Tag Archive for: Best Practices

Power BI – Best Practices for Metadata Modeling using Composite Models

/by

Microsoft recently released one of the most sought after features for Power BI. As of the December 2020 update, there is a new preview feature that allows you to build a partitioned metadata model in Power BI. Formally called “DirectQuery for Power BI datasets and Analysis Services”, this is essentially just an expansion of the Composite Model feature introduced in 2018. Previously, Composite Models did not support live connections to published Power BI datasets and Azure Analysis Services, but that is no longer the case with the latest enhancement.

So what is “DirectQuery for Power BI datasets and Analysis Services”? With this feature, Power BI users can now build an enterprise semantic Power BI metadata model for sharing (and enhancement!) across their organization. Starting with this “master” model, individual departments can quickly and flexibly extend and enhance the model as business needs dictate.  Updates made to the underlying master model are seamlessly inherited without impacting business continuity.

For example, IT creates a Product Master or Customer Master model in PowerBI.com as a dataset. Later, individual departments can then import and enrich the enterprise model to meet their own needs. If IT decides to update the Master model, all the dependent data set models will automatically inherit the updates.

The release of this functionality greatly enhances an organization’s ability to support and drive governance and master data efforts by allowing the promotion of a “single source of the truth” through Power BI.

So what are the details of this new feature? How does it really work?

  • Begin by publishing your enterprise Power BI dataset to powerbi.com.  An example might be a Customer Master dataset. 
  • Next, in Power BI desktop, enable the new preview feature.
  • You can now connect live to the existing dataset publish in Power BI Services.
  • Just like any other data source in Power BI desktop, you can extend the model by connecting to more data sources (e.g. an Excel file or a SQL database) and combine them into one semantic model, while still maintaining the relationship to the original enterprise model. If the enterprise model’s data is updated, then the newly created composite model will also see the new data.
  • A composite model does require you to have a local model. By default, if your PBIX model only has a live connection to your source data (like AAS) then you will not have a local metadata model in your PBIX file. If you start from a live connection to a data set then you’ll need to convert to a DirectQuery connection before you can import additional datasets. This will happen automatically once you import another data source. A local model is needed to “extend” the metadata model to include any new data source like Excel or MS SQL.
  • You can now extend and enhance your model by linking together additional data sources.  Important to understand is that extending beyond a “chain” length of 3 is not currently supported. Effectively this means that one cannot extend a data model that has already been extended. For example, if you publish an extended model to Power BI Service then another user can not further extend the model with additional data.

Additional suggestions to be aware of:

  1. If row level security is defined in the master model, it will be available in the extended model but it will not automatically apply to any newly imported data sources. You’ll need to define row level security on any data sources that you import into your composite model.
  2. Display folders, KPIs, date tables, row level security, and translations will not be imported from the source in this preview release. You can still create display folders in the local model. This maybe resolved before General Availability.
  3. You may see some unexpected behavior when using a date hierarchy. To resolve this issue, use a date column instead. After adding a date hierarchy to a visual, you can switch to a date column by clicking on the down arrow in the field name, and then clicking on the name of that field instead of using Date Hierarchy:
  4. Using on premise SQL Server Analysis Services (SSAS) as a DirectQuery source is not currently supported but Azure Analysis Services (AAS) is supported.

 “DirectQuery for Power BI datasets and Analysis Services” preview feature is a major step toward creating centralized and standardized metadata for any organization. There is no need to waste time remodeling and maintaining master data for each and every Power BI model. You can now simply extend your semantic models by leveraging a standard set of master metadata that is maintained in a central Power BI dataset. You can think of this new preview feature as Composite Models 2.0.

,

Ironside Uncovers TM1 SSL Certificate Expiration Issue

/by

Ironside recently reported a new issue to IBM that is now confirmed as having an impact on all TM1 sites regardless of version. The Ironside Managed Services team uncovered the issue while troubleshooting a client’s environment.

“In our efforts to secure an environment for one of our larger insurance clients, it was noticed that the SSL certificate was going to expire shortly,” said Erik Romanek, Ironside’s Director of Platform and Managed Services. “John Commons, one of Ironside’s platform consultants, worked with IBM to come up with a few solution options. This is just another example of Ironside going the extra mile to make sure the implementations we work on are configured securely and for longevity.” Read more

,

The Newbie Data Science Guide: Beginning Advanced Analytics

/by

Many of you have heard buzzwords such as “data science,” “big data,” or the “Internet of Things” before. You’re able to piece together that these fields relate to each other and deal with analyzing data in some way, but maybe you’re not so sure what these terms really mean. That’s what I’m here to help with.  As a newer member of the data science field, I developed this short data science guide based on my experiences and perspectives in an effort to help those who are just starting out. Read more

5 Ways to Drive Analytics Adoption: Engage Your Organization

/by

Let’s go back a few years. You and your company invested significant amounts of time, effort, and capital to implement a business intelligence environment meant to serve the analytic needs of your organization and help drive insights. You and your team spent extra effort in developing the metadata and reporting that the business said they required, yet you’ve been hearing about users downloading free tools for analysis, your development request backlog has dried up, and a quick look at your audit logs shows flat, or worse, declining usage of your environment. What’s going on? Read more

,

Why Your Dashboard Sucks: Design Thinking at Gartner BI

/by

This year at the Gartner BI & Analytics Summit in Grapevine, Texas, Ironside’s own Greg Bonnette delivered a presentation that got his audience questioning just what it is that makes a good dashboard. During his talk, Why Your Dashboard Sucks, Greg used the all-too-familiar staple of BI development as a gateway into a much wider discussion of design thinking and its applications in a business analytics setting. Read more

,

How Data Management and Governance Can Enable Successful Self-Service BI

/by

Ironside’s Crystal Meyers was featured in Vol. 19, No. 4 of TDWI’s Business Intelligence Journal. Her points are particularly relevant as business analytics begins a more pronounced shift toward a bimodal model in 2016.

Reprinted with permission of 1105 Media, Inc. As published in TDWI’s Business Intelligence Journal. Read more

,

Recipes for Predictive Modeling: Highly Imbalanced Data

/by

Suppose you’re trying to build a model to predict respondents, and in your data set, about 3% of the population will respond (target = 1). Without applying any specific analysis techniques, your prediction results will likely be that every record is predicted as a non responder (predicted target = 0), making the prediction result insufficiently informative. This is due to the nature of this kind of information, which we call highly imbalanced data. Read more

,

Ironside Tech Tip: Where to Apply Filter Strategies – In the Database, in the Package, or in the Report?

/by

Guarding the security of and defining access to data is often a necessary business requirement. Not all users can or should have access to a total data set, so filter strategies need to be applied to restrict what appears to different users running reports. As Cognos developers, the first question we should ask when faced with this requirement is what filter strategies work best for our data and why? Read more

3 Keys to Strong Data and Analytics MSP Relationships: How to Pick a Partner, Not Just a Provider

/by

Thanks to the widespread availability of the cloud and the rampant growth of the remote options it enables, these days it feels like everyone is looking for a Managed Services Provider (MSP) to help manage different aspects of their environment. Whether they provide firewalls, antivirus solutions, or business analytics platforms, MSPs abound. There are many ways to assess what these providers bring the table, but what are the most important characteristics to look for? As a provider and consumer of these services, I know what I strive to offer and what I look for from my vendors. In this article, I’ll illuminate my thought process for you in the hopes that it can set you on the road to successful, rewarding MSP relationships. Read more

,

Ironside Tech Tip: Conditional Blocks – Applications and Usages

/by

Synopsis

How to create, implement and use conditional blocks in Cognos reports.

Overview

Conditional blocks show different display results based on user interaction or parameters read in when a report is run. They provide a creative method for presenting either the same report data in various formats or executing several completely different reports from one prompt page. Read more