References:

[1] MicroStrategy Knowledgebase, How to use attributes from multiple datasets in the same Visual Insight dashboard and link them to existing attributes using the Data Blend feature in MicroStrategy Analytics Enterprise Web 9.4.1, TN Key: 46940, 04/24/2014, https://resource.microstrategy.com/support/mainsearch.aspx.

NOTE: You may need to register to view MiroStrategy’s Knowledgebase.

[2] Kristi Morton, Ross Bunker, Jock Mackinlay, Robert Morton, and Chris Stolte, Dynamic Workload Driven Data Integration in Tableau, University of Washington and Tableau Software, Seattle, Washington, March 2012, http://homes.cs.washington.edu/~kmorton/modi221-mortonA.pdf.