For industrial leaders, harnessing technology to make data-driven decisions and getting valuable insights has become pivotal. Owing to this trend, around 64% of SMEs and leading enterprises are expected to invest in Data Visualization by 2024.
While organizations are generating data exponentially, the demand for reliable Business Intelligence (BI) solutions is only anticipated to increase.
The digital solution Power BI has evolved as a leading tool that combines analytics & visualization into a single space. Enterprises might organize a wide gamut of analytics projects using Power BI and securely produce insights from different sources. Though the benefits of Power BI implementation are obvious, many still lack enough knowledge to accomplish it.
That’s why, adhering to a step-by-step implementation guide is important for handling all the pain points.
Power BI Implementation – Success in 5 Steps
The Power BI adoption plan requires strategic preparation, execution, and oversight from tech leaders. Understanding implementation procedures and pursuing the best practices might help enterprises set up a data-driven culture and navigate complexities.
Step 1: Determining Installation License
The implementation starts with acquiring the required Power BI licenses and forming the infrastructure. Tech leaders should obtain suitable licenses based on organizational requirements and deploy Power BI in their Cloud or on-premises. To find the right license, it is recommended to execute the following tasks:
- Evaluation – Leaders must assess the active licenses within their organization, specifically the subscriptions related to Microsoft 365. This helps in understanding whether Power BI licenses are already procured or not. If already available, check the license version and understand the functionalities and limitations available in the active license category.
- Procurement – When the enterprise does not have the essential Power BI licenses, consider obtaining the licenses. This way, experts can identify the licensing categories that ideally suit their organization’s project needs and budget.
- Governance – After the acquisition of the license, tech leaders should allocate the workspace to the authorized users. This ensures that the workforce can access the Power BI workspace, establish data integrations, and configure security settings. Furthermore, IT managers should define governance guidelines and policies for data accessibility within the Power BI ecosystem.
Remember that a solid license setup ensures a seamless transition to the following phases of implementation.
Step 2: Describing Business Requirements and Use Cases
To get the most out of Power BI, CIOs/CTOs need to categorize the business requirements. Otherwise, there is a huge chance of being unsuccessful since only 48% of Power BI users are driving innovation.
Hence, experts should define the sources of information that will be interconnected with Power BI. This helps in analyzing relationships between various systems and foreseeing connections. In this phase, self-service BI turns out to be ideal choice, allowing the workforce to produce their own visualizations.
Moreover, build report mock-ups to perform visualization requests, filters, and data cuts, laying the base for future projects. This approach demonstrates the model of self-service managed BI, highlighting the balance between IT governance and workforce empowerment. In short, this scheme allows enterprises to unlock the complete potential of their data assets.
Step 3: Data Models Development
With a clear know-how of business needs and objectives, experts can start building the data models. As a part of this process, experts should create a data structure that suits their requirements and incorporate metrics. Higher attention to detail in this stage ensures precise and trustworthy data analysis. Besides, it’s important to have collaboration among stakeholders and analysts to improve the models based on feedback and prerequisites. By developing robust data models, enterprises empower data-driven decision-making in the Power BI ecosystem.
Step 4- BI Data Warehousing
Typically, enterprises run and maintain a range of isolated data sources, starting from operational systems to data marts. Digital leaders would like to have a centralized data view from sources, but integrating Power BI directly is impossible. By integrating Power BI with Master Data Service (MDS), leaders can build a central repository or data warehouse. For handling intermediate data operations this warehouse works effectively, but eventually the restraint of this type of warehouse arises.
Let’s look at a popular use case of Power BI for data warehousing:
GH Retail, a leading US-based duty-free operator required to transform the organization’s financial data processing and management. To achieve this, they need a consolidated data warehouse that would allow retaining and analyzing all the financial data.
After partnering with a recognized Power BI Implementation services provider, they built a warehouse using Microsoft SQL Server. The user-friendly UI of the warehouse allows the managers to process precise major data straight within a centralized system.
We reduced 90% of the time of the Finance professionals to produce consolidated reports, says, George Fernandez, General Manager.
Step 5: Report Testing
Before sharing first reports and dashboards with associates or end-users, comprehensive testing is vital. Microsoft community offers “BI Validator” to automate the testing process and improve the whole testing journey. It offers steadfast environments to validate the exactness and guarantee compatibility across various devices and screen resolutions. On the whole, extensive testing ensures a greater user experience and enhances insights assurance delivered over Power BI.
Challenges & Solutions for Power BI Implementation
Like any new tool/technology deployment, some obstructions can arise during the early stage adoption of Power BI. Let’s look at some of the common challenges experienced by digital leaders and discuss solutions for fixing them.
Challenge 1 – Minimal Retention Time
This is one of the predominant challenges that tech leaders face during the implementation of Power BI. At first, the tool may not offer the required data history for specific analyses. Power BI possesses an average data retention duration of 24 months. This may not be sufficient for organizations that expect fast results and prolonged data retention time.
Solution: Assess data requirements and consider deploying a data archiving strategy to store previous data separately. Accordingly, use tools like SQL Server Reporting Services or Excel for analysis and reporting on historical data.
Challenge 2 – Restricted Data Storage
Another major challenge in implementing Power BI is the constrained data storage capability. Power BI comes with a restriction on the amount of information that can be logged in the Cloud-based version. This can be a hindrance for enterprises that maintain huge data volumes for a longer duration.
Solution: By hiring experts from the right Power BI development company, digital leaders can implement a hybrid setup. This scheme offers the benefits of Cloud-based analytics while providing better control over data security and storage.
Challenge 3 – Fragmented Data Models
Power BI allows importing data from diverse sources, which can lead to a lack of consolidated data models. This makes it tedious to analyze data reliably throughout the enterprise.
Solution: Build a centralized data model that caters to the whole organization’s data essentials. For this, collaborate with various teams to make sure the data model would suffice the requirements of all workers. Leveraging data modeling templates in Power BI helps leaders create standardized data models. This way, professionals can reuse it for future analyses and report creation.
Closing Thoughts
Implementing Power BI can be a disrupter for firms looking to unleash the potential of their data. However, the implementation procedure must be objectively planned, performed, and managed. Identifying a reliable development partner who can execute the solution’s implementation is important for an easy rollout. Overall, ensuring a meticulous implementation and utilizing best practices throughout the procedure.
