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Azure DevOps Power BI Reporting

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Azure DevOps (ADO) is fast becoming the application lifecycle management tool of choice for modern organisations. With boards, CI/CD pipelines and Git repo capabilities, Agile practices have never been so easy to implement in project management. However, as a DevOps tool, it is understandably not designed and equipped to be a fully-fledged reporting and analytics solution as well. Luckily, Power BI can be used to integrate with ADO to deliver the kind of enterprise reporting that project managers need to properly monitor their projects. This blog post covers Azure DevOps Power BI reporting and also some examples of what kind of reporting is available.

Connecting to ADO

A connection to ADO is made possible via the OData feed option available in Power BI. Once connected, you will need to select the relevant tables to begin building a data model. For most project managers, the main objective in ADO reporting is getting clear visibility of the progress of work items. For that reason, the tables imported into the model should contain information relating to Work Items and Iterations.

Once imported, some simple transformations are required to clean the data. It is crucial that the correct relationships are created between work item tables. This is because work items in ADO are hierarchical, starting with Epics, Features, User Stories and Tasks. This hierarchical logic must be captured in the model in order for the reporting to make sense.

Once the model has been created, the report visualisations can be built. Based on experience, a clearly constructed table outlining key work item information including Sprint, Epic, Feature, User Story, Task, Assigned To, Completed Date, Task Number and Sprint Percentage is precisely what project managers want to see. Although not the most visually compelling report, these tables  clearly articulate work progress in a single view, something not easily achieved natively within ADO.

What Reporting Is Available

As mentioned previously, ADO reporting is primarily concerned with reporting on the progress of work items. However, the OData feed is able to capture most of the ADO backend, meaning that additional reporting on things such as pipelines and test results is also possible. Some typical reporting examples include:

  • Sprint progress reporting
  • Resource burndown and capacity
  • Work item cycle time
  • Work item predictability and productivity
  • Task completion forecasting
  • Work item distribution
  • CI/CD pipeline failures
  • Application testing and release results

Virtually any reporting can be custom built using Power BI and the OData feed.

Developing Power BI reporting for ADO is also useful because of its scalability. The OData feed can be re-pointed to any ADO instance, meaning that your reporting can be easily reproduced in other ADO instances. At FTS, we offer a pre-packaged report that can be easily implemented in any instance. It contains the most relevant reporting out of ADO based on our experience and has been very useful for us in managing our projects.

Finally, Power BI reports can be easily embedded back into ADO via the native web-embed functionality. A dashboard in DevOps must be first created, and include an iframe dashboard widget. Then the Power BI report can be embedded into the widget in the dashboard, thereby allowing you to view your custom reporting within the DevOps browser. This ability to embed reporting elevates ADO into being an all-in-one development management tool, greatly assisting project managers keep track of resources and progress.

If you want to begin Azure DevOps Power BI reporting, please contact us for more information.

Power BI Dataflows: New and Improved

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If you attended one of our Dashboard In A Day events earlier this year, you would have seen a brief demonstration of Power BI Dataflows, and what they can mean for an organisation. With the recent Microsoft update of Dataflows, now is a good time to familiarise yourself with this feature and learn how you can leverage it to improve the data culture in your organisation.

What Are Dataflows

If you have worked with Power BI before, then you are familiar with Power Query, which is the tool used for extracting, transforming, and loading data into a data model. Power Query allows you to connect to a variety of data sources and perform detailed transformations to manipulate data into the desired format needed to perform analysis.

Dataflows is an extension of this, in that it allows you to create these Power Query transformations and make them available across your organisations for repeatable use. This is important for two reasons:

  1. It scales data preparation, and eliminates the need for users to perform transformations again and again.
  2. It introduces a layer of governance in centralising and standardising data preparation assets.

Dataflows gives users access to clean, transformed data that they can rely on and re-use. This is vital in supporting self-service analytics in an organisation, as it provides users with the platform needed to access reliable and pre-configured data assets.

New Capabilities

Power BI has now introduced Endorsement capabilities into the Dataflows feature. Dataset endorsement capabilities have already been in use for some time and have proven very useful in establishing quality data culture in an organisation. With this capability now extended into Dataflows, quality data assets can now be more easily identified and shared across an organisation. Per the Endorsement principles, Dataflows can be marked for Promotion or Certification.

Promotion – tells users that the dataflow owner believes that this dataflow is good enough to be shared and reused. Users will need to have confidence in the dataflow owner to trust the quality of the dataflow.

Certification – tells users the dataflow has passed internal tests for quality per organisational policy. Only specified users are authorised to mark Dataflows as Certified.

Certified and Promoted Dataflows are marked with badges when users attempt to connect to them in Power BI Desktop:

This identification means that users can easily see which dataflows they should use to connect to when preparing reporting or analysis.

Why It’s Important

Endorsement is an important step in making Dataflows an enterprise-ready feature. With endorsement, an organisation must adopt a policy for reviewing and certifying data preparation assets. The introduction of this policy greatly improves the quality of data in an organisation, as only certified dataflows are being used for reporting and analytics outcomes.

Organisations that wish to promote a self-service environment will also benefit greatly from endorsed dataflows, as it reduces the need for dedicated resources to create and control data access. Instead, quality data assets can be centrally managed via Power BI and made available to the organisation to connect to and use. Users can rely on the quality of data, and do not need to perform any additional tasks to cleanse the data to get it ready for their analysis.

If you want to know how Dataflows can be used in your organisation, please contact us for more information.

SSAS Tabular Optimisation In 5 Easy Steps

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SSAS Tabular Optimisation

A well-designed SSAS tabular model is often the key ingredient in a successful analytics solution. It aids the business in delivering the type of ad-hoc analysis and on-the-fly insights that drives performance improvement and economic benefit.  However, not all models are well-designed. Indeed, a poor performing cube can quickly become a burden for any organisation, negatively impacting quality of analysis and becoming a drain on valuable business resources. That’s why SSAS Tabular optimisation is so crucial for businesses wanting to get the most value out of their analytics solution.

Recently, I consulted for a large electrical merchandising business who were having some trouble with their SSAS tabular models. With national operations, it was imperative that their cubes could rapidly and reliably deliver the analysis needed for the business to confidently make strategic decisions around sales, purchasing and inventory. Memory issues and ambiguous design principles were proving to be a challenge in getting the tabular model to behave, and it was clear that I needed to tune the existing cubes with some simple optimisation techniques.

When attempting SSAS Tabular optimisation, I employ a straight-forward 5-step strategy:

  1. Examine the model and remove unnecessary tables
  2. Examine the tables, remove unnecessary columns and edit table structure/content
  3. Examine the columns and change data types
  4. Examine the DAX measures and edit expressions
  5. Examine server properties and edit memory settings

This 5-step performance tuning approach guarantees that tabular model issues can be precisely identified and appropriately addressed.

1.      Examine the Model

A concise tabular model is one that performs best. Therefore, the first step is to review the model itself. Very often a poor-performing cube contains unnecessary tables or relationships that provide no real value. A thorough review of what tables are present in the model and what value they bring will uncover what is necessary and what is redundant. Talking to stakeholders about what they need will also help determine what tables should go and what needs to stay. In my example, I was able to reduce the cube size by removing unnecessary dimension tables that I discovered the business was no longer interested in. This redesign process typically yields ‘quick-and-easy’ wins in terms of cube performance, as it is the easiest to implement.

Figure 1. Removing unnecessary tables reduces SSAS tabular model complexity

 

2.      Examine the Tables

What data actually goes into the tables will ultimately determine the quality of the tabular model. Similar to the first step, a review of the tables will often uncover unnecessary columns that do not need to be loaded into the model. For example, columns that are never filtered on or contain largely null values. Table structure is also important to tabular model performance, as it can affect how much data needs to be loaded. For example, you could reduce the row count of the sales fact table by aggregating it to be at the invoice level, instead of invoice line level. Such a reduction in size will mean that less memory is required by the cube.

Figure 2. Tidy up tables by removing columns, and reducing rows

 

3.      Examine the columns

A crucial aspect of cube performance is compression. Columns with certain data types, or have unique values for all rows will compress badly, and will require more memory. An effective optimisation technique is to correct the data type or value in a column, such that it is able to compress better. Casting values as integers instead of strings or defining decimal points are fundamental practices that are often overlooked in tabular model design, and ultimately come at the expense of performance. In my example, I was able to create a new unique invoice ID that could be used by the business and compressed as an integer. Previously the varchar invoice key was unique at almost every row of the sales table, and was compressing very poorly. The storage engine (Vertipaq) wants to compress columns, and having similar values in the same column greatly aids this. A great tool for this kind of analysis is the Vertipaq Analyzer. This tool can highlight potential areas of interest in compression activities and help track results in terms of cube optimisation techniques.

Figure 3. The VertiPaq Analyzer reveals compression pain points

 

4.      Examine the DAX

For cube users, it is critical that the OLAP queries they run return accurate results rapidly. If a user cannot get the information they need from a model in a reliable or timely manner, the cube is failing to provide the benefits expected of it. Therefore, an important part of tabular model optimisation revolves around the measures, and ensuring that the DAX expressions used are performance optimised for the formula engine. Keeping the measures simple by using basic expressions, and removing complicated filtering clauses means that the measures should perform better. In my example, I was able to change some of the expressions of sales measures at different period intervals (such as month-to-date and year-to-date), such that they could run across different filtering contexts, thus reducing calculation time.

Figure 4. Simple DAX equals better performance

 

5.      Examine the Server

Finally, the biggest factor in tabular model processing performance is the actual memory properties. Depending on the edition of the Analysis Services, there are various levels of memory limits. For the Standard Edition, the 16Gb limit imposed on a single instance of Analysis Services can often be the ‘killer of cubes’. If a reasonable business case exists, then moving to the Enterprise Edition or cloud-based solution can be the right answer to memory woes. However, there are steps that can be taken to get the best out of a SSAS tabular model without abandoning Standard Edition altogether. Increasing the amount of RAM on the server and modifying the server instance memory properties allows you to fine tune processing and reduce the likelihood of memory exception errors. In my example, the cube was failing to process as it would run out of memory during a Full Process. I increased the RAM from 32Gb to 40Gb, and reduced the Total Memory Limits in the server instance properties. With more memory and lower thresholds to which memory cleaner processes were initiated, the cube was able to process in full each time without error.

Figure 5. Fine tune the memory limits to find the optimal level of performance

 

Summary

Like any business asset, a SSAS tabular model loses value when it is not properly configured or utilised. However, with the proper approach methodology, any model can be transformed from an underperforming asset into a valuable resource for a business.

 

If you’re having trouble with SSAS tabular optimisation, we want to hear about it! Please contact us to find out about how we can help you optimise your cubes.

Data & AI Strategy metrics

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Why are Data & AI strategy metrics important? The beauty of “strategies” for some is that a strategy – unlike a tactic – often doesn’t come with any clear success / fail KPI’s. It allows a lot of wriggle room for ambiguous assessments of whether it worked or not. However any self-respecting Data & AI strategy should not allow this. After all, it is designed and executed in the name of improving the use of data and measurable outcomes within an organisation. A good Data & AI strategy should have measures to determine its success.

Data & AI Strategy metrics that matter

Commonly raised metrics are based around uptake and usage (software vendors are particularly fond of these). This seems based on the hope that the apparent usage of tools is inherently a good thing for a company that will somehow lead to – I don’t know – increased synergy?

Dilbert Utilising Synergy

Dilbert Utilising Synergy

Sometimes they are measured around data coverage by the EDW or project completion.  However, if I was to put my CEO hat on, I would want to know the answer to the question “how are all these Data & AI users improving my bottom line?”. After all, if the Data & AI tools are being heavily used, but only to manage the footy tipping competition, then I’m not seeing a great deal of ROI.

The metrics that matter are the Corporate metrics.

A good Data & AI Strategy should be implemented with a core goal of supporting the Corporate strategy, which will have some quantifiable metrics to align to. If not, a good Data & AI strategy isn’t going to help you much as your organisation has other problems to solve first!

In a simple case, imagine a key part of the strategy is to expand into a new region. The Data & AI strategy needs to support that by providing data & tools that supports that goal, enabling the team in the new region to expand – and should be measured against its ability to support the success of the Corporate strategy.

This is why at FTS Data & AI, our first step in defining a Data & AI Strategy for an organisation is to understand the Corporate strategy – and its associated metrics – so we can align your Data & AI strategy to it and create a business case to justify why you need to embark on a Data & AI strategy in the first place. The metrics are the foundation that prove that there is deliverable value to the business. This is why the Corporate Strategy sits at the top of our Strategy Framework:

Data & AI Strategy Framework

Data & AI Strategy Framework

We have extensive experience designing strategies that support your business. Contact us today to speak with one of our experts.

Agile Zero Sprint for Data & AI projects

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Agile methodologies have a patchy track record in Data & AI projects. A lot of this is to do with adopting the methodologies themselves – there are a heap of obstacles in the way that are cultural, process and ability based. I was discussing agile adoption with a client who readily admitted that their last attempt had failed completely. The conversation turned to the concept of the Agile Zero Sprint and he admitted part of the reasons for failure was that they had allowed Zero time for their Agile Zero Sprint.

What is an Agile Zero Sprint?

The reality of any technical project is that there are always certain fundamental decisions and planning processes that need to be gone through before any meaningful work can be done. Data Warehouses are particularly vulnerable to this – you need servers, an agreed design approach, a set of ETL standards – before any valuable work can be done – or at least without incurring so much technical debt that your project gets sunk after the first iteration cleaning up after itself.

So the Agile Zero Sprint is all that groundwork that needs to be done before you get started. It feels “un”-agile as you can easily spend a couple of months producing nothing of any apparent direct value to the business/customer. The business will of course wonder where the productivity nirvana is – and particularly galling is you need your brightest and best on it to make sure you get a solid foundation put in place so it’s not a particularly cheap phase either. You can take a purist view on the content from the Scrum Alliance or a more pragmatic one from Larissa Moss.

How to structure and sell the Zero sprint

The structure part is actually pretty easy. There’s a set of things you need to establish which will form a fairly stable product backlog. Working out how long they will take isn’t that hard either as experienced team members will be able to tell you how long it takes to do pieces like the conceptual architecture. It just needs to be run like a long sprint.

An Agile Zero Sprint prevents clogged pipes

An Agile Zero Sprint prevents clogged pipes

Selling it as part of an Agile project is a bit harder. We try and make this part of the project structure part of the roadmap we lay out in our Data & AI strategy. Because you end up not delivering any business consumable value you need to be very clear about what you will deliver, when you will deliver it and what value it adds to the project. It starts smelling a lot like Waterfall at this point, so if the business is skeptical that anything has changed, you have to manage their expectations well. Be clear that once the initial hump is passed, the value will flow – but if you don’t do it the value will flow earlier to their expectations, but then quickly after the pipes will clog with technical debt (though you may want to use a different terminology!)