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Filtering in Power BI

A question I get often is how to filter reports in Power BI.  Well, there are many ways to filter your data in Power BI.  I am going to walk you through the three ways to filter your dashboards using Power BI: Slicer, Page Level Filters, and Report Level Filters.
 
Slicer
The data slicer tool shows up under the Visualizations section, this tool allows you to filter data on a single page of visualizations.
slicer
Click on the data slicer visualizations, and then drag the field you will be filtering on to values.  This will add a filter to the page you are working on.  For discrete values, you will have the option to do a list or a drop down menu for your slicer.
power bi slicer
For date values, you will be able to search by date range.
power bi slider
If you click on format, you will have the option to do a single or multi-select as well as turning on an option to select all values.
slicer format
 
Page and Report Level Filters
Report and Page Level filters are set-up in a very similar way.  The only difference between the two, is that page level only applies to the page it is on versus report level apply across all pages of a report.  Report and Page Level filters are more flexible than data slicers.  To add one of these filters you will just need to drag a field to the Report or Page Level filters section which always appears under your Visualizations options.
report and page level filters
If you pull a field on as a filter, you will have the option to do basic filter (which shows all values which can be selected) or advanced filter.
 
Once you choose advanced filter, you will have multiple options on how to filter your data; including contains, does not contain, is blank, etc.
advanced
 
Once published, your Report and Page Level filters will appear on the right side of your report.
report view
I hope this helps you get started in using Power BI filters.
 
 
 
 
 
 

Tableau’s R Integration

Ever wondered how data scientists and data analysts use Tableau for predictive analytics? The ability to integrate R into Tableau is powerful functionality. For those familiar with using R, it can be tricky to get started. Here’s how to get started with the R Integration.

Step 1. Set Up R on Your Computer

First, you will need to have a user interface for R on your computer. We recommend R Studio Desktop.

Step 2. Install RServe Package

Next, you will need to install the RServe package. To do this, click on Packages -> Install. Then, type in RServe and it will find the package for you to install.
reserve

Step 3. Set Up Rserve Connection

Now you will need to run the following code to start up the Rserve connection:
library(Rserve)
Rserve()

Step 4. Set Up the External Connection in Tableau

There is one more thing you will need to do prior to writing in R in Tableau, but to do this you will need to switch over to Tableau. Tableau needs to have the external connection set-up in order to run R.  Go to the Help -> Settings and Performance -> Manage External Connections.
R Serve
 
In the pop-up, type in localhost for the Server name. Click on Test Connection to verify it is now connected.

Step 5. Start Using R Integration

At this point, we can now start taking advantage of the R integration.  The integration uses calculated fields to pass R code. There are four different types of calculations used in the R integration:

  1. SCRIPT_BOOL
  2. SCRIPT_INT
  3. SCRIPT_REAL
  4. SCRIPT_STR

Which one you use depends on what type of value you expect to get as a result of your R Code.  SCRIPT_BOOL would be used if you expected a TRUE/FALSE value returned.  SCRIPT_INT would be used if you expected to have an integer returned.  SCRIPT_REAL would be used if you expected a numeric value returned.  SCRIPT_STR would be used if you expected a string value to be returned.
The basic set-up of any R calculated field is as follows:
SCRIPT_REAL (
“R code”,
Tableau fields being passed in
)
The R code would be encased by quote marks and the parenthesis would encase both the R code and any Tableau measures/dimensions that will be used inside the R code. You can pass in multiple Tableau fields, you will just need to separate the field names using a comma.
Two important items to know is that inside the R code, you do not use the Tableau field name. You will use .arg and you cannot mix aggregate and non-aggregate arguments.  Here is an example below.
script_bool
Within my R code, I would need to refer to sum([Profit]) as .arg1 and ATTR([Department]) as .arg2.  Also, I made Department an Attribute in order to use both it and Profit.

Example of R and Tableau in Action

Now that you have the basics of the calculated field, here’s a real life example using the Superstore dataset. We’ll be looking at the correlation between Profit and Discount.  The returned value will be a numeric value, so I will be using SCRIPT_REAL.
script_real
Now, use that field to visualize the correlation coefficient between Customer Segment and Supplier. A value close to -1 indicates a negative linear relationship between the variables. A value to close +1 indicates a positive linear relationship between the variables.
matrix
This is just a starter in using the R integration. Hopefully, this will help you get started using this at your own association. If you need help developing predictive models or using R, contact us.

Tame Your Data Analytics with Governance

As more and more associations invest in data analytics, it’s important to develop some policies that govern the use of data analytics. It might seem inconsequential, but an established governance policy for data analytics can bring order to your analytics efforts and help maximize your investment.

Signs that You Need Data Analytics Governance

It’s easier than ever to use data analytics. Many programs now come with some sort of analytics capabilities and business tools, like Tableau and Power BI, make it simple to visualize data. Data analytics democratizes data, but that democratization can cause problems when there is not agreement on what data to use and how to use it.
Here are some signs that you may need data analytics governance:

  1. Staff is using different, conflicting data to create visualizations
  2. Staff is unsure which report, dashboard, or visualization to use to make decisions
  3. Old dashboards that use out-dated data or key performance indicators are still in use

If any of those symptoms sound familiar, don’t be alarmed. This sort of confusion is common when you are dealing with a new technology and data analytics is still relatively new. The key is to start thinking about how you can improve communication and management of data analytics through better governance.

Developing a Data Analytics Governance Policy

Here are some steps to help you develop a data analytics governance policy.

  • Form a Cross-Departmental Team – People are more likely to support that which they helped create. Form a cross-departmental team to help develop the policy and monitor governance going forward.
  • Define Responsibilities – Consider developing a RACI matrix to help identify responsibility for creating, editing, and deleting visualizations and dashboards.
  • Identify Data Sources – Your data governance policy should have a complete data inventory, but it’s also important to document which data sources feed each dashboard or visualization. This provides context for the visualization, but also helps staff find data sources to correct data quality issues when they arise.
  • Determine Visualization Tools – There are a number of visualization tools available, but too many tools can cause confusion and slow adoption. Minimize the number of tools you use and carefully assess the value of visualization tools before introducing them.
  • Document and Track Analytics Projects – For new analytics projects, document the intended purpose, timeline, and measures for success. Make sure staff knows about past, current, and pending projects to avoid duplicate work and to increase usage.
  • Validate – Ask staff members to validate and test new dashboards and visualizations for accuracy and clarity.
  • Review – Regularly assess dashboards and visualizations for accuracy and relevancy. It’s important to check to make sure data visualizations are still accurate. Cross-check data against the data source to check for quality. When considering relevancy, think about the impact of the visualization. Has it added value to decision-making processes? Is it being used? Is it answering current business questions? If not, then you may need to retire the visualization.

Finally, make sure your data analytics policy is part of your larger data governance policy. And keep it simple! This depends on your staff, but most people have trouble remembering a lengthy set of rules. Consider developing a short, memorable, and meaningful list of policies.
Governance can be a little dry, but it’s key to maintaining a shared understanding of data and ensuring the ROI of your dashboards and visualizations.
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Using Learning Analytics to Support Students

For many associations, education is more than just a source of revenue and a key member benefit. It is a cornerstone for the organization’s existence. Despite the importance of education, associations still struggle with how to measure the success of professional development programs. According to Tagoras, 87% of associations offer e-learning, but less than 30% use data to make decisions about their educational programs.
The problem is two-fold for associations.
First, there’s the question of what associations should be measuring to gauge the impact programs have on students.
Second, associations have to figure how to measure impact.

What to Measure in Learning Analytics

Recently, Debbie King, Association Analytics® CEO, presented a session about learning analytics at the American Society of Association Executives (ASAE) Spark Conference, an online conference about the art and science of adult learning. What is learning analytics and how can it help your association?
George Siemens, a noted expert on the topic, wrote that “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”
This is distinct from business analytics for educational programming, which focuses on operational or financial performance.
Most associations have access to business analytics for their educational programming. They can find revenue, expenses, demographics of registrants, or number of registrants for any given program. This is important information, but doesn’t necessarily provide insight into how to better engage students or how to improve student success.
To better understand the distinction between learning analytics and business analytics, it’s helpful to look at the difference between common key performance indicators in both areas.
Learning Analytics KPIs
See the difference? Learning analytics is focused on the student and their success. Business analytics for professional development is focused on the program and its success. The two are closely related, but separate and its worth considering both to ensure program success.

How to Measure Learning Analytics

One major obstacle to engaging in learning analytics is determining how you will measure professional development programs.
Where is the data needed to understand impact? How do you combine data from multiple, disparate data sources to easily analyze information in a single location?
After you define what you want to measure, you need figure out where to get the information. Relevant data sources may inclue your Learning Management System (LMS), Association Management System (AMS), Event Management System (EMS), and even yoru web analytics program.
The data can then be integrated with a data warehouse where it can be combined and prepared for consumption by business users. You can visualize and interact with the data using a business intelligence tool, like Power BI or Tableau. To learn more about data warehousing, see our post on data warehousing.
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