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November 2024

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It's a tool for a new kind of business intelligence from Microsoft. Tool for quick modeling of data structures and for visualizing almost everything you have in mind at the moment. There is still a lot of room for improvement and there is also a huge space for new functionalities. But it’s a simple and great tool for everyday use. 

You should get it and implement it. However, you should get a trial version, and contact a partner who can provide some sales presentation with the live session (CIE for example) and show, what the possibilities are of Power BI.

#21: Avoid using discretization buckets for your dimension attributes

Discretization buckets lets you group numerical attributes into ranges. Say you have a customer dimension including the age of the customer you can use this feature to group them into age clusters such as 0-5, 6-10 and so on. While you can tweak how the algorithm creates groups and even provide naming templates for the groups you still have relatively limited control over them. Worst case scenario: A grouping is removed / changed by the algorithm which is referenced in a report. A better way of grouping these attributes is by doing it yourself either in the data source view or a view in the database (there will be a separate tip on this). This way you have complete control over the distribution of values into groups and the naming of the groups.

#22: Do not build a SSAS solution directly on top of your source system

SSAS has a couple of features that enable it to source data directly from a normalized data model typically found in business applications such as ERP systems. For instance you can “fake” a star schema through queries in the data source view. You can also utilize proactive caching to eliminate any ETL to populate your cube with data. This all sounds very tempting but unfortunatly I have never seen this work in reality. Unless you are working with a very small source system with impeccable data quality and few simultanous users you should avoid the temptation for all the usual reasons: Proactive caching will stress your source system, data quality will most likely be an issue, integrating new data sources will be nearly impossible,etc. There is a reason BI projects spend 70-80% of their time working with modelling and integrating data.

#23: Deploy SSAS cubes with the deployment tool

If you are working with multiple environments (dev/test/prod) do not use the deployment functionality of visual studio to deploy to another environment. This will overwrite partitions and roles that may be different between the environments. Use the deployment wizard.

#24: Remember that your SSAS cubes are a single point of failure

Keep in mind that most client tools do not cope well with changes to SSAS data models. Any renames or removals you do in the model will most likely cause clients that reference those entities to fail. Make sure you test all your reports against the changed model before deploying it to production. Also, if you allow ad-hoc access to your SSAS solution be aware that users may have created reports that you do not know about. Query logging may help you a little here (it gives you an indication of which attribute hierarchies are in use). The best way to avoid all of this is to thoughtfully design your cube and the naming of your SSAS objects so that there is no need to change or remove anything in the first place.

#25: Avoid “real time”

“Real time” means different things to different people. Some interpret it as “simultaneous to an event occurring” while others have more leeway and have various levels of tolerance for delays. I prefer the term “latency”: How old can the data in the BI solution get before it needs to be refreshed?. The lowest latency I have ever implemented is two hours. That is hours not minutes. I know this does not sound very impressive but that is honestly the best I have been able to do at a reasonable cost. When doing “real time” you need to consider a lot of factors: Partitioning, changes to dimensions, ROLAP vs MOLAP / direct query vs xVelocity, source system access, how to administer it, etc., etc. These things add up quickly to a point where the value simply does not justify the cost.

#16: Implement reporting dimensions in your SSAS solution

Reporting dimensions are constructs you use to make the data model more flexible for reporting purposes. They usually also simplify the management and implementation of common calculation scenarios. Here are two examples:

• A common request from users is the need to select which measure to display for a given report in Excel through a normal filter. This is not possible with normal measures / calculations. The solution is to create a measure dimension with one member for each measure. Expose a single measure in your measure group (I frequently use “Value”) that you assign the correct measure to in your MDX script / DAX calculation based on the member selected in the measure dimension. The most frequently used measure should be the default member for this dimension. By doing this you not only give the users what they want, but you also simplify a lot of calculation logic such as the next example.
• Almost all data models require various date related calculations such as year to date, same period last year, etc. It is not uncommon to have more than thirty such calculations. To manage this effectively create a separate date calculation dimension with one member for each calculation. Do your time based calculations based on what is selected in the time calculation dimension. If you implemented the construct in the previous example this can be done generically for all measures that you have in your measure dimension. Here is an example for how to do it tabular. For dimensional use the time intelligence wizard to get you started.

#17: Consider creating separate ad-hoc and reporting cubes

Analysis Services data models can become very complex. Fifteen to twenty dimensions connected to five to ten fact tables is not uncommon. Additionally various analysis and reporting constructs (such as a time calculation dimensions) can make a model difficult for end users to understand. There are a couple of features that help reduce this complexity such as perspectives, role security and default members (at least for dimensional) but often the complexity is so ingrained in the model that it is difficult to simplify by just hiding measures / attributes / dimensions from users. This is especially true if you use a “reporting cube” which I talked about in tip #16. You also need to consider the performance aspect of exposing a large, complex model to end user ad-hoc queries. This can very quickly go very wrong. So my advice is that you consider creating a separate model for end users to query directly. This model may reduce complexity in a variety of ways:

• Coarser grain (Ex: Monthly numbers not daily).
• Less data (Ex: Only last two years, not since the beginning of time).
• Fewer dimensions and facts.
• Be targeted at a specific business process (Use perspectives if this the only thing you need).
• Simpler or omitted reporting dimensions.

Ideally your ad-hoc model should run on its own hardware. Obviously this will add both investment and operational costs to your project but will be well worth it when the alternative is an unresponsive model.

#18: Learn .NET

A surprisingly high number of BI consultants I have met over the years do not know how to write code. I am not talking about HTML or SQL here but “real” code in a programming language. While we mostly use graphical interfaces when we build BI solutions the underlying logic is still based on programming principles. If you don’t get these, you will be far less productive with the graphical toolset. More importantly .Net is widely used in Microsoft based solutions as “glue” or to extend the functionality of the core products. This is especially true for SSIS projects where you quite frequently have to implement logic in scripts written in C# or VB.net but also applies to most components in the MS BI stack. They all have rich API’s that can be used for extending their functionality and integrating them into solutions.

#19: Design your solution to utilize Data Quality Services

I have yet to encounter an organization where data quality has not been an issue. Even if you have a single data source you will probably run into problems with data quality. Data quality is a complex subject. Its expensive to monitor and expensive to fix. So you might as well be proactive from the get-go. Data Quality Services is available in the BI and Enterprise versions of SQL Server. It allows you to define rules for data quality and monitor your data for conformance to these rules. It even comes with SSIS components so you can integrate it with your overall ETL process. You should include this in the design stage of your ETL solution because implementing it in hindsight will be quite costly as it directly affects the data flow of your solution.

#20: Avoid SSAS unknown members

Aside from the slight overhead they cause when processing, having unknown members means that your underlying data model has issues. Fix them there and not in the data model.

# 6: Use a framework for your Integration Services solution(s) because data is evil

I know how it is. You may have started your ETL project using the SQL Server import / export wizard or you may have done a point integration of a couple of tables through data tools. You might even have built an entire solution from the ground up and been pretty sure that you thought of everything. You most likely have not. Data is a tricky thing. So tricky in fact that I over the years have built up an almost paranoid distrust against it. The only sure thing I can say is that it will change (both intentionally and unintentionally) over time and your meticulously crafted solution will fail. Best case scenario is that it simply will stop working. Worst case scenario is that this error / these errors have not caused a failure technically but have done faulty insert / update / delete operations against your data warehouse for months. This is not discovered until you have a very angry business manager on the line who has been doing erroneous reporting up the corporate chain for months. This is the most likely scenario. A good framework should have functionality for recording data lineage (what has changed) and the ability to gracefully handle technical errors. It won’t prevent these kinds of errors from happening but it will help you recover from them a lot faster. For inspiration read The Data Warehouse ETL Toolkit.

#7: Use a framework for your Integration Services solution(s) to maintain control and boost productivity

Integration Services is a powerful ETL tool that can handle almost any data integration challenge you throw at it. To achieve this it has to be very flexible. Like many of Microsoft’s products its very developer oriented. The issue with this is that there are as many ways of solving a problem as there are Business Intelligence consultants on a project. By implementing a SSIS framework (and sticking with it!) you ensure that the solution handles similar problems in similar ways. So when the lead developer gets hit by that bus you can put another consultant on the project who only needs to be trained on the framework to be productive. A framework will also boost productivity. The up-front effort of coding it, setting it up and forcing your team to use it is dwarfed by the benefits of templates, code reuse and shared functionality. Again, read The Data Warehouse ETL Toolkit for inspiration.

#8: Test and retest your calculations.

Come into the habit of testing your MDX and DAX calculations as soon as possible. Ideally this should happen as soon as you finish a calculation, scope statement, etc. Both MDX and DAX get complicated really fast and unless you are a Chris Webb you will loose track pretty quickly of dependencies and why numbers turn out as they do. Test your statements in isolation and the solution as a whole and verify that everything works correctly. Also these things can have a severe performance impact so remember to clear the analysis services cache and do before and after testing (even if you have cache warmer). Note that clearing the cache means different things to tabular and dimensional as outlined here.

#9: Partition your data and align it from the ground up.

Note that you need the enterprise version of SQL Server for most of this. If you have large data sets you should design your solution from the ground up to utilize partitioning. You will see dramatic performance benefits from aligning your partitions all the way from your SSIS process to your Analysis Services cubes / tabular models. Alignment means that if you partition your relational fact table by month and year, you should do the same for your analysis services measure group / tabular table. Your SSIS solution should also be partition-aware to maximize its throughput by exploiting your partitioning scheme.

#10: Avoid using the built-in Excel provider in Integration Services.

I feel a bit sorry for the Excel provider. It knows that people seeing it will think “Obviously I can integrate Excel data with my SSIS solution, its a MS product and MS knows that much of our data is in Excel”. The problem is that Excel files are inherently unstructured. So for all but the simplest Excel workbooks the provider will struggle to figure out what data to read. Work around this by either exporting your Excel data to flat files or look at some third party providers.

Having worked with Microsoft BI for more than a decade now here are the top 30 things I wished I knew before starting development of a solution. These are not general BI project recommendations such as “listen to the business” or “build incrementally” but specific lessons I have learned (more often than not the hard way) designing and implementing Microsoft based Business Intelligence solutions. So here are the first five:

#1: Have at least one SharePoint expert on the team.

The vast majority of front-end BI tools from Microsoft are integrated with SharePoint. In fact, some of them only exist in SharePoint (for instance PerformancePoint). This means that if you want to deliver Business Intelligence with a Microsoft solution, you will probably deliver a lot of it through SharePoint. And make no mistake: SharePoint is very complex. You have farms, site collections, lists, services, applications, security… the list goes on and on. To make matters worse you may have to integrate your solution with an already existing SharePoint portal. There is a reason there are professional SharePoint consultants around, so use them.

#2: Do not get too excited about Visio integration with Analysis Services.

Yes, you can query and visualize Analysis Services data in Visio. You may have seen the supply chain demo from Microsoft which looks really flashy. You might think about a hundred cool visualizations you could do. Before you spend any time on this or start designing your solution to utilize it, try out the feature. While its a great feature, it requires a lot of work to implement (at least for anything more than trivial). Also, it (currently) only supports some quite specific reporting scenarios (think decomposition trees).

#3: Carefully consider when to use Reporting Services.

Reporting Services is a great report authoring environment. It allows you to design and publish pixel perfect reports with lots of interactivity. It also provides valuable services such as caching, subscriptions and alerts. This comes at a cost though. The effort needed to create SSRS reports is quite high and needs a specialized skill set. This is no end user tool. There are also issues with certain data providers (especially Analysis Services). But if you need any combination of multiple report formats , high scalability (caching, scale-out), subscriptions or alerts, you should seriously consider Reporting Services.

#4: Use Nvarchar / unicode strings throughout the solution.

Unless you live in the US (and are pretty damn sure you will never have “international data”) use unicode. Granted, varchars are more efficient but you do not want to deal with collations / codepages. Ever. Remember this is not only an issue with the database engine but also with other services such as Integration Services.

#5: Check if it exists on codeplex.

Do not build anything before you have checked codeplex. Chances are someone has already done the same or something similar that can be tweaked. If you are skeptical of including “foreign” code in your solution (like me) use the codeplex code as a cheat-sheet and build your own based on it. There is a lot stuff there including SSAS stored procedures, SSIS components and frameworks and much more.

Disclosure: The company I work for is a Microsoft Partner
[Syndicated from www.peterkollerbi.wordpress.com]

What needs improvement?

For how long have I used the solution?

What do I think about the stability of the solution?

What do I think about the scalability of the solution?

What other advice do I have?

What is most valuable?

What needs improvement?

For how long have I used the solution?

What do I think about the stability of the solution?

How are customer service and support?

What's my experience with pricing, setup cost, and licensing?

Which other solutions did I evaluate?

What other advice do I have?

Which deployment model are you using for this solution?