Archive for Dashboards

Coolest New Power BI Features Revealed!

Recently the Microsoft Business Applications Summit 2019 highlighted new Power BI features and these are the coolest features to note IMO:

1. New Power BI App Workspace Experience in Preview Power BI App Workspaces were introduced to enable collaboration amongst the data/business analysts within an organization. The new experience introduces numerous improvements to better enable a data-driven culture including:

•   Managing access using security groups, distribution lists, and Office 365 Groups
•   Not automatically creating an Office 365 group
•   API’s for Admins, as well as new tools for Power BI Admins to effectively manage workspaces

2. Printing Reports via Export to PDF
You can now easily print or email copies of reports by exporting all visible pages to PDF.

3. Bookmark Groups
Now you have a way to organize bookmarks into groups for easier navigation.

4. Python Integration in Preview
Now data scientists can use Python in addition to R within Power BI Desktop.

5. New Visual Header
More flexibility and formatting options have been added to the header section of each visual.

6. Tooltips for Table and Matrix Vizs
Report page tooltips are now available for the table and matrix visuals

7. Many to Many Relationships in Preview
You will now be able to join tables using a cardinality of “Many to Many” – prior to this feature, at least one of the columns involved in the relationship had to contain unique values.

And now I’ve saved the best for last!

8. Composite Models in Preview
With this feature, you’ll now be able to seamlessly combine data from one or more DirectQuery sources, and/or combine data from a mix of DirectQuery sources and imported data. For example, you can build a model that combines sales data from an enterprise data warehouse using DirectQuery, with data on sales targets that is in a departmental SQL Server database using DirectQuery, along with some data imported from a spreadsheet.

As you can see there are many new features to digest but it would be well worth your while to follow the links provided.

On a closing note, I’d like to give you a teaser for two new features coming up soon that will have a huge impact on self-service data prep and querying for big data:

  • Dataflows
  • Aggregates

Stay tuned!
Mario Di Giovanni, BASc, MBA, CBIP
Director, Business Analytics

More about Mario

 

Association Analytics: Begin with the End in Mind

Often association leaders ask me, “Where is the best place for us to begin our data analytics initiative?”  I like this question and am reminded of Steven Covey’s expression that before we undertake a new initiative, we should “begin with the end in mind”.  When it comes to data analytics for associations, this means starting with your strategic plan.
One association’s strategic plan, for example, specified that they would grow meeting attendance by 10% in 3 years.  This was their largest source of revenue.
In the past the association made decisions by looking at historical trends and was unable to confidently estimate expected attendance, revenue, venue amenities needed, onsite staffing levels and more.  In fact, the more events they held, the greater the risk of an incorrect estimate.
Yet because of the strategic initiative to increase attendance, the first thing they tried was to increase the number of events and market them by email campaigns and direct mail.
The visualization below is a simple bar chart showing the number events by year. The higher the bar, the more events. The color shading of the bars indicate the number of registrants. The darker the green, the more registrants. We can quickly see 2010 had the most events, but not the most registrants.  In other words, although the association increased the number of events, this did not increase the number of attendees. In fact, holding more events actually caused a decrease in meeting profitability as a whole, since there were more expenses associated with fewer registrants.
events
By reducing the number of events by combining smaller, similar events to a larger meetings, the organization was able to dramatically exceed the target set by the board.  In other words, by holding fewer events, they had more participants.

What is the Cost of Not using Data?

When making business decisions it’s important to weigh the costs of:

  1. Bad decisions
  2. Decision delay
  3. Lost opportunities
  4. Damage to image/reputation

Often these costs are much greater than the costs associated with analyzing data. Taking a “decision tree” approach can be a logical way to evaluate where to start:

  • Are there areas in your strategic plan where data can answer questions, provide insight and direction?
  • Which areas do our customers care about? Will it make a difference to them – add more
    value, improve their experience – advance our mission?
  • Do we have the data – or can we get it?
  • Will we be able to take action on the results of the analysis?

 

How to Choose the Right Visualization for your Association

Choosing the right data visualization is as important as choosing the right outfit to wear to an important meeting. Although your alma mater’s sweatshirt is perfect for the ball game, a suit and tie is more appropriate when trying to convince your board to increase your budget. Similarly, you are going to catch some flack for showing up to the game in a suit and tie! Choosing the right visualization for your audience is similar to choosing the right outfit for the function.
Did you know that the human brain is able to process images three times faster than text? From our primitive beginning, we’ve depended on our brain’s ability to detect subtle patterns and interpret meaning. So, how do you choose the right visualization? Let’s take a look at some common types of visualization and when they should be used to effectively communicate the story your data is telling.

Tabular

table

  • Best used when exact quantities of numbers must be known.
  • Numbers are presented in rows and columns and may contain summary information, such as averages or totals.
  • This format is NOT favorable to finding trends and comparing sets of data because it is hard to analyze sets and numbers and the presentation is cumbersome with larger data sets. It is estimated that the visual working memory has a capacity of about seven items. This means that you can store up to 7 bits of information (like numbers) in your brain’s “RAM” simultaneously. If you build a table with financial information for each month of the year for different areas of your association, it becomes difficult to find outliers or even the most profitable month.
  • This kind of visualization is likely what many association staff are accustomed to (think of all those excel spreadsheets floating around your office) so you may need to use a tabular format in conjunction with one of the other types listed below to convey the information.
  • A variation of the tabular chart is a highlight table. A highlight table applies color to the cell based on its value. The use of color can make outliers stand out more.

 Line Charts

line chart

  • Best used when trying to visualize continuous data over time.
  • Line charts use a common scale and are ideal for showing trends in data over time.
  • Example: membership or registrant counts throughout the year compared to previous years.
  • Trend lines and goal lines can also be added to compare actual counts with certain benchmarks.

Bar Charts

bar chart

  • Best used when showing comparisons between categories.
  • The bars are proportional to the values they represent and can be shown either horizontally or vertically. One axis of the chart shows the specific categories being compared, and the other axis represents discrete values.
  • Example: Bar charts can be helpful when looking at certain segments of your customers, registrants or members.
  • Goal lines can also be added to compare the actual counts with your benchmarks.
  • A variation of the bar chart is the stacked bar chart. This incorporates the use of color to visually show how certain segments add up to the total. In the example above, it’s easy to see that while 2010 Conference attendance counts are higher, the number of Paid attendees actually decreased from the previous year.
  • Another variation of the bar chart is called a bullet chart. This chart allows you to take a single measure (for example, revenue) and compare it to another measure (for example, revenue goal). It also can display percentiles.

bullet chart

Pie Charts

pie chart

  • Best used to compare parts to the whole.
  • Pie charts make it easy for an audience to understand the relative importance of values.
  • Using this format for more than 5 sections is not recommended as it can become difficult to compare the results. Too many sections make interpretation difficult because the difference between the sections can become too narrow to effectively interpret.
  • Often, even when wanting to compare parts to the whole, a bar chart can be more effective.

In addition to difference chart types, the use of filters and sorting is important to increase the association staff person’s ability to explore the data in more detail.
The goal of any visualization should be to communicate the information in the most concise and impactful way by using the appropriate visualizations for your data.  Effective visualizations enable your audience to quickly understand the story in the data and speeds the ability for association staff to reach key insights.

Data Discovery – a “Bicycle for the Mind”

Steve Jobs said that computers are like a “bicycle for the mind” allowing us to go further faster with less effort.  Within the field of business intelligence, I believe data discovery should be the same, amplifying our intelligence and creativity, allowing us to see patterns and insights which we can use to create the future of our organizations.

Data Visualization is Key

A key feature of data discovery is the ability to present data visually in order to quickly convey the information to our brains.  The goal is to stimulate our minds and then to allow us to interact and have a direct “conversation” with the data.  We “ask” follow-on questions by clicking through and moving elements on the screen, “re-presenting” the data many ways.
We are inspired when we see data visually and notice how it changes based upon the questions we ask.  We can see the power as we interact with the data and the analysis process becomes a natural extension of the activity of thought, enabling us to drill down, drill up, filter, bring in more data sources, or create multiple visual interpretations.  Interactivity supports visual thinking.  We can work with the data visually at the speed of thought, rather than writing queries to the databases.  We can learn and reach insights faster.  When we interact with the data visually, we are participating in the data discovery process and data becomes our partner in gleaning the information that becomes business intelligence.

How Does your Association Make Decisions Today?

An MIT study shows that most organizations still rely on instinct, intuition, politics and tradition.  But according to Harvard Business Review, top performing organizations are 5x more likely to use data to make decisions.  If we know that decisions based on data tend to be better decisions, why don’t we use it all the time?  Research says that only 15-20% of organizations believe they have access to the data they need in order to make good decisions.  Why is this?
IVeniceMonet once heard it explained this way – imagine you are Monet and you are explaining to a friend how to create a painting of Venice at twilight – like this one.  You might say, start out with a church steeple on the left sort of a brownish color and surround it by a golden light, with deep blue in the sky.  Oh yes, and maybe add some little purple/violet touches in the water to the right.  Do you think your friend would create a painting that looks like this one?  Probably not.  And yet that is what is happening daily between business and IT.  Business tries to describe in words the data that they want to see.  And IT tries their best to represent in reports and dashboards what they heard the business say.
The way it typically works is you have a question and ask IT to create a query or report and then when they deliver it, usually you have another question, or want another piece of data or have it grouped a different way.  Traditionally this change request has been considered a BAD thing!  And then you have to wait for IT to revise it.  At least that’s what used to happen to me when I worked for an association.  This process doesn’t really work well for either the IT department or the business users in the association trying to make decisions.

Change the Ending

Association staff want access to their data!  They don’t know their questions until they start to see the data.  Data discovery enables them to have a conversation with the data directly.  Data discovery allows all of us to shift perspective quickly and change the way we look at a problem.  We can cycle through different views deliberately trying to get the insight to pop out!
This is how we learn to understand the story our data is telling.  And once we understand the story, we can change the ending.