Power BI Governance, What Organisations Need to Know

Power BI Governance Art Built by Bing Image Creator

In recent years, Power BI has become one of the most widely used business intelligence (BI) tools. Power BI is more than just a reporting tool; it is a comprehensive analytical platform that enables users to collaborate on data insights and share them internally and externally. In addition to creating reports and dashboards, Power BI allows users to collaborate and share their work with others. For instance, users can share dashboards with their colleagues, allowing them to view, interact, and engage with the data quickly. However, as more organisations adopt Power BI, it becomes essential to ensure appropriate governance processes, policies, and rules are in place. This blog post explains Power BI governance and why business owners need to be conscious of it.

Power BI governance refers to a set of processes, policies, and standards that organisations put in place to manage and control the use of Power BI. Governance is critical to ensure that the use of Power BI is aligned with the organisation’s objectives and strategy, complies with relevant regulations and standards, and protects sensitive data. Power BI governance encompasses several areas, including security, data management, compliance, and user management. It also involves defining data access, sharing, security, and compliance guidelines within Power BI. This includes defining roles and permissions for users, specifying approved data sources that can be used, and ensuring that the data is accurate, up-to-date, and secure across the organisation. In addition, Power BI governance involves monitoring and auditing the use of Power BI to ensure that it is being used appropriately and in compliance with the organisation’s policies. Lack of Power BI governance can impact businesses in various negative ways, so it is important that everyone within the organisation, especially the managerial teams, has a good understanding of how they can benefit from supporting the establishment of Power BI governance across the organisation. Here are some reasons:

  • Better decision-making
    With Power BI governance in place, organisations can ensure that the data used in decision-making is accurate, consistent, and trustworthy. This can help them make informed decisions based on reliable data insights.
  • Improved security and compliance
    Power BI governance helps to establish security measures to protect sensitive data and ensure compliance with regulations and industry standards. This helps to avoid costly data breaches and non-compliance penalties.
  • Efficient use of resources
    By establishing guidelines for roles and responsibilities, data access, sharing, and storage, Power BI governance can help organisations use their resources more efficiently. This can result in cost savings and improved productivity.
  • Enhanced collaboration
    Having Power BI governance policies help business owners to promote collaboration and communication among team members. This can result in improved teamwork and better outcomes for the organisation.
  • Better management and control
    Power BI governance helps organisations to manage and control the use of Power BI within the organisation. This can help them ensure that the tool is being used effectively and efficiently and that data is being used in a way that aligns with their business objectives.
Continue reading “Power BI Governance, What Organisations Need to Know”

Slowly Changing Dimension (SCD) in Power BI, Part 1, Introduction to SCD

Slowly changing dimension (SCD) is a data warehousing concept coined by the amazing Ralph Kimball. The SCD concept deals with moving a specific set of data from one state to another. Imagine a human resources (HR) system having an Employee table. As the following image shows, Stephen Jiang is a Sales Manager having ten sales representatives in his team:

SCD in Power BI, Stephen Jiang is the sales manager of a team of 10 sales representatives
Image 1: Stephen Jiang is the sales manager of a team of 10 sales representatives

Today, Stephen Jiang got his promotion to the Vice President of Sales role, so his team has grown in size from 10 to 17. Stephen is the same person, but his role is now changed, as shown in the following image:

SCD in Power BI, Stephen's team after he was promoted to Vice President of Sales
Image 2: Stephen’s team after he was promoted to Vice President of Sales

Another example is when a customer’s address changes in a sales system. Again, the customer is the same, but their address is now different. From a data warehousing standpoint, we have different options to deal with the data depending on the business requirements, leading us to different types of SDCs. It is crucial to note that the data changes in the transactional source systems (in our examples, the HR system or a sales system). We move and transform the data from the transactional systems via ETL (Extract, Transform, and Load) processes and land it in a data warehouse, where the SCD concept kicks in. SCD is about how changes in the source systems reflect the data in the data warehouse. These kinds of changes in the source system do not happen very often hence the term slowly changing. Many SCD types have been developed over the years, which is out of the scope of this post, but for your reference, we cover the first three types as follows.

SCD type zero (SCD 0)

With this type of SCD, we ignore all changes in a dimension. So, when a person’s residential address changes in the source system (an HR system, in our example), we do not change the landing dimension in our data warehouse. In other words, we ignore the changes within the data source. SCD 0 is also referred to as fixed dimensions.

Continue reading “Slowly Changing Dimension (SCD) in Power BI, Part 1, Introduction to SCD”

Business Intelligence Components and How They Relate to Power BI

Business Intelligence Components and How They Relate to Power BI

When I decided to write this blog post, I thought it would be a good idea to learn a bit about the history of Business Intelligence. I searched on the internet, and I found this page on Wikipedia. The term Business Intelligence as we know it today was coined by an IBM computer science researcher, Hans Peter Luhn, in 1958, who wrote a paper in the IBM Systems journal titled A Business Intelligence System as a specific process in data science. In the Objectives and principles section of his paper, Luhn defines the business as “a collection of activities carried on for whatever purpose, be it science, technology, commerce, industry, law, government, defense, et cetera.” and an intelligence system as “the communication facility serving the conduct of a business (in the broad sense)”. Then he refers to Webster’s dictionary’s definition of the word Intelligence as the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal”.

It is fascinating to see how a fantastic idea in the past sets a concrete future that can help us have a better life. Isn’t it precisely what we do in our daily BI processes as Luhn described of a Business Intelligence System for the first time? How cool is that?

When we talk about the term BI today, we refer to a specific and scientific set of processes of transforming the raw data into valuable and understandable information for various business sectors (such as sales, inventory, law, etc…). These processes will help businesses to make data-driven decisions based on the existing hidden facts in the data.

Like everything else, the BI processes improved a lot during its life. I will try to make some sensible links between today’s BI Components and Power BI in this post.

Generic Components of Business Intelligence Solutions

Generally speaking, a BI solution contains various components and tools that may vary in different solutions depending on the business requirements, data culture and the organisation’s maturity in analytics. But the processes are very similar to the following:

  • We usually have multiple source systems with different technologies containing the raw data, such as SQL Server, Excel, JSON, Parquet files etc…
  • We integrate the raw data into a central repository to reduce the risk of making any interruptions to the source systems by constantly connecting to them. We usually load the data from the data sources into the central repository.
  • We transform the data to optimise it for reporting and analytical purposes, and we load it into another storage. We aim to keep the historical data in this storage.
  • We pre-aggregate the data into certain levels based on the business requirements and load the data into another storage. We usually do not keep the whole historical data in this storage; instead, we only keep the data required to be analysed or reported.
  • We create reports and dashboards to turn the data into useful information

With the above processes in mind, a BI solution consists of the following components:

  • Data Sources
  • Staging
  • Data Warehouse/Data Mart(s)
  • Extract, Transform and Load (ETL)
  • Semantic Layer
  • Data Visualisation

Data Sources

One of the main goals of running a BI project is to enable organisations to make data-driven decisions. An organisation might have multiple departments using various tools to collect the relevant data every day, such as sales, inventory, marketing, finance, health and safety etc.

The data generated by the business tools are stored somewhere using different technologies. A sales system might store the data in an Oracle database, while the finance system stores the data in a SQL Server database in the cloud. The finance team also generate some data stored in Excel files.

The data generated by different systems are the source for a BI solution.

Staging

We usually have multiple data sources contributing to the data analysis in real-world scenarios. To be able to analyse all the data sources, we require a mechanism to load the data into a central repository. The main reason for that is the business tools required to constantly store data in the underlying storage. Therefore, frequent connections to the source systems can put our production systems at risk of being unresponsive or performing poorly. The central repository where we store the data from various data sources is called Staging. We usually store the data in the staging with no or minor changes compared to the data in the data sources. Therefore, the quality of the data stored in the staging is usually low and requires cleansing in the subsequent phases of the data journey. In many BI solutions, we use Staging as a temporary environment, so we delete the Staging data regularly after it is successfully transferred to the next stage, the data warehouse or data marts.

If we want to indicate the data quality with colours, it is fair to say the data quality in staging is Bronze.

Data Warehouse/Data Mart(s)

As mentioned before, the data in the staging is not in its best shape and format. Multiple data sources disparately generate the data. So, analysing the data and creating reports on top of the data in staging would be challenging, time-consuming and expensive. So we require to find out the links between the data sources, cleanse, reshape and transform the data and make it more optimised for data analysis and reporting activities. We store the current and historical data in a data warehouse. So it is pretty normal to have hundreds of millions or even billions of rows of data over a long period. Depending on the overall architecture, the data warehouse might contain encapsulated business-specific data in a data mart or a collection of data marts. In data warehousing, we use different modelling approaches such as Star Schema. As mentioned earlier, one of the primary purposes of having a data warehouse is to keep the history of the data. This is a massive benefit of having a data warehouse, but this strength comes with a cost. As the volume of the data in the data warehouse grows, it makes it more expensive to analyse the data. The data quality in the data warehouse or data marts is Silver.

Extract, Transfrom and Load (ETL)

In the previous sections, we mentioned that we integrate the data from the data sources in the staging area, then we cleanse, reshape and transform the data and load it into a data warehouse. To do so, we follow a process called Extract, Transform and Load or, in short, ETL. As you can imagine, the ETL processes are usually pretty complex and expensive, but they are an essential part of every BI solution.

Continue reading “Business Intelligence Components and How They Relate to Power BI”

Exporting Power BI Desktop Data Using Power BI Exporter

Exporting Power BI Desktop Data Using Power BI Exporter
Exporting Power BI Desktop Data Using Power BI Exporter

I am excited to announce that our amazing team at Data Vizioner has developed a lightweight and easy to use external tool for Power BI Desktop named Power BI Exporter. The Power BI Exporter is a free community tool available for download here. With External Tools General Availability, you can expect to see more and more External Tools built by our amazing community members. I personally stunned to see many amazing ideas turn into useful tools. Learn more about Power BI announcements about External Tools GA here. I am humbled to be named in the announcement though. So thank you to Microsoft for that.

Let me start with a little background. In the past few years, I wrote a series of blog posts about exporting data from Power BI Desktop and Power BI Service. Those posts are quite popular. They are in the top 10 most visited posts on my website, contributing to about 15% of my website visitors. Here are the previous posts I wrote on this specific topic:

Apart from my website statistics showing many people want to export data from Power BI Desktop, some of my customers asked the same question. They wanted to export the curated data from their data model within Power BI Desktop to CSV format and make the curated data available for their other platforms. While all the methods mentioned in my previous blog posts are working, some users still find them complex. So I thought, we can make it better. We can make a straightforward tool that exports the data with only two clicks. So we started building the Power BI Exporter as a micro-project. We added some more ideas to the original idea of only exporting the data. We thought it is good to export the data along with the table names, column names and relationships. Having that information handy, we can quickly build the same data model as the one we exported its data but using the CSV files as the data sources. The other idea was to pack everything in a ZIP file on the fly, so we have a single ZIP file, including the tables, columns, and relationships. As a result, the first version of the Power BI Exporter is born. In this post I explain how it works.

Downloading and Installing Power BI Exported

You can download Power BI Exporter from its official webpage from Data Vizioner website. You require to enter your email address then click the Download button as shown in the following image:

Downloading Power BI Exporter from Data Vizioner Website
Downloading Power BI Exporter from Data Vizioner Website
Continue reading “Exporting Power BI Desktop Data Using Power BI Exporter”