Tag Archives: Analysis Services

DAX Performance tips– lessons from the field

XLCubed has supported a drag/drop interface for creating reports against Tabular Analysis Services since the first release of the new engine. It lets users easily create reports which run DAX queries on the cube, and we’ve often seen very good performance at customers when MDX against Tabular was a cause of long running reports.

So when we were approached at SQL Pass in Seattle by some attendees who had a SSAS Tabular performance issue we were optimistic we’d be able to help.

In this case the business wanted to retrieve thousands of rows from the cube at the transactional level, and the first approach had been to use PivotTables in Excel. To get to the lowest level they cross-joined the lowest levels of all the hierarchies on the rows section which would give the right result, but performance was terrible, with several queries taking 20 minutes or more and others not returning at all.

We hoped using an XLCubed table running DAX would be the solution and created the same report in the designer. Sadly while performance was a little better it was still far from acceptable; the model was large, and the number or columns combined with their cardinality meant that a lot of work was being done on the server.

XLCubed’s DAX generator was trying to cross-join all the values from each column, which had worked well for our other customers. But when there are a dozen columns including the transaction ID things do not go so well. DAX in itself is not a magic bullet and SSAS Tabular models can hit performance problems on low level data – we needed a new approach.

After some investigation we discussed the issue and our thinking with our friends at SQLBI and determined that instead of cross-join we wanted an option to use Summarize() instead as this only uses the rows in the database, and it can access columns related to the summarized table which were required for the report.

As the customer’s report had the transaction ID in it the result wasn’t aggregated, even though we were using summarize. But we wanted to add true transactional reporting too, using the Related() function.

Finally, SQL 2016 adds a couple of new functions, SummarizeColumns() and SelectColumns(), both of which are useful for this type of reporting, but offer better performance than the older equivalents.

The end result in XLCubed is a new option for DAX tables to allow users to set the type of report they want to run, and some internal changes so that XLCubed will automatically use the most efficient DAX function where they are available.

A beta was sent to the business users and the results were fantastic. The report which had run for several minutes now completed in a few seconds, and 20 minutes was down to 15 seconds – we had some very happy users!

The changes will be in the next release of XLCubed so that all our customers can benefit from the improvements. It’s always nice when a customer request helps improve the product for everyone.

A sample of the syntax change is included below

Before:

 

EVALUATE
FILTER (
    ADDCOLUMNS (
        KEEPFILTERS (
            CROSSJOIN ( VALUES ( 'Customer'[Education] ), VALUES ( 'Product'[Color] ) )
        ),
        "Internet Total Units", 'Internet Sales'[Internet Total Units],
        "Internet Total Sales", 'Internet Sales'[Internet Total Sales]
    ),
    NOT ISBLANK ( [Internet Total Units] )
)
ORDER BY
    'Customer'[Education],
    'Product'[Color]

After:

 

EVALUATE
FILTER (
    ADDCOLUMNS (
        KEEPFILTERS (
            SUMMARIZE ( 'Internet Sales', 'Customer'[Education], 'Product'[Color] )
        ),
        "Internet Total Units", 'Internet Sales'[Internet Total Units],
        "Internet Total Sales", 'Internet Sales'[Internet Total Sales]
    ),
    NOT ISBLANK ( [Internet Total Units] ) || NOT ISBLANK ( [Internet Total Sales] )
)
ORDER BY
    'Customer'[Education],
    'Product'[Color]

Report Flexibility, with Control

Sometimes we want to let report users modify the structure of a report but to govern exactly what they can and can’t do. While Grids can be restricted at a granular level to enable and disable functionality, that approach still requires some degree of product knowledge by the user.

XLCubed provides the XL3SetProperty() formula, which enables manipulation of many of the core objects such as Grids, Slicers and Small Multiples. It means report users can have simple slicer selections to change the structure of a report, what’s being displayed in a chart, or to vary the chart type. It gives flexibility within the report, but requires no product knowledge from the end user which can be crucial when delivering web reports on a widespread basis.

One common example of usage is where the hierarchy to be viewed in a grid needs to change based on the measure a user selects (depending on the structure of the cube some measures may not be applicable for all hierarchies). Typically that would need to be handled in two Grids, but we can use XL3SetProperty to bring this together, and also to give user choice on the associated Small Multiple Chart view.

The final published report is shown below:

 

S1

 

If the user selects an “Internet” measure, we show Customer Geography on rows, whereas a “Reseller” measure should show Reseller Type on rows. The same logic applies to the Small Multiple chart. In the screenshot below, the user has selected Reseller Gross Profit as the measure, and ‘Stacked Column’ as the chart type. You can see that the hierarchy on rows has been switched, as has the split within the individual charts, allowing the user to easily vary their view of the data with simple button selectors.

 

S2

 

This is implemented through the following key points:

  • A lookup table in Excel to determine what hierarchy is applicable for each measure
  • An Excel list showing the available chart types – this is used in the Chart Type slicer
    • The chart slicer outputs its selection into cell $AG$10
  • The measure slicer is linked directly to the grid and the small multiple, but also outputs its selection to an Excel cell ($A$B4)
  • A vlookup determines which hierarchy to use based on the selected measure
  • Three XL3SetProperty() formulae now control what is displayed based on user selections:
    • $AB$7 – sets the grid rows
    • $AB$8 – sets the small multiple columns
    • $AB$7 – sets the chart type

 

Formulae

 

The approach gives a deep level of access to the key XLCubed reporting objects, and enables controlled flexibility within web and mobile-delivered reports. No programming is needed, just a mid-level understanding of Excel itself, and XLCubed.

This is just one example of what the approach can achieve – it’s really limited only by imagination. See XL3SetProperty() for more detail, or contact us if you’d like the example workbook.

Finding a needle in a haystack – Member Searching made easy!

Searching for specific elements of large hierarchies can be a real pain in many Analysis Services client tools, and we often hear of it as a major frustration in Pivot Tables where dialogs can be cumbersome and prone to locking up.

XLCubed has both a Quicksearch and an Advanced search in the Member Selector, but in this blog we’ll show how to link the search dynamically to an Excel cell (or a web entry cell on a published report) and to retain the search as a dynamic part of the report rather than a point in time selection.

Let’s say we are a retailer with a large product hierarchy running to tens or hundreds of thousands of products. The naming convention means groups of products can be searched by a partial match on their name, and as a report designer we’d like the users to be able to type the search in as quickly and easily as possible rather than go into a custom search dialog. Here’s how:

Below is the final result in Excel, a simple list-report where the user just types the text they want to search the hierarchy for, and matching products are shown on the rows of the report.

Search1

 

We start with a regular grid, putting Product Categories on rows, and then in the Member Selector we can either select a specific level or set of data to be searched, or go to the Advanced tab and select the whole hierarchy as shown below.

EditHierarchy

 

 

In the advanced dialog, click on the binoculars:

Binoculars

 

to add a search, and then in the dialog below you can either type a search term directly in the ‘Search Value’ or reference an Excel cell, in this case $C$3. ‘Search By’ allows you to specify exact match, begins, contains etc.

Search2

 

At this point it’s worth mentioning that while in this case we are just searching by the name of the product (MEMBER_CAPTION) we could also chose to search by any member properties which exist.

So having done that we simply type the search string into $C$3 and we get the matching products straight away – couldn’t be easier.

To make this available for web deployed reports there are two additional steps:

  • Make $C$3 available for web input. To do that right click on the cell and choose Format cells, and then on the protection tab uncheck ‘locked’.
  • Add a search or refresh hyperlink or button so that the web user can refresh the report when they’ve typed the search term. This can be handled using either XL3Link() or XL3Picturelink and the process is described in our previous blog.

The web version is shown below:

Websearch

XLCubed V7 & SQL Server 2012

SQL server 2012 has recently been released to manufacturing, and at XLCubed we’re well placed to take advantage of everything that is new in 2012.

SQL 2012 delivers Business Intelligence under the ‘BISM’ umbrella (Business Intelligence Semantic Model). BISM comes in different flavours though:

  • BISM Multi-dimensional
    • (Latest version of Analysis Services as we know it)
  • BISM Tabular
    • In-Memory Vertipaq
    • Direct Query

For client tools, BISM Multi-dimensional is largely the same as connecting to existing versions of Analysis Services, with MDX being the query language. For XLCubed we can leverage what we already have in that respect, and the transition is seamless.

BISM tabular is different though. If you choose to deploy in-memory to Vertipaq, client tools can still use MDX, and as such don’t need significant change, other than to handle the tabular rather than hierarchical data environment. However if the deployment is Direct Query (for example for real-time BI), the only available query language is DAX.

There are best use cases for the different deployment options, but it’s fair to say there is a degree of confusion in the space at the moment about the relative merits of each. We’ll try to shed some light and guidance here over the next weeks and months. As a product though, it’s important for us to support and extend the full range of 2012 BI deployment options, and to make these available and accessible to our customers. That’s exactly what we’ve done for version 7.

XLCubed v7, which releases next month, is a client for both MDX and DAX, and as such provides one consistent client interface in Excel and on the Web which can access any of the SQL 2012 deployment models for BI. We are also adding a much richer relational SQL reporting environment.

We are really pleased with some of the beta feedback we’ve had to date, and if you’d like to trial the beta version contact us at beta@xlcubed.com .

We’re looking forward to releasing the product next month, and will be previewing it at SQL Server Connections next week in Vegas.

 

Solve order shenanigans

Today I’m going to blog about a problem we recently solved in a client’s cube, an error in the Mdx script that’s very easy to make if you aren’t careful.

We’ll run a simple example in AdventureWorks (what else?) to demonstrate the issue.

The client had already added a calculation to their cube to show year-on-year growth. The formula is:

Create Member CurrentCube.[Measures].[Delta to PrevYear] as
(
    ([Measures].[Internet Sales Amount])
    -
    ([Measures].[Internet Sales Amount],
        ParallelPeriod(
            [Date].[Calendar].[Calendar Year],
            1,
            [Date].[Calendar].CurrentMember
        )
    )
)
/
    ([Measures].[Internet Sales Amount],
        ParallelPeriod(
            [Date].[Calendar].[Calendar Year],
            1,
            [Date].[Calendar].CurrentMember
        )
    )
, Format_String = "0.00%";

(some error checking removed for clarity)

This screenshot shows a couple of simple XLCubed Grids showing the real value, and below the percentage change. I have added in an Excel calculation to show the results are as expected.

Later during the cube development, the client added a calculated member in their Product dimension, one that gives a total excluding one of the product categories.

To replicate this I’ll add a calculation for “All Ex Bikes”:

Create Member 
CurrentCube.[Product].[Product Model Categories].[All Products].[All Ex Bikes]
as
(
    ([Product].[Product Model Categories].[All Products])
    -
    ([Product].[Product Model Categories].[Category].&[1])
);

And if we run the report again we get the following.

Notice the cell I’ve highlighted. The “All Ex Bikes” calculation works fine on the normal measure, but it gives totally the wrong number for the percentage calculation. What’s going on?

The problem is that in the cell highlighted Analysis Services has two calculations to think about when working out the result.

  • Compare this year to last year
  • Get the “Grand Total”, and subtract “Bikes”

As the number returned is 1.85% we can see that Analysis Services has chosen the second option, “Grand Total” – “Bikes”.

What we really want is for the calculation to be done by getting the subtotal, and then doing the percentage change based on that.

Fortunately the fix was a simple one. Analysis Services will run the calculations in the order they are found in the Mdx Script, so to fix the issue we simply moved the new “All Ex Bikes” definition up above the percentage calculation.

Now the number returned matches our expectations.

Pass/Solve Order can be a complex topic, so you may need to be careful.

In this case the number is totally wrong, so it was easy to spot, but some bugs will be much more subtle, so watch out!

Ranking, Sorting and Filtering

Once we have returned cube members into a grid report we often need to exclude or change the order of the result set to provide more meaningful information. MDX (Multidimensional Expressions) language includes some very useful operators to provide filtering (FILTER), sorting (ORDER) and ranking (TOPCOUNT/BOTTOMCOUNT) of dimension members. These can be quite overwhelming even for power users of XLCubed.  So, in V6, we have introduced a new feature “Advanced Member Selections” to provide easy access to this powerful part of Microsoft Analysis Services.

Using this new functionality we can nest and combine these operations to answer complex business questions (for simpler operations you can right-click on a member in the grid and use the “Apply” menu to perform simple ranking, filters and sorting).

Filtering

So let’s go through a simple filtering example.  Say, for example, that we want to find the products at Product Key level that sold more than 25 units in 2003, Quarter 1 and show the sales figures for those subcategories during 2003 and its quarters.

  1. Start by clicking the Grid ribbon item (or the XLCubed > Design Grid menu item in Excel 2003 and below), and selecting the Internet Sales cube file
  2. Drag Calendar Period to Columns and Product to Rows. You can also drag any other hierarchies to Headers. In the example image below, Measures and Customer have been added there.

  1. Click on the Product hierarchy so that its details appear in the bottom-right panel.
  2. Drag the Product key level over to the right of the dialog. You can switch between the members view and levels view by clicking on the Show Levels icon ().
  3. Click the Advanced tab to show the advanced selection pane:

  1. Click the Members drop down and choose Filter result:


  1. Click the Calendar Period edit control in the grid to change its selection to the desired member (2003, Quarter 1):

  1. Select the This measure radio button, and select Order Quantity as the desired measure.
  2. Change the Operation to >, and type 25 in the edit field on the right:

  1. Click OK. The new filter is displayed in the advanced selections tab:

  1. Click OK again to run the Report – the Grid shows the members that fit our criteria:

 

So we can see the results, filtering by 2003 Q1, but displaying the values for All Time (or any other period we wish to use). We could have also used the Range selector:    to drive the period selecting from an Excel Range and our grid would automatically refresh whenever the driving value changes.

Ranking

Now let’s add a ranking to find the bottom 8 selling products at the Product Key level that have sold more than 25 units inQ1:

  1. Display the Product Hierarchy Editor dialog
  2. Click the Rank result icon () on the advanced selections tab to display the Edit Ranking dialog
  3. Select the Bottom radio button, and type 8 into the edit field
  4. Select 2003, Quarter 1 for the Calendar Period hierarchy in the grid below:

We now have the filter, following by the ranking:

 

Run the Grid: only the lowest 8 members are returned

 

Sorting

Now let’s sort the report on a different dimension – for example, descending order of the Q1 sales.

  1. Display the Hierarchy Editor for the Product hierarchy by double-clicking on the Product label in the Grid
  2. If it’s not already visible, select the Advanced tab
  3. Click the Sort result toolbar button ()
  4. Change the Calendar Period selection to 2003, Quarter 1:

  1. Click the Sort Descending (9-1) radio button
  2. Click OK. The new sort is displayed in the advanced selections tab
Click OK again to run the Report

 

Joining Results

It’s also possible to join different results together: combining both sets (UNION), excluding members (EXCEPT) and returning common members (INTERSECT).

So we could also add the top 10 products  along side the bottom 8 products to the grid. Begin by adding another member selection using the “Add Member List” tool-bar button:

As before, we select the list of members to rank (in this case the Product Key level) and then select the operation we want to perform, a Top 10:

There are various options to decide how to combine the lists, we’ll stick with Add:

 

 

And we get both results combined:


So the “Advanced Member Selections” feature provides lots of the power of Analysis Services in a simplified way  – to try this feature for yourself you can begin by downloading XLCubed.

Small Multiples on River Quality

The phrase small multiple was popularised by Edward Tufte, and has become a generic term for a visual display using the same chart or graphic to display different slices of a data set. Their close positioning and shared scale make comparisons very easy and shared trends or outliers can be quickly spotted. Various other terms are also used to describe this charting approach, or specific aspects of it, including Trellis Charts, Lattice Charts, Grid Charts and Panel Charts.

The most common use case for small multiples is separate line charts to compare trend across a large number of varying elements. Placing them all within one chart would cause either a ‘spaghetti chart’ , or lots of occlusion as shown in the comparison below. Here we use a standard Excel line chart, and an XLCubed small multiple to chart the same data. Separating the charts while keeping a consistent axis scale makes for a much easier comparison than in the single chart.

We took a slightly different approach when using small multiples to take a look at differences in river water quality across regions of the UK. Our source data was not absolute numeric values, but 14 years of results categorised into four bandings (bad, poor, fair and good). We wanted to provide a ‘one-pager’ which gave a feel for the trend within each region, but also access to the annual breakdown of the different water qualities.

In the end we settled on a Small Multiple display of 100% stacked columns as shown below.

A percentage base seemed a sensible way to approach the data, as different regions will have differing numbers of rivers and of samples taken. Using this approach we’re able to see a comparison of the relative water quality rather than dealing in absolutes.

The user selects a geographic area of the country to view the regional breakdown within the selected area. The water quality for a particular year can be analysed by locating the region, and the specific year to see the percentage breakdown for each of the four categories.

The colouring of the 4 categories was chosen to aid ‘at a glance’ recognition of the overall water quality by region, and also of the trend. Dark blue signifies bad quality water (opaque), and light blue signifies good quality (think ‘you can see right through it….’).

So to read the display overall, or for trend:
• Dark colour signifies water quality problems.
• Light colour signifies good quality water.
• Reading left to right, increasing colour saturation shows declining quality over time.
• Reading left to right, decreasing colour saturation shows improving quality over time.
• Any region can be zoomed in on to see a larger chart and understand the breakdown in more detail.

Fairly quickly, and from just this one display we can draw a number of conclusions as below:
• Across the region, as a broad brush summary, water quality has improved since 1992.
• Doncaster has shown strong and steady improvement.
• Kingston upon Hull has the worst quality overall in the region, and varies significantly year on year.
• If you’re off for a swim in a Yorkshire river, Richmondshire looks a good bet!

We’ve designed a pre-set view in this case to work for the data in question, but the small multiple concept is also very powerful when interactively exploring data. A picture can tell a thousand words as they say – take a look at our youtube videos on small multiples: Video1 Video2

 

Sql Server “Denali” CTP3 – first impressions…

Microsoft recently released their third CTP of Denali the upcoming SQL Server release (SQL Server 2011), so here are some initial thoughts now it’s more widely available.:

The first thing to look at is the new Tabular mode for Analysis Services (as opposed to the traditional multi-dimensional mode, which is still available). This is the server version of the VertiPaq engine first seen in the PowerPivot add-in, and moves the engine from being a personal/team tool to an organisation/enterprise level affair.

This means IT are going to get involved (and people can disagree about how they feel about that!), but that report sharing should be easier as data is held centrally. In the past the report contained all the data, which could make for very large workbooks, or you published to SharePoint, which not everyone was set up to do.

Cubes can be queried using MDX, which is great for a front-end vendor like us, and XLCubed works out of the box against the CTP. Existing functionality is working smoothly, and as Microsoft Gold Partners we’re working closely with the releases to utilise all the functionality for the RTM.

We have ported a few existing cubes to the new architecture and one first impression is that removing columns or using perspectives is going to be needed to keep things sensible for end-users, you can quickly end up with hundreds of attributes.

The ability to create hierarchies was something that was often asked for in PowerPivot, and thankfully that’s there now. This should simplify many cubes.

Attribute-tastic

 

The intricacies of MDX put most business users off trying to use it directly, whereas DAX’s similarities with Excel functions means there is more scope to have users create formulae on the fly. Examining how best to expose that to users is something we’ll be spending some time on in the coming months..

Easier distinct counts and the built in date calculations are the obvious candidates, but there are a number of others which we feel we can make more accessible for the majority of users.

It’s certainly an interesting move, and thinking in Tables and Columns instead of the Multidimensional model takes some getting used to, conversely for some people its more natural.

It’ll also be interesting to see how MDX and DAX are integrated. The Tabular server supports both languages for query. Currently using MDX you can use the “With Member” syntax to create members sent to the Tabular server, could you declare a DAX calculation in a similar manner?

Parent-Child Dimensions in Analysis Services – Performance Walkthrough

Parent-child hierarchies are a good fit for many data structures such as accounts or employees, and while they can speed development in some cases, they can also cause performance problems in large cubes.

We often see customers with these type of performance issues, and thought it worth sharing a simple technique for altering the dimension structure to improve query speed.

The problem

Often parent-child hierarchies are created as this is the structure used in the relational source, so they seem a good fit to model the members. In many cases though data is only at the leaf level of the hierachy, meaning parent-child isn’t really needed.

Performance problems occur because no aggregates are created for parent-child dimensions, as detailed in the Analysis Services performance guide:

Parent-child hierarchies

Parent-child hierarchies are hierarchies with a variable number of levels, as determined by a recursive relationship between a child attribute and a parent attribute. Parent-child hierarchies are typically used to represent a financial chart of accounts or an organizational chart. In parent-child hierarchies, aggregations are created only for the key attribute and the top attribute, i.e., the All attribute unless it is disabled. As such, refrain from using parent-child hierarchies that contain large numbers of members at intermediate levels of the hierarchy. Additionally, you should limit the number of parent-child hierarchies in your cube.

If you are in a design scenario with a large parent-child hierarchy (greater than 250,000 members), you may want to consider altering the source schema to re-organize part or all of the hierarchy into a user hierarchy with a fixed number of levels. Once the data has been reorganized into the user hierarchy, you can use the Hide Member If property of each level to hide the redundant or missing members.

 

The performance guide hints at re-organizing the hierarchy to improve perfomance, but doesn’t say how.

The solution

This article will walkthrough the steps needed to change your parent-child hierarchy structure to have real levels, so that aggregations work, and your performance is as good as you expect with normal hierarchies.

This process is known as flattening or normalizing the parent-child hierarchy.

Firstly, let’s look at the data in our relational source.

Code: Sql Create ScriptSelectShow

Not a large dimension, but enough to demonstrate the technique. As you can see my real products are all at the leaf level.

The strategy is quite simple:

  • Create a view to seperate the members into different levels.
  • Create a new dimension using these real levels.
  • Configure the dimension to appear like the original parent-child dimension, but with the performance of a normal dimension.

Create the view

We want to create a denormalised view of the data. To do this we join the Product to itself once for each level. This does mean we need to know the maximum depth of the hierarchy, but often this is fixed, and we’ll build in some extra levels for safety.

The tricks here are:

  • Use coalesce() so that we always get the lowest level ID below the leaves, never a NULL. This allows us to join to the fact table at the bottom level of our hierarchy.
  • Leave Name columns null below the leaves, this will allow us to stop the hierarchy at the correct leaf level in each part of the hierarchy.

Code: Sql View ScriptSelectShow

Running this we get:

Obviously we can update this view to create more levels as required, but 5 are enough for now.

The Dimension

Next we go to BIDS, and add the view to our Data Source View, and then add a new Dimension based on the view.

The key steps to creating the dimension correctly are:

  • Set the key attribute to Level5ID, and the name to Level5Name.
  • Create an attribute for each Level ID, and on each set the Name Column appropriately.
  • Create a hierarchy using these attributes in order.
  • On each attribute set AttributeHierarchyVisible to False.
  • On each level of the hierarchy set HideMemberIf to NoName.
  • Set up the Attribute Relationships between the levels.

You should end up with the following:

Dimension Structure

 

Attribute Relationships

 

If you browse the dimension you’ll see that it never goes as far as level 5, even though it exists. This is becuase we set up the member hiding option, and returned NULLs in our view.

Conclusion

And that’s it done, you can now join to your fact tables at the lowest level, build your cube as normal and get the performance benefits of aggregation!

See also

A tool to achieve the same result is available from Codeplex, we’ve not personally tried it but may well be a timesaver. This works in a similar way to the example above, but it’s often useful to understand how something works, even if you choose to automate it.

PowerPivot, SQL R2, Sharepoint 2010, Office 2010.

So we’ve been using PowerPivot for a while now, and Office 2010 has been part of our lives for some time. I’ll use this blog to answer some of the questions that keep cropping up in conversation with our customers:

1. Does XLCubed work with Excel (Office 14) 2010?

a. Yes, we’ve been using it since the first CTP release and each release since then.

2. Can I use XLCubed Web with SharePoint 2010?

a. Yes, publishing to the web and embedding the reports within your SharePoint site works in exactly the same way as with previous versions.

3. Does XLCubed connect to PowerPivot?

a. Yes, XLCubed connects to the PowerPivot published cubes, and our client tools can be used to build reports and dashboards from them.

4. Can I build reports from SQL Server R2 using XLCubed?

a. Yes this will work just fine, just as you can build reports from previous version of SQL or other relational sources. (here is an example)

PowerPivot in the real world

The services team have been working on migrating some of our internal models and sample databases across to a PowerPivot environment – looking at the pros and cons, using DAX rather than MDX to perform some calculations. Results have been varied, its been interesting to see some features that we’ve had for a while (like cube formulas, slicers and web parameters) appear in a similar way in PowerPivot.

Quite clearly PowerPivot isn’t the be all and end all or anything like a replacement for Analysis Services, but it certainly has a role for tactical solutions, some power user analysis, and we think likely also for RAD prototypes of larger scale AS implementations. It doesn’t venture into the gap left by PerformancePoint Planning (as many thought it would in early 2009) – we’ve moved to address this area with the XLCubed PM suite that uses in memory OLAP cubes and/or Analysis Services.

Trying out some of the tools

Here’s a few download sets for you to try, take careful note of the hardware spec and requirements for the MS ones though:

The 2010 Information Worker Virtual machine

Register and Download Office 2010

PowerPivot 32Bit, 64Bit

XLCubed Evaluation

If you would like to evaluate against your own data – contact the XLCubed Product team for evaluation editions or if you want to try a no risk proof of concept or prototype contact the XLCubed consulting team.