Self-service Business Intelligence Tools: Unleashing The Prospective Of Computer-based Analytics – Exporting to Excel is more likely to mess up than enable users in any sustainable self-service way
With Power BI self-service, there are many options. Which of these is right for us? It is difficult to decide on a self-service model that works for our organisations. There is no one-size-fits-all solution; we can’t release all “Analyze-in-Excel” flawlessly, and we can’t necessarily expect all of our users to be able to create their own Power BI datasets. At the same time, we should not shut down self-service altogether. We should consider the different tools and approaches – it is important to consider which users will have access to what, how they will use it and why. If we neglect this, users may not have the right tools to answer the right questions. To make this easier, it helps to think about this challenge by examining each “level” and the dimensions below it.
Self-service Business Intelligence Tools: Unleashing The Prospective Of Computer-based Analytics
By picturing the self-service toolset in this way, it can be easier to reflect on your self-service strategy – how certain tools and users will be supported to solve business problems, and how they will be managed.
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Many different tools and approaches are used to address business problems with data in Power BI. They are located along an axis that increases the flexibility the tools provide, as well as the data skills users need to be effective with them, and the effort to create and maintain the answers.
It can be helpful to think of each option as “levels” grouped in “tiers”, increasing in complexity from (1) Using Published Reports to (8) Creating and Distributing Dataflows and Datamarts. Each of these levels is made of different base tools with their own considerations, use cases and governance / operational needs:
These are limited end user experiences where no additional tools are required. Users use the functionality and design in published reports to explore the data at their own pace. They are limited by the design of the report and data model, but they don’t need to learn new software or tools to answer their questions. There is little risk of users creating irresponsible queries as they cannot create new reporting items; they just use what’s already there. Maintenance is therefore easier, as it is centralized in the reports they use.
Image personalization is particularly valuable, although in my experience, it is rarely used in practice. With good model views and some flexible views, users can get a full mock report creation experience without ever creating new reports or using the Power BI desktop.
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Here Excel or Power BI Desktop users connect to Power BI datasets, maintaining data security and a single source of truth while enabling users to explore data and create their own reports. They leverage enterprise datasets created either by IT, the Center of Excellence or Champions within the end user community who have more knowledge of data modeling and DAX. This frees up central teams to focus on enterprise reporting usage scenarios rather than dedicating capacity to do so
Note that the Composite Models over Power BI Datsets & AAS is a preview feature that is still under development. For more information, see this link.
REFORM A COMMUNITY OF PRACTICE To successfully implement managed self-service, BI teams need to foster a culture of information sharing among users and developers alike. This Community of Practice is a critical concept introduced in the Power BI Adoption Roadmap written by Melissa Coates and Matthew Roche et al. Doing this means creating a socially shared space and culture that encourages learning, compliance and data literacy, with the goal of producing an independent and capable user community.
It’s important to leverage the approval features in the Power BI service to clearly certify what these single-truth datasets are, so they can be labeled as ready to use. There should also be policies in place to promote quality reports while downgrading promoted reports that do not fit the bill.
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Self-service users can analyze the data themselves, creating and distributing their own reports. As such, it is recommended at this level to use Data Loss Control policies and Sensitivity Labels. Having Mandatory and Inherited Sensitivity Labels will mitigate the risk of Data Loss due to unsanctioned data distribution or report export.
CATALOGING THE DATA SET AND TRAINING USERS TO USE IT Data sets created for use need to be done in a business friendly way. It is clear that fields should be named and sorted into folders, while technical fields should be hidden. Ideally, there should also be a catalog of what is in the dataset and how it is made. Data cataloging and genealogy tools like Purview certainly help, here. Despite any organizing and cataloging efforts, however, users will still need training to know which measures and areas to use.
Administering this ecosystem requires a monitoring solution to monitor user activities and the quantity and quality of assets being published and shared. Creating such a solution means relying on out-of-the-box admin solutions like the Premium Metrics app and Admin Portal Usage Metrics (which is a bit limited, and that treemap is brutal), as well with creating custom solutions with the Power BI REST APIs and Activity Logs.
This level is complex, as it involves users loading data into Power BI and creating their own data model, measures and logic, then sharing and reporting from these datasets. Such datasets can be small – from a single Excel file – or they can connect to analysis layers and flat files at the same time to combine large amounts of data. The maintenance work here is therefore very high, as it involves a lot of effort to train users and manage the environment they distribute. Users have varying levels of knowledge about Power BI depending on their needs. So it is difficult to manage this scenario as no one-size-fits-all approach will work.
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Each user (group) will follow their own learning path based on their needs and skills. Being aware of this is important to govern and monitor this situation. Mapping this is difficult, however, and could be discussed in a subsequent post looking at the other dimensions under the tools themselves:
Mapping users along a learning path against their needs is important for adoption and governance, and overall ensuring that users are enabled to successfully use Power BI to answer business data questions.
The last level is the most complex as it is one layer earlier; this is the centralized creation of ETL solutions for data to be used by multiple datasets. They are the highest effort to manage and maintain, but they provide the highest flexibility and agility if used correctly by the right users for the right use case. These instances are generally used to support larger scale self-service operations, either in terms of usage or data volume/complexity.
Note that Datamarts is a Premium feature currently in preview. For more information, see this article.
Business Intelligence Applications
Using Datamarts or Dataflows in self-service requires users to think about solution architecture – considering multiple layers rather than doing everything inside one .pbix (dataset + report).
It is possible for users to create datamarts or dataflows to feed single datasets, although more commonly these are reused among multiple datasets to maintain central transformation logic. In general, this layer has the same considerations as Layer C, although it forces users to work in a “multi-layer” deployment scenario they have to think in a broader platform/solution way. This is different from Power BI Datasets, where a user with a simple scenario could connect to an Excel file and create a report in the same file, being naive to the concept of different data layers.
There is no one solution that will suit every use case; it will vary across individuals, teams, departments, and organisations. To decide who should use what and why, it helps to break it down into layers and levels; segmentation. This will help you to…
In part 2, we visualize other dimensions of self-service, looking at learning pathways. Click on the image below to read that article.
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