Business Intelligence Community · 2016. 9. 23. · Business Intelligence Community BIC@UI. Welcome...
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Business Intelligence Community BIC@UI
Transcript of Business Intelligence Community · 2016. 9. 23. · Business Intelligence Community BIC@UI. Welcome...
Business Intelligence Community
BIC@UI
Presenter
Presentation Notes
Welcome everyone. My name is Brenda Ulin, and I’m a Data architect and interim lead of the Business Intelligence Shared Service Center in ITS. I also chair the Institutional Data Users Group and am so very excited about our newly formed BI Community.
BIC@UI
September 21st, 2016
BI Community Purpose Overview & Background Brenda Ulin
BI Community Steering Committee & Website Nick May
Stephen Few Dashboard Design: What NOT to do Dawn Moore
BI Community Focus Feature Input Forum Group
BI Community Kickoff PRIZE drawing Group
Presenter
Presentation Notes
For today’s initial BIC@UI session we will Provide an introduction to the BI Community and some context to the formation for the group Introduce the steering committee and share some resource information Our focus feature today is a visualization best practices presentation by Dawn Moore, based on principles of Stephen Few We will follow that up with a Focus Feature input forum and wrap up by drawing for our two BI Prize giveaways.
BI CommunityPurpose Overview & Background
Presenter: Brenda Ulin
ITS BI Shared Service Center (BISSC)
Presenter
Presentation Notes
To start we wanted to provide some context about the community. All of you should have received the charter document as an attachment to our invitation emails. I encourage you to read the charter and to provide us with feedback. If you have not yet read it, Nick will share with you shortly where you can find a copy.
Data & BI
Institutional Data Users
Group (IDUG)
BI Shared Service Center (BISSC)
OneIT BI Needs
Assessment
Campus Data Portal
BI Community (BIC@UI)
Institutional Data and Business Intelligence
Focus and initiatives over last two years
Presenter
Presentation Notes
Over the last two years we have seen some deliberate focus on Institutional data and BI Institutional Data Users Group(IDUG) – created in May of 2014 with apurpose to increase data literacy through monthly knowledge sharing. We have averaged approx. 85 attendees per month and have just recently transitioned to a bi-monthly format. - how many attended IDUG? How many presented? I would continue to expect to see the technical crossover into the IDUG community, but we will not expect to see the functional IDUG membership as part of the technical BI Community. BI Shared Service Center – Central IT team focused on delivering BI Solutions, Officially last fall but we already have a handful of solutions. The team currently consists of myself, Dawn Moore and Alex Junk. OneIT BI Needs Assessment –We saw the One IT Bi Needs Assessment comet to fruition. project to determine campus information gaps for their strategic, tactical, and operational decision making and how best to fill them. We interviewed around critical decision makers across campus, in academic and administrative units and heard a lot of fasincating information. How many heard about this effort and looked at the final report. I encourage you to read the summary report, we will publish a link to our web page. You may want to drill into your specific organization or department to see what your leadership identified for key business intelligence and information needs. Creating a BI community was a potential strategy to increase becoming a more data driven campus. Ed Hill and his team have developed Campus Data Portal – focused on publishing, accessing, and finding data. How many know about or have used the Campus Data Portal? Ed and his team will present October IDUG Session preview latest release of new features and enhancements.
BI Where are we heading?
Difficulty
Valu
e
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
What happened?
Why did it happen?
What will happen?
How can we make it
happen?
Gartner
Presenter
Presentation Notes
So, we’ve seen a lot of focus around the data, assessment of BI needs and some early BI solutions. But where are we really on the data to knowledge spectrum, and what is the potential? Through BI we as providers have the opportunity, responsibility really, to help our customers use data, through analytic solutions and optimize their use of information. We have the capability to develop Business Intelligence solutions that takes us through the spectrum of analytics from Descriptive, What Happened, Diagnostic, Why, Predictive What? Through Prescriptive, How can we make it happen.
BI Why do we want to get there?
Facts
Data
Information
Knowledge
Understanding
Enabled Intuition
No timely retrievalDisorganized Data
RetrievableOrganized Data
Information views targeted by roleOperational Collaboration
Reusable InformationPut into context of targets or time
Business ModelingStrategic Collaboration
Breakthrough Visionary Thinking
Objective:Improve speed, effectiveness, and efficiency of decision making
Altera
Presenter
Presentation Notes
Because even with all the wonderful initiatives around data and BI, there is so much opportunity to enhance the decision making processes across campus. Through our BI solutions we can improve the speed, effectiveness and efficiency of decision making. Where do you feel your organization or departments are on the data to knowledge spectrum? What can you do to help move your area up the information latter?
BI Community
BI Practitioners
Technical Solution Providers
Informatics & Statisticians
BAIS Faculty/Students
BI Technology Resources
Business Intelligence CommunityBIC@UI Increased Communication & CollaborationBI Creativity, Innovation & Networking HubEnable Data Driven Decision MakingDevelop common BI LanguageRicher Data PortalsPredictive & Analytic modelsBAIS Student OpportunitiesCollaboration on SolutionsTechnical training and awarenessEnhanced self-services BI ComponentsDeepening technical relationships
across campus
Presenter
Presentation Notes
To take us to the next level, we are developing the BI Community, which is more closely aligned to the Campus Application Developer Community, Really about bringing together BI Practitioners and technical solutions providers to raise awareness and knowledge of BI Trends, technologies and enable further development of Campus Solutions. Membership will be made up of technical resources from a wide variety of disciplines and organizational backgrounds. In the upper right you will see a list of Goals mentioned by the Steering Committee: One thing you would like to see BIC@UI accomplish (strategic or visionary level)
BI CommunitySteering Committee
BI Community Website
Presenter: Nick May
College of Public Health
Presenter
Presentation Notes
BI Community Steering CommitteeMember Title College/Unit/DepartmentRebekah Ahrens Senior Application Developer College of Liberal ArtsMatt Anson Assistant Director, Academic Planning & Analysis, Assessment Academic Planning & Analysis,
Assessment
Jim Borland Database Administrator and BI Architect Information Technology Services
Grant Brown Assistant Professor, Biostatistics BiostatisticsHans Hoerschelman Application Developer Business Services
Andrew Jenkins IT Director, Graduate College Graduate CollegeJose Jimenez IT Director, RIS Research Information Systems
Mike Kaplan Director Administrative Systems HR Human ResourcesNick May *** Application Developer College of Public HealthBrandon Mills IT Director, Residential Services Residential and Student Life Services
Mike Noel* Director, Administrative Information Systems Information Technology Services
Duane Staskal Lead Application Developer College of MedicineNick Street Professor, Management Sciences Management Sciences, TCOBBrenda Ulin** Interim Lead Business Intelligence Shared Service Center Information Technology Services
Kris Yows Senior Application Developer Office of the ProvostKatie Tunning IT Director, Health Care Information Systems HCISJosh Gants Director of Application & BI Development UI Foundation
* = Executive Sponsor, ** = Chairperson ***=Co Lead
https://itcommunities.uiowa.edu/business-intelligence-community
Presenter
Presentation Notes
Mention the BI Listserve address here as well Charter is on website as well as archived event information and link to upcoming events
13 Most Common Dashboard Mistakes
Based on the book Information Dashboard Design
& Business Intelligence Workshops
by Stephen Few
Presenter: Dawn Moore
ITS BI Shared Service Center (BISSC)
Presenter
Presentation Notes
Welcome everyone. My name is Dawn Moore, and I’m a BI architect with the Business Intelligence Shared Service Center in ITS.
Sources
Today’s topic comes from Stephen Few’s Business Intelligence Workshop, which is based on his books.
4-day workshops held around the world
Show Me the Numbers: Table and Graph Design
Information Dashboard Design
Now You See It: Visual Data Analysis
Attended his workshop in Minneapolis in June
Next event in the US is the East Coast Visual Business Intelligence Workshop from Apr 24 to Apr 27, 2017 in Portsmouth, Virginia
Presenter
Presentation Notes
Today I’m going to share with you some of the things I learned at a recent Business Intelligence workshop hosted by Stephen Few. His workshops are based on his books, and an entire day of the 4-day workshop is devoted to dashboard design. If you’re not familiar with Stephen Few, he is recognized as a “leading expert in data visualization …and communication.” Notice I didn’t say he’s an expert on a BI tool. Few’s focus is communication, and that’s what we’re going to focus on today: effective communication … specifically the 13 most common mistakes people make when designing dashboards that impede or inhibit effective communication.
You’ve met with your customers to talk about what data they need…
You’ve labored over the ETL to ensure all of the data is accurate…
You’ve sourced the data…
You have a big, beautiful data warehouse full of amazing data… now what?
Presenter
Presentation Notes
So, you’ve taken the time to curate your data. You’ve met with customers and labored over the ETL to ensure all of the data is accurate. You’ve built a big, beautiful data warehouse full of amazing data. Now what? Well, now you need to get the data to the people that need it in a way that makes sense to them. You need some BI artifacts.
What Is a Dashboard?
BI Artifacts
Dashboards
A dashboard is a specific type of BI artifact.
All dashboards are BI artifacts, but not all BI artifacts are dashboards.Reports Info-
graphics
Presenter
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In some cases, users will use the tables and views you’ve created. But often the data needs to be exposed in different ways … BI artifacts can be reports, spreadsheets … more and more often today users want easy access to common key performance indicators and metrics. Basically, they want “dashboards”. First, at least for the purposes of today’s conversation, we need to agree on what a dashboard is. A dashboard is a specific type of BI artifact that’s different from regular reports, or infographics.
Definition of a Dashboard
A dashboard is a visual display
of
the most important information needed to achieve one or more objectives
that has been
consolidated on a single computer screen*
so it can be
monitored at a glance.
Because computer monitor resolutions vary, a “single screen” will depend on the typical resolution size of your target audience.
Presenter
Presentation Notes
A dashboard is a visual display of the most important information needed to achieve one or more objectives that has been consolidated on a single computer screen so it can be monitored at a glance.
Presenter
Presentation Notes
This is an example of a well formed dashboard created by Stephen Few. I’ll show this again at the end of the list of mistakes, and we’ll take a little closer look at why it’s well-formed. For now, let’s dive into the most common issues…
Exceeding the Boundaries of a Single Screen
There are limits to the human mind’s working memory.
It is temporary
It has a limited capacity
We can store only 3-4 “chunks” of data in working memory at a time.
By putting the most important information together on a single page, it can be viewed and interpreted together, reducing the requirement to swap chunks in and out of our working memory
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Much like a computer, the human mind has a limited amount of “working memory.” We can only hold 3-4 chunks of data at a time and then it’s full.
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Sales 1
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If you have sales on one page…
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Employees
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… and employees on another, it’s very difficult to compare the two.
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However, if you put them on the same page…
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Sales vs Employees 1
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Or better yet the same graph, they’re much easier to compare. Don’t make the users work any harder than they have to, to get the data they need.
Supplying Inadequate Context for the Data
Quarter to date sales are $856,000. Is that good or bad?
What types of questions might you ask to determine if it’s good or bad?
Context
What quarter?
Where are we at in the quarter?
What were sales last quarter? Last year for the same quarter?
What were projected or expected sales for this quarter?
Is that above or below the target?
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Raise your hand if you think that’s good…. If you think that’s bad. The reality is, we don’t know… because we haven’t been provided sufficient CONTEXT. Don’t make the users work any harder than they have to, to get the data they need.
Displaying Excessive Detail or Precision3
Dashboards should display high level information to provide an overview
Excessive precision makes data difficult to read and interpret
Region YTD Annual Sales Profit Margin
East $58,627.36 36.8975%
West $62,333.98 42.6458%
South $100,566.84 13.9485%
North $16,226.64 7.4987%
Region YTD Annual Sales (USD) Profit Margin
East 58,627 37%
West 62,333 43%
South 100,566 14%
North 16,226 7%
Presenter
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Dashboards should display high level information to provide an overview Excessive precision makes data difficult to read and interpret Notice how much easier the 2nd table is to read with the cents removed and the profit displayed at an overview level.
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And if you’re thinking that nobody actually does that … yes, they do. Don’t make the users work any harder than they have to, to get the data they need.
Expressing Measures Indirectly4
Don’t make users “work” for the data. Show them the data they want to see.
If the key performance indicator is profit, don’t show sales and costs & make the user calculate profit, just show profit.
Presenter
Presentation Notes
Don’t make users “work” for the data. Show them the data they want to see. If the key performance indicator is profit, don’t show sales and costs & make the user calculate profit, just show profit.
020,00040,00060,00080,000
100,000
Revenue Variance to Budget
Budget Revenue
4
-30%
-20%
-10%
0%
10%
20%
30%
40%
Revenue: Actual to Budget Variance
Budget Variance
Presenter
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In the top chart, revenue and budget each have their own lines. In order to calculate the variance or difference, the user would need to subtract actual spend from budgeted spend for each month. In the bottom chart, the math is done FOR the user making interpretation of the data much faster. Don’t make the users work any harder than they have to, to get the data they need.
Choosing Inappropriate Display Media 5
Presenter
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Before we talk about what is “appropriate” we need some background on why some visualizations work and some don’t. One of the key things that Few talks about in his dashboard design book are preattentive attributes. Certain attributes of what we see are recognized during preattentive processing at extraordinarily high speed, which results in certain objects and their attributes standing out … all without conscious thought. The preattentive attributes here are the ones that are most useful to us when presenting data visually. Some of these visual attributes are perceived quantitatively (i.e., some values are greater than others), which are marked with red arrows. The visual attributes that are marked with a pale red arrow are perceived quantitatively but not as powerfully as those with the bright red arrow. The rest are perceived, but NOT quantitatively. Stephen Few goes into much greater detail about the science in his books and workshops … for the purposes of today’s discussion I’m just going to show you a few examples of how powerful this can be when done well … and a few when it’s not.
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Which visualization makes it easier to see a pattern? That’s because our brains see the individual numbers in the table as individual “chunks” and it can’t hold them all at one time. But our brains also see entire lines as chunks, so the patterns become clearer using the chart.
Presenter
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How about here? Again, we’re taking advantage of the brain’s ability to recognize certain attributes – like the length of lines – to speed up recognition of patterns. Don’t make the users work any harder than they have to, to get the data they need.
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Easier to count the 5’s in the 2nd photo right? That’s because intensity is one of those pre-attentive attributes.
What’s the value of the larger circle?
Using Area to Encode Values5
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Answer is 16. Don’t feel bad if you didn’t get the right answer. The human mind just isn’t wired to interpret circle area. We can tell the second circle is bigger than the first, but can’t interpret differences in area.
Our natural tendency is to compare the sizes of two circles using a single dimension (diameter).
For February, it looks like revenue is 3x operating costs, but in reality the area of the circle representing revenues is 5 ½ times bigger than the operating costs.
February and April revenues are roughly equal.
In January, what are revenues?
5
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Our natural tendency is to compare the sizes of two circles using a single dimension (diameter). For February, it looks like revenue is 3x operating costs, but in reality the area of the circle representing revenues is 5 ½ times bigger than the operating costs. February and April revenues are roughly equal. In January, what are revenues? Don’t make the users work any harder than they have to, to get the data they need.
A simple bar graph would work much better. 5
Presenter
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Notice how much easier the data is for us to interpret when it is presented in an appropriate format like a bar graph. Don’t make the users work any harder than they have to, to get the data they need.
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Which slice of the pie is the biggest? Easier to tell in 3D? No! Easiest is the bar chart. You can quickly tell which section is the biggest. That’s because our brains do not interpret areas of circles – or parts of a circle like a slice of pie – well at all.
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People are impressed by displays that don’t work …until they must actually use them for something real.
Presenter
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Choosing Inappropriate Display Media While some display media seem cool and may be requested by the users, that “coolness” factor wears off. People are impressed by displays that don’t work … until they must actually use them for something real. Don’t make the users work any harder than they have to, to get the data they need.
Using Poorly Designed Display Mechanisms
In addition to choosing the right display mechanism, be sure to design it properly.
What problems do you see?
Excessive detail in labels
Excessively bright colors are distracting
3-D makes it harder to read
6
If purpose is to compare budget to actual for each region, this layout does not work
Presenter
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We avoided a pie chart and circles, but is this chart good to go? What issues do you see? Excessive detail in labels Excessively bright colors are distracting 3-D makes it harder to read If purpose is to compare budget to actual for each region, this layout does not work Don’t make the users work any harder than they have to, to get the data they need.
6OcclusionWhen data is obscured or hidden behind other data in a BI object.
What problems do you see?
3-D makes it harder to read
Direct Sales, Mail and Fax data is occluded.
Presenter
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What problems do you see? 3-D makes it harder to read Direct Sales, Mail and Fax data is occluded. Occlusion When data is obscured or hidden behind other data in a BI object. Don’t make the users work any harder than they have to, to get the data they need.
Introducing Meaningless Variety
Don’t be afraid to re-use similar charts on a dashboard.
Always select the means of display that works best, even if that results in a dashboard that is filled with nothing but multiple instances of the same type of graph.
Consistency can work in your favor, because it allows viewers to use the same perceptual strategy for interpreting the data, which saves them time and energy.
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Don’t be afraid to re-use similar charts on a dashboard. Always select the means of display that works best, even if that results in a dashboard that is filled with nothing but multiple instances of the same type of graph. Consistency can work in your favor, because it allows viewers to use the same perceptual strategy for interpreting the data, which saves them time and energy.
Introducing Meaningless Variety7
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What purpose do the varying colors on the bars serve? Actually, no purpose at all. There is no reason for the different colors, but users don’t know that. They may spend time trying to decipher why the bars have different colors. Don’t make the users work any harder than they have to, to get the data they need.
Encoding Quantitative Data Inaccurately
Sometimes graphical representations of quantitative data are actually miss-designed in ways that inaccurately display the quantities
When using bars, ensure the Y axis always contains zero.
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The bars that represent revenue and costs for the month of January suggest that revenue was about four times costs. An examination of the scale, however, reveals the error of this natural assumption: revenue is actually less than double the costs.
Presenter
Presentation Notes
It looks like January’s revenues are 4x the costs … but that’s not true. It only looks that way because the data has been encoded incorrectly. When using bars, ensure the Y axis always contains zero. Don’t make the users work any harder than they have to, to get the data they need.
Arranging Information Poorly
Make the most important data prominent
Minimize or remove logos
Arrange the data in a manner that fits the way it’s used.
Encourage comparisons
“If a dashboard isn’t organized well with appropriate placement of information based on importance and desired viewing sequence, and visual design that segregates data into meaningful groups without fragmenting it into a confusing labyrinth, the result is a cluttered mess.”
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If a dashboard isn’t organized well with appropriate placement of information based on importance and desired viewing sequence, and visual design that segregates data into meaningful groups without fragmenting it into a confusing labyrinth, the result is a cluttered mess. The goal is not simply to make the dashboard look good, but to arrange the data in a manner that fits the way it’s used. The most important data ought to be prominent. Data that requires immediate attention ought to stand out. Data that should be compared ought to be arranged and visually designed to encourage comparisons.
Presenter
Presentation Notes
Notice on the dashboard above that the most prominent position – the top left – is used to display the vendor’s logo and navigational controls. What a waste of prime real estate! As you scan down the screen, the next information that you see is a meter that presents the average order size. It’s possible that average order size might be someone’s primary interest, but it’s unlikely that of all the information that appears on this dashboard, this is the most important. Notice also that the line graph in the top center position displays the historical trend of order size, which logically relates to the average order size data that appears in the meter on the left, so why isn’t it next to the meter so their relationship can be easily seen. Don’t make the users work any harder than they have to, to get the data they need.
Ineffectively Highlighting What Is Important
Data that is most important and should therefore be highlighted falls into two categories: data that is always important and data that is important right now.
Always Important:
Research indicates that particular areas of a screen or page receive different levels of attention from readers: specifically the upper left. Location is the best means to highlight some information as generally more important than the rest.
Important Right Now
When we look at a dashboard, our eyes should immediately be drawn to the information that is most important … even when it does not reside in the most visually prominent areas of the screen. Use of bolder font or brighter color is effective for this type of highlighting.
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We’ve already talked about the fact that the most important metrics should be in a place of prominence (upper left) because of how we read. But there is also the concept of what’s important “right now” … like the fact that an error condition exists. What’s important “right now” can be highlighted with bold or different color.
Presenter
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But what happens if EVERYTHING is bold and highlighted already? Then attempts to highlight what’s important “right now” fail. What stands out as important on this page? Buttons outlined in orange Prominent logo Bold colors on all graphs and charts. All data is equally bold and colorful, and therefore nothing stands out as important Don’t make the users work any harder than they have to, to get the data they need.
Cluttering It With Useless Decoration
Everything you put on the dashboard should further the users’ understanding of the data, not interfere with it.
Visual clutter like backgrounds and pictures get in the way of a user processing the information on the dashboard.
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Everything you put on the dashboard should further the users’ understanding of the data, not interfere with it. Visual clutter like backgrounds and pictures get in the way of a user processing the information on the dashboard. Here’s a chart we’ve seen before. In addition to the useless varying colors, we also have a dark background and background decoration.
Presenter
Presentation Notes
The makers of the dashboard above did an exceptional job of making it look like an electronic control panel. If the purpose were to train people in the use of equipment that actually looks like this by simulating it, then this would be great, but that isn’t the purpose of a dashboard. The graphics dedicated to this end are pure decoration, visual content that the viewer must process to get to the data. Don’t make the users work any harder than they have to, to get the data they need.
Misusing or Overusing Color
Don’t introduce color variation for no reason
Use bold color only to call out differences or highlight important information
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Don’t introduce color variation for no reason Use bold color only to call out differences or highlight important information
Presenter
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First – colors are muted, but they don’t mean anything. This causes user’s to try to decipher why they’re different colors… to search for meaning that doesn’t exist. Second – dark background … to much color saturation … highlighting Third – Neon colors … too bright and nothing stands out. Fourth – Dark background AND neon colors POINT OUT THE LAST ONE IS A POPULAR NEW APP Knowing how to use Word doesn’t make you a best selling author.
Designing an Unappealing Visual Display13
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We still want to create a display that is visually appealing for the users to use … this is an example of one that’s just plain ugly.
13 Most Common Dashboard Mistakes1. Exceeding the boundaries of a single screen
2. Supplying inadequate context for the data
3. Displaying excessive detail or precision
4. Expressing measures indirectly
5. Choosing inappropriate display media (pie charts!)
6. Using poorly designed display mechanisms
7. Introducing meaningless variety
8. Encoding quantitative data inaccurately
9. Arranging information poorly
10. Ineffectively highlighting what is important
11. Cluttering it with useless decoration
12. Misusing or overusing color
13. Designing an unappealing visual display
Presenter
Presentation Notes
Exceeding the boundaries of a single screen Supplying inadequate context for the data Displaying excessive detail or precision Expressing measures indirectly Choosing inappropriate display media (pie charts!) Using poorly designed display mechanisms Introducing meaningless variety Encoding quantitative data inaccurately Arranging information poorly Ineffectively highlighting what is important Cluttering it with useless decoration Misusing or overusing color Designing an unappealing visual display
Presenter
Presentation Notes
This is an example of a well formed dashboard created by Stephen Few. Note the features: Color has been used sparingly. The prime real estate on the screen has been used for the most important data. Small, concise display media have been used to support the display of a dense set of data in a small amount of space. Some measures have been presented both graphically and as text. White space alone has been used to delineate and group data. The dashboard has not been cluttered with instructions and descriptions that will seldom be needed. What could be better: Legend could include what the red dots/circles mean
Presenter
Presentation Notes
This is an example of a well formed dashboard created by Stephen Few. Note the features: Color has been used sparingly. The prime real estate on the screen has been used for the most important data. Small, concise display media have been used to support the display of a dense set of data in a small amount of space. Some measures have been presented both graphically and as text. White space alone has been used to delineate and group data. The dashboard has not been cluttered with instructions and descriptions that will seldom be needed. What could be better: Legend could include what the red dots/circles mean
Additional Resources
https://sharepoint.uiowa.edu/sites/bi/BISSC
http://www.perceptualedge.com/blog/
https://itcommunities.uiowa.edu/business-intelligence-community
Books Author
Information Dashboard Design Stephen Few
Show Me The Numbers Stephen Few
Now You See It Stephen Few
Information Visualization Colin Ware
The Visual Display of Quantitative Information Edward R. Tufte
BI CommunityFocus Feature Forum
Group Input session
Presenter: Group Exercise
BI Community
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Presentation Notes
We would now like to do a group exercise to get your input on future Focus Feature topics for the BI Community.
BIC@UI Focus Feature Forum• Small group breakout• Forms will be handed out• 15 Minute Group Discussion• 5 Minute Report Back
Please list suggestions for a future BI Community Focus Feature and who the presenter might be.A Focus Feature can be a Conference Recap, Training Session, Solution Showcase or any other ideas you may have. Presenters can be internal to the University, Administrative, Academic or a Vendor.
Focus Feature Topic Potential Presenter
1)2)etc.
Contact Name & Number in case clarification is needed:
Presenter
Presentation Notes
Nick May and Jim Borland will walk around and place you into groups They will provide one member in your group with a scribe sheet. Please take about 15 minutes to brainstorm content ideas for future sessions Technical focus Can be internal or external presenter, such as a IT group on campus, and Vendor or an Academic resource Think big and creatively When the groups are done we will take 5 minutes for a round robin report out from each group Please leave your scribe sheets at the table to be collected later After the report out we will move on to the prize giveaway
BI CommunityWrap Up & Prize Drawing
Presenter: Brenda Ulin
ITS BI Shared Service Center (BISSC)
Presenter
Presentation Notes
Thank you for your participation and engagement today. We look forward to bringing many technically focused BI topics, boot camps and training sessions to the BI Community.
BI Community November Session
Wednesday, November 16, 2016 8:30am-10:00am
UCC-2520D
SAS to RCollege of Public Health
Bio-Statistics
BIC@UI PRIZE
Business Intelligence Prize Drawing
