For data analysts, business intelligence professionals, and CTOs to optimize and scale business operations, they must first understand the business data that is available to them.
One of the best platforms to turn complicated data points from multiple platforms into a singular, coherent data set is Microsoft Power BI. However, you must first prepare the data sets to eliminate fragmentations and create structural consistency.
This article explores how to prepare data for Microsoft Power BI.
Table of contents:
- What is Microsoft BI?
- Who Is Microsoft Power BI For?
- Role Specific Uses
- What are the Advantages of Using Microsoft Power BI?
- What are the Challenges of Using Microsoft BI?
- How to Prepare Data for Microsoft Power BI
- Cleaning and Transforming Data
- What is an ETL?
- Why Use an ETL?
- Combining Integrate.io and Microsoft Power BI
What is Microsoft BI?
Microsoft BI is a platform that encompasses several tools for analyzing and visualizing business data. These Microsoft products are business intelligence, data warehousing, analytics, reporting, and online analytical processing.
Microsoft Power BI is an extensible set of tools designed to process large amounts of raw data into easy-to-read reports.
In essence, Microsoft BI powers different aspects of decision-making by providing insights into what will help organizations take actionable steps towards their goals.
Who Is Microsoft Power BI For?
Microsoft BI is for any business looking to understand their data and take actionable steps based on it.
Due to its flexible nature, the Microsoft BI suite can transform even raw data into visual, interactive reports and dashboards that are easy to understand for any member within the organization.
Most businesses today struggle to understand their data and make decisions on inaccurate assumptions.
If a business is looking to remain competitive and scale at competitive rates, they need a system capable of transforming all available data into valuable visuals with straightforward messages.
The importance of this can not be understated.
Organizations have multiple departments with varying skill sets. One of the most important elements of being successful in the data engineering space is understanding the data and effectively communicating the findings with all decision-makers within the organization.
A tool such as Microsoft Power BI can drastically help relay valuable pieces of information to the stakeholders and decision-makers.
However, one important thing to know about Power BI is that each role within an organization will have different needs for the Power BI software and can go from simple tasks useable by all skill-sets to complex tasks designed for data engineers.
Role Specific Uses
The use of Power BI depends on the need for particular roles within an organization. Power BI is suited for different roles in the organization. Here are a few examples of different uses based on various roles in an organization.
Business managers- A business manager can create and share dashboards with employees or external stakeholders to communicate important information. Business managers should be familiar with their organization's data and be able to explain the data within their teams. They can use these insights to make better decisions that lead to effective results.
Data analysts- Data analysts are tasked with cleaning dirty data into usable datasets for Power BI to load onto reports. Data Engineers create processes, toolsets, workflows, and APIs to prepare the data to be used inside Power BI.
Business Intelligence Developers- Business developers will often use the APIs in Power BI to create custom applications. This can be achieved through inputting the data and transferring them into data sets, and from there, they can create custom visuals.
General Employees - General employees may only use Power BI service features to view data and charts. Often, employees will primarily use this feature for output reasons rather than actual input.
What are the Advantages of Using Microsoft Power BI?
There are many advantages to using a program such as Microsoft Power BI.
As briefly mentioned above, one of the key advantages is the ability to easily transform data into anonymized, usable chunks to be used in an interactive visual format. This allows for greater understanding and deeper analysis of user-friendly dashboards and reports.
Business intelligence is most effective when it can provide all stakeholders with a single source of information to make educated decisions about business performance and prospects.
Power BI's advantages lie in its flexibility to meet any organization's needs, regardless of whether it's a small startup or large corporation. The multitude of different features comes in handy when creating a custom solution suited specifically for a company's needs.
What are the Challenges of Using Microsoft BI?
While Microsoft BI can have significant benefits for an organization, some challenges come along with it.
One of the biggest challenges is that companies often store their data in hundreds of different locations.
Centralizing this data in a systematic approach can be a timely and challenging process.
The second challenge is that since these data sources are in multiple locations, the data must be adequately cleaned and prepared before use.
Data preparation can be a highly arduous, expensive, and time-consuming process. According to Microsoft, data preparation takes up 60 percent to 80 percent of the time and money allotted to an analytics project.
Fortunately, there are some things you can do to help organize and streamline the data preparation process.
How to Prepare Data for Microsoft Power BI
To properly prepare data to be utilized inside of Microsoft Power BI, often a business will want to set up a data pipeline that looks something like this:
- Data is stored in hundreds of internal locations of an organization
- Clean and transform the data
- Centralize the data
- Import the data for use inside of Microsoft BI
Here we will break down how to make a data pipeline based on these four steps accessible starting with the importance of cleaning the data.
Cleaning and Transforming Data
There are various ways to cleanse and transform data. Some people like to cleanse and transfer the data within Microsoft BI. However, this can be a lengthy process.
The first option is to clean the data inside of Microsoft BI manually. Here are the steps to manually cleaning data.
Here are some helpful tips for someone who does want to cleanse the data manually.
1. Analyze the Imported Data
Once dirty data has entered Microsoft Power BI, the first thing to do is review it to see if everything looks as it should. This means checking to ensure the headers all make sense, the columns are as expected, and there are no error symbols on the spreadsheets. Analyze all values that show as "null" and inspect leading and trailing lines.
2. Ensure the Data Type is Correct
The data type is a classification that identifies the type of value a variable has. Data types allow operations to occur using that data point without causing errors. A typical data type is an integer, which represents the classification of a whole number.
Once data gets imported into Power BI, data types will automatically apply to the data points. The data points must be correct before you can move forward.
3. Ensure Differentiation Across Field Names
When importing data sets that may have unnamed fields, Power BI may apply a category name it has identified as valid. If Power BI sees two data sets that look similar in their structure, the field names may replicate. Ensure that each field name is unique before using the data in Power BI.
Concluding Manual Cleansing Tips
While you can manually cleanse data within Microsoft Power BI, if your organization is importing thousands of data sets, this can drain productivity. To put things in perspective, on average, a single organization uses data from over 400 sources, while 20 percent of companies employ more than 1000 data sources.
The second way to clean data is to use automated software known as ETL's.
What is an ETL?
An ETL is a system that takes a large volume of data and converts it for use in a storage warehouse.
ETLs comprise three primary steps:
Extract: This step reads data from its original location and copies it to a new environment.
Transform: This step modifies the extracted information so you can use it in Power BI. This may involve removing duplicates, sorting values alphabetically, or converting numbers into graphs.
Load: This step loads the changed information into data storage.
One of the most popular cloud data warehousing solutions is Google BigQuery and Amazon Redshift.
If a company is not interested in using cloud storage technologies, it can store its information on-premises or in something called a data lake.
An ETL has many benefits over manual correction of data, as it saves time and reduces room for error.
Why Use an ETL
When trying to utilize Microsoft Power BI, the primary objective is that the data inputs are accurate and aligned with one another.
ETL's do just that.
If an organization has thousands of data sets, storing them in a centralized place such as Microsoft Azure can be very helpful.
The primary reason for using ETL software is that it is fast and efficient.
These programs require little human interaction when compared to regular data manipulation, which means there is less room for error. They also help save time because they automate repetitive tasks, like naming fields properly or changing a field type into something usable within Power BI. This automation can be done very quickly with minimal human interaction.
ETL's have proven valuable to businesses of all sizes by improving efficiency and accuracy in creating reports.
Combining Integrate.io and Microsoft Power BI
The optimal approach to preparing data for Microsoft BI is by using an ETL to transform and centralize the data.
Integrate.io is an ETL software that provides industry-leading technology to transform data and transfer it into a data warehouse.
Once the data has been cleaned and accessible in a data warehouse, importing the data into Microsoft Power BI is a simple process. The information is now ready for use.
Reach out today to see how Integrate.io can help your business centralize data and make proper sense of the numbers. For a demo on how the Integrate.io team works, schedule a time for an introductory call.