Remember that self-service BI enables business users (information workers, like business managers or marketing managers, and power users) to offload effort from IT pros so they have to stay in line waiting for someone to enable BI for them. And, team BI allows the same users to share their reports with other team members without requiring them to
install modeling or reporting tools. Before we go deeper in personal and team BI, let’s take a moment to compare it with organizational BI. This will help you view self-service BI not as a competing technology but as a completing technology to organizational BI. In other words, self-service BI and organizational BI are both necessary for most businesses, and they complement each other.
2.1.1 Understanding Organizational BI
Organizational BI defines a set of technologies and processes for implementing an end-to- end BI solution where the implementation effort is shifted to IT professionals (as opposed to information workers and people who use Power BI Desktop or Excel as part of their job).
Classic organizational BI architecture
The main objective of organizational BI is to provide accurate and trusted analysis and reporting.
Figure 2.1 shows a classic organizational BI solution.
Figure 2.1 Organizational BI typically includes ETL processes, data warehousing, and a semantic layer.
In a typical corporate environment, data is scattered in a variety of data sources, and
consolidating it presents a major challenge. Extraction, transformation, and loading (ETL) processes extract data from the original data sources, clean it, and then load the trusted data in a data warehouse or data mart. The data warehouse organizes data in a set of dimensions and fact tables. When designing the data warehouse, BI pros strive to reduce the number of tables in order to make the schema more intuitive and facilitate reporting processes. For example, an operational database might be highly normalized and have Product, Subcategory, and Category tables. However, when designing a data warehouse, the modeler might decide to have a single Product table that includes columns from the Subcategory and Category tables. So instead of three tables, the data warehouse now has only one table, and end users don’t need to join multiple tables.
While end users could run transactional reports directly from the data warehouse, many organizations also implement a semantic model in the form of one or more Analysis
Services Multidimensional cubes or Tabular models for analytical reporting. As an information worker, you can use Power BI Desktop, Excel, or another tool to connect to the semantic model and then start slicing and dicing data, such as to see how the product sales are doing over time. And IT pros can create operational reports and dashboards from the cube.
Understanding organizational BI challenges
Although it’s well-defined and established, organizational BI might face a few challenges, including the following:
Significant implementation effort – Implementing an organizational BI solution isn’t a simple undertaking. Business users and IT pros must work together to derive
requirements. Most of the implementation effort goes into data logistics processes to clean, verify, and load data. For example, Elena from the IT department is tasked to implement an organizational BI solution. First, she needs to meet with business users to obtain the necessary business knowledge and gather requirements (business requirements might be hard to come by). Then she has to identify where the data resides and how to extract, clean, and transform the data. Next, Elena must implement ETL processes, models, and reports. Quality Assurance must test the solution. And IT pros must configure the hardware and software, as well as deploy and maintain the solution.
Security and large data volumes bring additional challenges.
Highly specialized skillset – Organizational BI requires specialized talent, such as ETL developers, Analysis Services developers, and report developers. System engineers and developers must work together to plan the security, which sometimes might be more complicated than the actual BI solution.
Less flexibility – Organization BI might not be flexible enough to react quickly to new or changing business requirements. For example, Maya from the Marketing department might be tasked to analyze CRM data that isn’t in the data warehouse. Maya might need to wait for a few months before the data is imported and validated.
The good news is that self-service BI can complement organizational BI quite well to address these challenges. Given the above example, while waiting for the pros to enhance
the organization BI solution, Maya can use Power BI to analyze CRM data or Excel files.
She already has the domain knowledge. Moreover, she doesn’t need to know modeling concepts. At the beginning, she might need some guidance from IT, such as how to get access to the data and understand how the data is stored. She also needs to take
responsibility that her analysis is correct and can be trusted. But isn’t self-service BI better than waiting?
2.1.2 Understanding Self-service BI
Self-service BI empowers business users to take analytics in their own hands with guidance from their IT department. For companies that don’t have organizational BI or can’t afford it, self-service BI presents an opportunity for building customized ad hoc solutions to gain data insights outside the capabilities of organizational BI solutions and line-of-business applications. On the other hand, organizations that have invested in
organizational BI might find that self-service BI opens additional options for valuable data exploration and analysis.
REAL WORLD I led a Power Pivot training class for a large company that has invested heavily in organizational BI.
They had a data warehouse and OLAP cubes. Only a subset of data in the data warehouse was loaded in the cubes. Their business analysts were looking for a tool that would let them join and analyze data from the cubes and data warehouse.
In another case, an educational institution had to analyze expense report data that wasn’t stored in a data warehouse.
Such scenarios can benefit greatly from self-service BI.
Self-service BI benefits
When done right, self-service BI offers important benefits. First, it makes BI pervasive and accessible to practically everyone! Anyone can gain insights if they have access to and understand the data. Users can import data from virtually any data source, ranging from flat files to cloud applications. Then they can mash it up and gain insights. Once data is imported, the users can build their own reports. For example, Maya understands Excel but she doesn’t know SQL or relational databases. Fortunately, Power BI doesn’t require any technical skills. Maya could import her Excel file and build instant reports.
Besides democratizing BI, the agility of self-service BI can complement organizational BI well, such as to promote ideation and divergent thinking. For example, as a BI analyst, Martin might want to test a hypothesis that customer feedback on social media, such as Facebook and Twitter, affects the company’s bottom line. Even though such data isn’t collected and stored in the data warehouse, Martin can import data from social media sites, relate it to the sales data in the data warehouse and validate his idea.
Finally, analysts can use self-service BI tools, such as Power Pivot and Power BI Desktop, to create prototypes of the data models they envision. This can help BI pros to understand their requirements.
Self-service BI cautions
Self-service BI isn’t new. After all, business users have been using tools like Microsoft Excel and Microsoft Access for isolated data analysis for quite a while (Excel has been around since 1985 and Access since 1992). Here are some considerations you should keep in mind about self-service BI:
What kind of user are you? – Are you a data analyst (power user) who has the time, desire, and patience to learn a new technology? If you consider yourself a data analyst, then you should be able to accomplish a lot by creating data models with Power BI Desktop and Excel Power Pivot. If you’re new to BI or you lack data analyst skills, then you can still gain a lot from Power BI and this part of the book shows you how.
Data access – How will you access data? What subset of data do you need? Data quality issues can quickly turn away any user, so you must work with your IT to get started. A role of IT is to ensure access to clean and trusted data. Analysts can use Power BI
Desktop or Excel Power Query for simple data transformations and corrections, but these aren’t meant as ETL tools.
IT involvement – Self-service BI might be good, but managed self-service BI (self- service BI under the supervision of IT pros) is even better and sometimes a must.
Therefore, the IT group must budget time and resources to help end users when needed, such as to give users access to data, to help with data integrity and more complex
business calculations, and to troubleshoot issues when things go wrong. They also must monitor the utilization of the self-service rollout.
With great power comes great responsibility – If you make wrong conclusions, damage can be easily contained. But if your entire department or even organization uses wrong reports, you have a serious problem! You must take the responsibility and time to verify that your model and calculations can be trusted.
“Spreadmarts” – I left the most important consideration for the CIO for last. If your IT department has spent a lot of effort to avoid decentralized and isolated analysis, should you allow the corporate data to be constantly copied and duplicated?
TIP Although every organization is different, I recommend an 80/20 split between organizational BI and self-service BI. This means that 80% of the effort and budget should be spent in designing and implementing organizational BI artifacts and backend services, including data warehouses, data quality, centralized semantic models, trusted reports, dashboards, Big Data initiatives, master data management, and so on. The remaining 20% would be focused on agile and managed self-service BI.
Now that you understand now organizational BI and self-service BI compares, let’s dive into the Power BI self-service BI capabilities which benefit business users like you.