What is Microsoft Fabric?
Microsoft Fabric is the new Microsoft branded analytics platform that integrates data warehousing, data integration and orchestration, data engineering, data science, real-time analytics, and business intelligence into a single product. This new one-stop shop for analytics brings a set of innovative features and capabilities by providing a transformative tool for data professionals and business users. With Microsoft Fabric, we can leverage the power of data and AI to transform organizations and create new opportunities from data.
For more Microsoft Fabric topics 👉 Microsoft Fabric | element61
How vital is Self-Service Business Intelligence?
Self-service BI is crucial for business professionals because it empowers them to access, analyze, and visualize data independently without needing specialized IT skills or support. This democratization of data allows for faster decision-making as business users can directly extract insights and trends from their data without waiting for IT departments to generate reports. It also fosters a data-driven culture within the organization, as everyone can leverage data in their roles. Furthermore, it frees IT resources to focus on more strategic tasks rather than routine report generation. In essence, self-service BI enhances agility, efficiency, and innovation in a business environment.
While self-service BI is an invaluable business asset, it brings many benefits and challenges.
What are the benefits of Self-Service BI?
Self-service BI tools enable business users to access and analyze data independently, reducing reliance on IT or data experts and promoting user empowerment and autonomy. This autonomy can facilitate faster, more informed decision-making. Additionally, the capacity to generate reports and insights as needed allows businesses to adapt more swiftly to evolving circumstances and market trends, underscoring the importance of responsiveness. On the financial front, by lessening the reliance on IT departments for data analysis and report creation, businesses can redirect those resources to other strategic areas, potentially leading to cost savings. Moreover, when more employees have access to and can comprehend data, it promotes a data-centric culture within the organization, leading to more informed decision-making at all levels.
What are the challenges of Self-Service BI?
With multiple users generating reports and insights, there's a risk of having several sources of truth leading to data quality and consistency issues, leading to misinformed decisions. Furthermore, more access points to data can increase the risk of data breaches if not properly managed, highlighting security risks. On the user side, not all business users may be comfortable with data analysis. Without proper training and support, some users may find self-service BI tools overwhelming. Additionally, without adequate oversight, there's a risk that data can be misused or misinterpreted, leading to inaccurate conclusions and potentially harmful business decisions, underscoring the potential for misuse.
How do you kickstart your data exploration in Fabric?
Let's consider a situation where a data analyst is assigned to develop a report to support his management in making data-based decisions.
Before the analyst can create the report, he needs to mix in some data he's been working on with the primary data source. To do this, he swings by the IT department and asks them to blend his data with the primary source. Unfortunately, his request gets lost in the IT department's mountain of work, and he's asked to wait a few months. For a split moment, he considers just dumping all the data into an Excel sheet and making the report right there. However, this approach could lead to all sorts of problems, like messing up the quality and consistency of the data, opening security risks, significant time investment in data updates, etc. So, he decides to try out Fabric.
What is the role of OneLake in Self-Service BI?
Rather than embarking on the laborious process of creating the dataset from scratch, the analyst opts for efficiency and capitalizes on reusability. Leveraging Microsoft Fabric's OneLake data hub, he starts looking through all accessible datasets, simplifying and accelerating data sourcing.
The OneLake data hub facilitates locating, examining, and utilizing Fabric data items a user can access. It provides a list of all accessible data items that can be filtered, a collection of recommended data items, a feature to locate data items by workspace, an option to display only the data items from a chosen domain, and a menu of options for each data item.
As a result, our data analyst has pinpointed a dataset that seems to align with his requirements. This dataset is linked to a Lakehouse working on top of OneLake. Reusing the dataset will minimize the risk of encountering data quality and inconsistency issues.
OneLake is an integrated, logical data lake for the whole organization, "the OneDrive of data". Included as a default feature with every Microsoft Fabric tenant, it's crafted to serve as the centralized repository for all your analytical data. This seeks to dismantle data silos and simplify data management. It also champions a collaborative approach towards your organization's data responsibility, enhancing data sharing efficiency while eradicating data duplication requirements.
Since the data analyst can write SQL statements, he wants to use the Lakehouse SQL endpoint to check out the underlying relational data model by running some queries. This way, he can ensure the dataset has everything he needs for his work.
When you set up a lakehouse, Fabric automatically comes with an SQL endpoint and a default dataset (in Direct Lake mode). Check out this link, If you want to know the difference between direct lake and import mode. This characteristic streamlines the entire data-to-reporting process, enhancing its efficiency and seamlessness.
It's worth mentioning that the Lakehouse, unlike the default warehouse, is a "read-only" place and doesn't fully support the extensive T-SQL capabilities of a transactional data warehouse. The SQL Endpoint can only work with tables in Delta format, so it can't handle other types like Parquet and CSV.
Depending on preferences and skillset, queries can be executed in two ways. First, using the visual query editor, which mainly involves drag-and-drop operations, proves to be a non-intrusive solution.
Second, the SQL query editor where T-SQL codes are written.
How does Fabric support Self-Service ETL?
After looking at the relational data model, the analyst is sure the dataset has about 90% of the data he needs. The only missing element is the ad-hoc data he had previously prepared. He's keen to store his ad-hoc data in OneLake, intending that others could potentially leverage it for their needs. He needs an easy-to-use tool to add this data to the Lakehouse. He decided to use a Dataflow Gen2 since he is already used to working with Power Query.
With Dataflow Gen2, you've got the Power Query experience but on steroids. It can be created in the Data Factory workload, the Power BI workspace, or the lakehouse. Dataflows (Gen2) can extract data from varied sources, refine it using a wide array of transformation operations, and subsequently deposit it into a designated location. A dataflow Gen2 integrates all necessary transformations to reduce data preparation time. The transformed data can be incorporated into a new table or fed into a Data Pipeline.
I'd recommend visiting this link to gain a deeper understanding of creating a lakehouse using drag-and-drop ETL.
Now that all the data is in the lakehouse, the analyst can enrich the dataset and add any missing data. He can also add new measures and make connections between tables from the web browser, so he doesn't have to bother with the Power BI Desktop app. This makes everything a lot easier and more user-friendly. Just one thing to keep in mind, - as of when this was written, you can't use the browser to add calculated columns or Row Level Security.
But our analyst isn't one to take the long way around if he doesn't have to. He decides to try Copilot to help him whip up a few DAX measures and assemble a report's first draft. After a rewarding trial run, he can tweak and polish this draft to fit what's needed before making it available via a Power BI application to the management team.
Does Microsoft Fabric support Self-Service BI?
To sum it up, products like Microsoft Fabric are changing the game in the business world, making it easier for everyone to dig into data and make informed decisions. Not only that, but they promote collaboration and ensure seamless integration and functionality. Our analyst's journey reveals how Fabric's self-service features can simplify the journey from raw data to insightful knowledge, keeping the data quality and consistency high and lessening security worries while offering a user-friendly "low code-no code" platform for all.