Self-service Business Intelligence Tools: Unleashing The Prospective Of Computer-based Analytics

Posted on

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.

Deploying Microsoft Power Bi For Digital Transformation

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.

Future Proofing Online Communities With Artificial Intelligence: 6 Key Factors To Consider

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.

Emerging Business Intelligence Tools

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.

Best Practices For Data Preparation Software In 2022

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.

A Powerful Embedded Business Intelligence Tool

Jun 21 Power BI External Tools Impact Factor Jun 7 The value Power BI could bring to Academic Research We are a boutique consultancy with deep expertise in Azure, Data & Analytics, Azure Synapse Analytics, Power BI, and high performance .NET Development. Based in the UK with a global customer base.

We specialize in modernizing data and analytics platforms, and .NET Applications. We help small teams achieve big things.

Whether it’s a global brand, or an ambitious scale, we help the small teams that power them, to achieve more.

We love to share what we’ve learned the hard way, through blogs, talks or thought leadership. This is the good stuff!

Top 50 Business Intelligence Statistics And Trends For 2022

If you would like to ask us a question, talk about your requirements, or arrange a chat, we would love to hear from you. Want to know more about how it could help you?

Copilot in Power BI and Microsoft Fabric is impressive. Of course, it’s still very early days and your mileage may vary between the polished marketing demos and your real world experience. But, history has shown us that this is only going to get better – check out GitHub Copilot – over time, with use, training and incremental improvements it will turn into something really useful

Self-service business intelligence, business intelligence analytics software, business intelligence analytics tools, self service data analytics tools, cloud based analytics business intelligence, self service business intelligence tools, business intelligence & analytics software, self service analytics tools, cloud based business intelligence, cloud based business intelligence tools, web based business intelligence tools, unleashing the power of emotional intelligence

Leave a Reply

Your email address will not be published. Required fields are marked *