Power BI

How can Power BI Contribute to Your Data Governance Efforts? (Part 1)

The unstoppable volume of data collected by healthcare organizations, combined with their sensitive nature, makes effective data governance essential.

In our last blog, we covered the basics of data governance and its goals. We have mentioned that data is crucial for making informed decisions, especially in healthcare, where decisions are about patient care, resource allocation, and business operations. However, the unstoppable volume of data collected by healthcare organizations, combined with the sensitive nature of this data, makes effective data governance essential. 

How can Power BI help you achieve a successful data governance implementation?


Goal 1: Ensuring Data Quality and Consistency
Inaccurate or incomplete data can have serious consequences in the healthcare industry. Patients rely on accurate and timely information to receive the best possible care, and healthcare providers need reliable data to make informed decisions. Here is how Power BI can help ensure consistency:

-    Data lineage enables you to track the source and alteration of your data. It serves as a useful tool for monitoring any modifications made to your data and ensuring data consistency across multiple sources.

-    Power BI query is a data cleansing and transformation tool that assists in standardizing your data by removing any inconsistencies or errors. These tools aid in converting your data into a uniform format that can be effortlessly analyzed and visualized.

-    Power BI promotes data sharing and collaboration, thereby making sure that everybody in the organization is making use of consistent and superior data.

-    Data validation guarantees that data meets predetermined quality standards. Moreover, Power BI presents alerts and notifications in case of any detected data quality concerns. For instance, users can set a rule that requires a particular column to contain dates and specify that the data type should be of a date format. Subsequently, Power BI will validate the data in that column to ensure that it is in the correct format.

-    Data Profiling facilitates a comprehensive analysis and visualization of data, with the aim of detecting potential data quality issues. Data profiling enables users to unveil patterns and relationships within the data, as well as to identify any missing, incomplete, or inconsistent data. This encompassing feature includes column distribution, column profiling, and data quality rules.

A-    Column Distribution is a tool that enables users to examine the distribution of values in each column of their data. By doing so, users can identify outliers and inconsistencies in their data that might require further analysis.
B-    Column Profiling, on the other hand, provides statistical analysis of each column in the data. It allows users to determine data patterns, such as the minimum, maximum, and average values of a column. This tool enables users to acquire a deeper understanding of their data, thereby facilitating informed decision-making.
C-    Data quality rules validate data against specific criteria through its Data Quality Rules feature. Users can establish rules that check for missing data or data that falls outside of a specific range, for example. By doing so, users can maintain high-quality data, which is fundamental for any data-driven business.

Goal 2: Maintaining Data Security and Privacy
Protecting patient data is a top priority for healthcare organizations. Data breaches and cyber-attacks can have serious consequences, including financial penalties, legal action, and damage to an organization's reputation. Here is how Power BI can help avoid this risk:

-    Row-Level Security rules are designed to restrict data access based on user roles. Users can view data that aligns with their level of permission. For instance, RLS rules can be set up to limit access to data for specific departments or regions.

-    User Authentication is done through various methods such as Azure Active Directory, Power BI, and custom authentication.

-    Encryption protects data in transit and at rest which includes encryption of data connections, data transfer, and data storage.

-    Data Classification allows you to classify data based on sensitivity levels, such as confidential or public.

-    Compliance feature includes GDPR, HIPAA, and other regulatory requirements which help ensure that data is secure and compliant.

-    Audit logs allow you to track user activities, such as data access, data changes, and report creation which help detect and prevent unauthorized access and data breaches.

Make sure to check part 2 of this blog the next 2 goals of data governance will be covered and how they can be achieved in Power BI: complying with regulations and improving decision-making and collaboration.

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