2. Contents
Introduction to Visual Data Discovery Tools.................................................................................................2
Definition ......................................................................................................................................................2
Key Features..................................................................................................................................................3
Data Connectivity..........................................................................................................................................4
SQL Database ........................................................................................................................................4
Hadoop..................................................................................................................................................5
Data warehouse....................................................................................................................................6
Excel Spreadsheet files..........................................................................................................................6
OLAP Cube.............................................................................................................................................7
The Visual Data Discovery Market................................................................................................................8
Choosing the Best in the Segment............................................................................................................9
Best in Segment Table.............................................................................................................................10
Qlik Sense................................................................................................................................................10
Power BI..................................................................................................................................................11
Tableau....................................................................................................................................................11
Self-service BI..............................................................................................................................................12
Tableau........................................................................................................................................................14
Using Tableau..............................................................................................................................................16
Evaluating Tableau......................................................................................................................................19
Dashboards .................................................................................................................................................22
References ..................................................................................................................................................26
Appendix .....................................................................................................................................................27
Appendix 1 – Worksheets & Related Question Number .......................................................................27
Appendix 2 – Questions & Related Dashboards.....................................................................................34
3. Introduction to Visual Data Discovery Tools
The recent data explosion has seen the volume, variety and velocity of data increase. This has created
the need for more powerful BI tools which can explore and understand data in more interesting and
user friendly ways. This is where Visual Data Discovery (VDD) tools come in. VDD tools not only explore
and understand data but are becoming the fastest way for business users to unlock the potential of the
data. Data is represented in a highly visual, interactive form that can provide valuable insights, at a
glance, to the user.
Definition
Visual data discovery tools are a relatively new addition to the business intelligence market. Like any
new tool there are a lack of sources that provided a clear definition which has led to a fair amount of
confusion on “what” it is and “what” it is not. This report will review and evaluate many academic
sources, BI Industry books, journals and reports as well as Vendor specific websites to try and identify
the most accurate definition. It has been initially agreed that ease of use, time to insight, the ability to
view data through highly visual reports are some of the key features of VDD tools. The three definitions
below were chosen as they represent two of the main industry players and an independent researcher.
Tableau, a business intelligence software provider who are currently Gartner's Magic Quadrant for
Business and Analytics Platforms top performer for VDD tools, define VDD tools as tools that "can
enable diverse types of users from data scientists working with big data to nontechnical business
managers and frontline users to see significant trends and patterns in data that they would have
struggled to see in voluminous tabular reports and spreadsheets".
David Stodder, Director of TDWI Research for Business Intelligence, added during his recent webinar
with Tableau that the "users’ ability to comprehend information quickly and put it to productive use
hinges on data visualization."
Datawatch, who provide the only Managed Analytics Platform that brings together self-service data
preparation with VDD, define VDD tools as interactive and exploratory. Tools that build on your intuitive
ability to find the nuggets of truly important information – the stuff that will transform your business.
You can ask questions of the data and see results instantly, then ask follow up questions in just seconds.
Drill down to get more detail about an outlier, or zoom out to discover trends you never saw before.
Interactive filters let you get rid of irrelevant data and noise so you can spot the underlying patterns and
see what others can’t. It’s a highly effective and iterative exploration that gives you the tools to make
better and faster decisions, no matter how much data you have to sort through or how quickly it
changes.
Wayne Eckerson Director, BI Leadership Research defines VDD tools as self-service, in-memory analysis
tools that enable business users to access and analyse data visually at the speed of thought with
minimal or no IT assistance and then share the results of their discoveries with colleagues, usually in the
form of an interactive dashboard. VDD tools are used by -
(1) Power users to explore and analyse data in a variety of systems,
(2) Super users and BI specialists to create interactive dashboards for colleagues and
(3) Casual users to view and work with those dashboards.
4. Cindi Howson, a BI industry expert and author of Successful Business Intelligence: Unlock the Value of BI
& Big Data, defines a Visual Data Discovery tool as a "tool that speeds the time to insight through the
use of visualizations, best practices in visual perception, and easy exploration. Such tool support
business agility and self-service BI through a variety of innovations that may include in-memory
processing and mashing of multiple data sources".
After careful consideration and deliberation a decision was made to compare Cindi Howson's definition
of a VDD tool against the other three definitions to find which one of the three definitions more closely
matched her definition. It was agreed that as an industry expert her definition gave a broader overview
of the VDD tool and seems the more impartial viewpoint. It captures the essence of what Visual Data
Discovery in a concise manner and would be the best definition to compare the others against. Given
this, the definition from Wayne Eckerson captures some key features and explains how the user can
manipulate the data in such a way that the business can make better and faster business decision.
The other two definitions where considered more technical and contain more industry jargon that could
be confusing to those who could be conceived as non-technical and unfamiliar with BI. The other two
definitions fail to mention much on the key features and more on the patterns and trends associated
with the tool. As both these definitions are from specific vendors of VDD tools they can be viewed as
having a bias towards their own products and therefore do not capture the true essence of what VDD
tools are.
Key Features
Through the research carried out to determine a definition for a VDD tool It was agreed that ease of use,
time to insight, the ability to view data through highly visual reports are some of the key features of VDD
tools. In this section of the report we will look to expand on these features and add to them to give a
better understanding of what they are and why they are key for any VDD tool to be successful in today’s
market. The features are listed below in the agreed order of importance.
1. Highly integrated graphics that incorporate data visualisations best practices takes the number
one spot on the list as VDD tools by nature should be highly visual. This allows the end users to
see graphical elements that would mean something to them. The tool should also be able to
automatically represent the data with the most appropriate visual for the type of data selected,
(i.e. provide geographical map views for a quick understanding of geospatial data, identify and
explain the relationships between variables, and offer a variety of analytic visuals such as box
plots, heat maps, and correlations.)
2. Data Connectivity meaning the ability to connect to multiple data source takes the second spot
on the list as modern businesses tend not store data in single place. Multi-platform data storage
solutions are the rule and not the exception. The ability to analyses data no matter where it is
stored to get the answers needed is essential to any business.Figure1 below shows a visual
representations of the different types of data sources that can be connected to a VDD.
5. Figure 1 http://www.qlik.com/products/qlik-sense
3. In-memory processing capabilities adds to speed of analysis and enable very fast interaction by
storing the data in memory so it does not have to continually access data from the mechanical
disk drive. There is no predetermined structure in-memory, so that analysis can be completely
ad-hoc and can be used against any element of the data
4. Integrated, intuitive, approachable analytic capabilities should remove the complexity of data
structures for nontechnical users so that they can explore and seek correlations on data sets.
Allowing users to slice and dice multidimensional data by applying filters on any level of a
hierarchy structure drill up and down through hierarchies or expand and collapse entire levels.
Calculate new measures and add them to any view and save views as report packages to share
with others.
5. Reporting is the number one BI process and has been for a number of years. As VDD tools are
aimed not only at technical but nontechnical users Easy report building is an important feature
allowing nontechnical users the means to share their finding with collages through dashboards
and story boards.
6. Ability to easily distribute insights via mobile devices and Web portals. As we are now in the
“mobile age” more and more business wants to access their data while on the move. Giving
users the ability to easily distribute insights via Mobile devices and web portals will encourage
more users to engage with VVD tools.
Data Connectivity
Throughout the investigation conducted to select a definition and find key features of a VDD tool it was
noted that most vendors were listing the same data sources when talking about data connectivity. There
were five data sources that every vendor spoke about and they were Excel spreadsheets, SQL databases,
Data warehouses, Hadoop and OLAP cubes due to these finding a decision was made to conduct further
investigation into what these source are and how they are used. Listed below are the five explanations
of the data sources outlining how they store and manage the data.
SQL Database
SQL databases are more commonly referred to as a relational database are used to store structured and
dynamic data , a relational database can be defined as a set of tables containing data fitted into
6. predefined categories. Each table (which is sometimes called a relation) contains one or more data
categories in columns. Each row contains a unique instance of data for the categories defined by the
columns. The example in figure 2, shows an appointment booking database for a vet’s surgery would
include a table to describe a customer with columns for name, address, phone number, and so forth.
Another would describe the pet and finally there would be a table to record the appointment details.
Different views of the data can be created to suit the users of the databases needs. This Data type was
chosen as it is the most commonly used data base type.
Figure 2
Hadoop
Hadoop, formally called Apache Hadoop, is an Apache Software Foundation project and open source
software platform for scalable, distributed computing. Hadoop can provide fast and reliable analysis of
both structured data and unstructured data. Given its capabilities to handle large data sets, it's often
associated with the phrase big data. The Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large datasets across clusters of computers using a simple
programming model. Hadoop can scale up from single servers to thousands of machines, each offering
local computation and storage. The Apache Hadoop software library can detect and handle failures at
the application layer, so it can deliver a highly-available service on top of a cluster of computers, each of
which may be prone to failures. Unlike relational database Hadoop can handle the storage of structured
and unstructured data. The diagram in figure 3 shows how the data in Hadoop is stored and queried.
Figure 3 https://www.linkedin.com/pulse/big-data-answer-hadoop-spark-jawad-yaqub.
7. Data warehouse
The term data warehouse was coined by William H. Inmon, who is known as the Father of Data
Warehousing. Inmon described a data warehouse as being a subject-oriented, integrated, time-variant
and nonvolatile collection of data that supports management's decision-making process.(Inmon, 1993).
Data warehouse can store these types of data:
· A data warehouse typically contains several years of historical data. The amount of data that you
decide to make available depends on available disk space and the types of analysis that you want to
support. This data can come from a transactional database archives or other sources.
· Derived data is generated from existing data using a mathematical operation or a data
transformation. It can be created as part of a database maintenance operation or generated at run-
time in response to a query.
· Metadata is data that describes the data and schema objects, and is used by applications to fetch
and compute the data correctly.
A data warehouse is made of data marts which often hold only one subject area- for example, Finance,
or Sales the diagram in figure 4 shows an example off many data marts are joined to create a
warehouse.
Figure 4- http://www.dataonfocus.com/data-mart-vs-data-warehouse/
Excel Spreadsheet files
Microsoft Excel has been the industry standard spreadsheet program since 1993. Excel has the same
basic features of all spreadsheets, using a grid of cells arranged in numbered rows and letter-named
columns to organize data manipulations like arithmetic operations. It has a battery of supplied functions
to answer statistical, engineering and financial needs. In addition, it can display data as line graphs,
histograms and charts, and with a very limited three-dimensional graphical display that allow users to
organize, format and calculate numeric data with formulas using a spreadsheet system ,Figure 5 shows a
basic excel report.
8. ]
Figure 5
Figure 6 http://clasificadosde.com/microsoft-excel-spreadsheet-templates.html
OLAP Cube
OLAP (online analytical processing) is computer processing that enables a user to easily and selectively
extract and view data from different points of view by slicing and dicing the cube of data. To facilitate
this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database
9. can be thought of as two-dimensional, a multidimensional database considers each data attribute (such
as product, sales region, and time period) as a separate "dimension." OLAP software can locate the
intersection of dimensions (all products sold in the Eastern region above a certain price during a certain
time period) and display them. Attributes such as time periods can be broken down into sub attributes.
An example of this is found in figure 7
Figure 7 http://www.oracle.com/technetwork/articles/sql/11g-dw-olap-100058.html
The Visual Data Discovery Market
Since around 2013 VDD tools have begun to receive widespread attention and deployment. Specialty
vendors such as Tableau, Qlik, Birst and Pentaho (to name just a few) have emerged to challenge the
Industry giants such as Microsoft, SAP, IBM, Oracle and SAS. As the speciality vendors are proving to be
more “agile” in deploying visual solutions they have gained large chucks of the market place with the
more traditional global multinational vendors seeing their market share stagnate. More recent trends
have suggested this gap maybe closing. Microsoft for example has release Power BI which is a free VDD
solution and has seen Microsoft regain ground on what are considered specialty vendors such as
Tableau and Qlik.
Gartners Magic Quadrant for BI and Analytics Platforms shows how vendors within the market place are
currently performing
10. Figure 8 http://www.informationweek.com/big-data/big-data-analytics/gartner-bi-magic-quadrant-
2015-spots-market-turmoil/d/d-id/1319214
Research of the VDD marketplace was conducted with the intention to highlight vendors and tools
considered “best in the segment”. In order determine “best in the segment”, and justify the choice,
many products and vendors had to be eliminated. To help with the elimination process a number of
factors which fall into certain criteria where considered. Each of which are listed below-
Platforms the VDD tool is available on- Windows, MAC etc.
An estimated rating – This has been sourced from independent journals, websites, academic
resources, Industry and social media reviews.
Deployment - Cloud, Mobile and traditional
Business size - Small, Medium and Large
Key Features
Availability to Research team – Including costs involved available demos of the software through
vendors and other third party sites.
Each member of the research team analysed several vendor tools independently from the other and
gave feedback on what vendor tools they found matched the criteria given above and which ones had
been eliminated. This enabled the search to be filtered down quickly with three vendors emerging as
potentially “best in the segment”. Tableau, Qlik Sense by Qlik, and Microsoft Power BI.
Choosing the Best in the Segment
The following table (Table 1-1) shows the three VDD Vendor tools selected as potentially the best in the
segment and the key features considered to be important for the success of this type of BI tool.
11. A rating system has been devised to give a quantative measure based on the results of the findings
(Table 1-2). The measures will help to justify which vendor and BI tool is considered “best in the
segment”.
Best in Segment Table Table 1-1
Rating System Table Table 1-2
Score Explanation
3 Exceptional capabilities
2 Very good capabilities
1 Limited capabilities, difficult to do, may require work around
0 Minimal capabilities out of the box. The software may require customisation
Qlik Sense
Qlik Sense [4] is the next generation, data visualisation product from Qlik. It provides self-service data
visualisation that is made to be fast, easy to use and understand, and to easily visualise data . Qlik Sense
helps business users use their intuition to prompt them to ask questions of the data that can provide
valuable business insights and that can lead to important business decisions.
Qlik Sense supports a range of use cases, including centrally deployed guided analytics apps and
dashboards, custom and embedded analytics, and self-service visualization [5] and includes an in-
memory engine. Below is a list of strengths and challenges associated with the software.
Main strengths include -
The ability to tell “Stories” through the use of storyboards. The user has a power-point
style platform that they can use to navigate through, and provide a highly visual
presentation of, the results of analysis.
Load scripts perform data cleansing and transformation.
Has the potential to take the place of a full ETL tool and data warehouse due to its
scripting and in-memory engine.
New measures and dimensions can be created and stored as a master item, providing
reusability across all the sheets within the application. [6]
Best in Segment Table Vendors
Key Features Tableau Power BI Qlik Sense
Integrated Graphics 3 2 2
Data Connectivity 2 3 2
In-Memory processing 2 1 3
Report Building 3 2 2
Distribution Via Mobile devices and Web portals 3 3 3
Analysis of Real Time & High Volume Data 3 1 2
Total Rating 16 12 14
12. Challenges
Currently does not include native forecasting features
Transformations and joining of tables requires scripting
Calculations can be created, but there’s no GUI for the functions
Power BI
Power BI is a visual data discovery tool developed by Microsoft it was originally known as Power BI for
office 365. Power BI is based on Power Pivot for Microsoft Excel and has now been developed into a
standalone BI application that works independently from Excel
Power BI is the newest to the market compared to the other two tools and has heavily focused on
collaboration and has integrated it with other Microsoft services and unlike the other two it is free for
businesses which have a Microsoft for business account.
Main strengths include-
Lesser Learning curve for those who come from a Microsoft background as many element are
based off existing products
The Integration to the cloud enables storing sharing and collaboration
No additional cost to businesses who have already adopted Microsoft services
Power BI has an app developed for (Windows Phone, Android and IOS)
Challenges
The product feels immature lacks many of the features of existing tools
Lack of information about the product and only has a small online community
Can only be shared to users inside the same organisation
Maximum of 10GB of Data can be stored on the cloud
Tableau
Tableau as a company was founded in 2003 as a commercial spin off a PHD research thesis conducted at
Stanford University’s Department of Computer Science between 1999 and 2002.The Company now
offers five main products: Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader and
Tableau Public the last two are available for free to use. In French tableau has two meanings -- painting
and table. Artwork and data. Excellent match of brand and product concept
Gartner the world's leading information technology research and advisory company have moved
Tableau into its coveted upper right quadrant this year, said that “Tableau has the intuitive, visual-
based, interactive data exploration experience that customers love to use and competitors love to
imitate.
13. Main strengths include -
State-of-art data visualization makes Tableau the stand out tool in visual data discovery.
Tableau people talk about "being creative with data”, it’s easy to see why looking at clean and
elegant Tableau dashboards you can create.
Tableau is a Research and Development (R&D) driven company. It continues to invest in R&D at
a higher pace (in terms of percentage of revenue — 29% in 2014) than most other BI vendors.
This approach has been fruitful as the company has now raised into the position of market
leader.
Web-based analytics in minutes, not days as tableau now connect directly to Google Analytics to
allow exploration of website' analytical data in an easy, visual way to find patterns and trends,
giving multi-dimensional insights into your website traffic.
Tableau's Data Engine has the ability to do ad-hoc analysis of millions of rows of data in seconds
with. The Data Engine is a high-performing analytics database on your PC. It has the speed
benefits of traditional in-memory solutions without the limitations that the data must fit in
memory. There’s no custom scripting needed to use this feature allowing people with a low it
skill set to use this function
Challenges
Tableau offers limited advanced analytics capabilities. R integration has been recently added
and is a major improvement for users needing more statistical and advanced capabilities. Other
vendors, such as SAS, SAP and Tibco, have more advanced native capabilities.
The in-memory calculations are slower than other venders such as Qlik Sense.
Self-service BI
VDD tools are becoming synonymous with self-service BI due to their ease of use and lack of IT
involvement. Gartner defines self-service BI “as end users designing and deploying their own reports
and analyses within an approved and supported architecture and tools portfolio.”[8] Imhoff, C. & White,
C. (2011) define self-service BI as “the facilities within the BI environment that enable BI users to
become more self-reliant and less dependent on the IT organization. These facilities focus on four main
objectives: easier access to source data for reporting and analysis, easier and improved support for data
analysis features, faster deployment options such as appliances and cloud computing, and simpler,
customizable, and collaborative end-user interfaces”[15]. [Figure 9]
14. Figure 9- The four main objectives of do-it-yourself or self-service BI. (Courtesy of BI Research and
Intelligent Solutions,Inc)
https://tdwi.org/Articles/2011/11/09/RESEARCH-EXCERPT-Introduction-to-Self-Service-Business-
Intelligence.aspx
Traditional BI created a centralized BI model with a heavy reliance on IT departments for producing
reports and analysis. One of the main reasons traditional BI best practices were considered “a bit of a
failure” was that requests for information became built-up as IT departments tried to cope with the
demand from every area of the business. This created a BI “bottleneck” leading to slow response times
(days or weeks) from IT departments to requests for reports and analysis. The current shift away from
the centralized BI model to a more decentralized model, in which business users are at the fore front of
defining requirements, has seen a shift towards “self-service BI”. Self-service BI has become a top
investment and innovation priority for businesses over the past few years. Self-service BI has the
potential to offer great value to businesses and as such many industry leaders agree that it should be
part of any organisations BI portfolio.
The annual Gartner Business Intelligence and Analytics Summit highlighted that “Self-service analytics is
white hot and growing while demand for traditional dashboard BI is in remission”. But the successful
implementation of self-service BI has proved difficult. Gartner’s survey on the effectiveness of Self
Service BI highlighted that 90% of self-service BI projects are projected to fail due to inconsistencies [9].
What is worth noting is that self-service BI should not be about giving business users BI tools and access
to BI data and letting them ”get on with it”. For self-service BI to succeed organisations must understand
the different considerations of each business user group and have the approved and supported
architecture and tools portfolio needed for each group to work more autonomously.
Howson.C (2013) highlights four main Self-service BI considerations
The use case
Sophistication of users
Degree of IT involvement
15. Breadth of data sources [10]
Differences in users’ technical awareness and ability can effect what they are trying to accomplish and
without carefully considering the four main points above their self-service BI deployments may not be
as successful as initially hoped.
To put Figure 10 into context an understanding who accesses data and at what level needs to be
explained. Information consumers constitute 90% of the BI community in a normal business and they
need to track perhaps 20% of the data available. Power users total maybe 10% of the community but
will require up to 80% of the data. It highlights the differences in technical skills needed at the various
ends of self-service BI spectrum and can relate back to the four main self-service BI considerations. It
also highlights the centralisation and de-centralisation BI models and the type of reports and analysis
associated with each type of user group.
Figure 10 http://exia.ca/en/self-service-bi/
Current self-service BI implementation tries to strike a balance between centralised and de-centralised
models but suffers from inconsistencies in terms of data definitions and measures across an
organisation. Could this be an indicator as to why Gartner [9] predicts 90% of self-service BI projects are
projected to fail due to these inconsistencies?
Self-service BI is still a relatively new trend and has its champions. The idea of offering BI tools, and in
particular VDD tools, that business users can then use to explore and analyse their data and ask tough
business questions with, without the need for a centralized BI model, is indeed an interesting
proposition.
Tableau
With the BI industry growing at an enormous rate and to be worth around 20.1 Billion by 2018 from the
13.9 Billion it was worth in 2013 this has made more companies try and get into this market. Tableau
shook the market in 2013 when it over took the traditional VDD providers such SAP, Oracle, SAS, and
Microsoft. It became a publicly traded company in May of 2013 and revenues have almost doubled since
2013 from 232 Million to 432 Million this has in effect enabled them to invest more in research and
16. development. Where 29% of profits are invested into research and development this is one of the
highest in the industry and has given them the competitive edge and the ability to innovate at a faster
rate.
Tableau formed as a company in 2003 from a project called Polaris from which the language wVizQL was
developed this combined query, analysis and visualization and was developed by PHD students at
Stanford University which tableau still runs on today. The first consumer release of tableau was in 2005
when Tableau 1.0 was released, the piece of software caught the attention of the industry because it
allowed users without a high technical skill to use it, but with the severe lack of data connectivity made
it no real threat to existing VDD software companies, although this changed in later revisions of the
software which made them more of a viable threat.
Tableau enables users to create visualisations from one or more data sources so that the viewer can
interpret the information with ease. Unlike BI software that can draw graphs it can create multiple types
of visualisations and helps the user by adhering to best practice for visualisations with only visualisation
that have the correct data used to be created.
Compared to the traditional BI software where only highly technical, highly skilled users could perform
analytics on the business data compared to tableaus approach of BI for everyone with Tableau’s
tremendously powerful and with its easy to use intuitive drag and drop interface and advanced analytic
capabilities has empowered all users of any skill level to perform data analysis and visual data discovery,
this enables the software to meet its mission statement “Tableau helps people see and understand their
data” (Tableau, 2015).Tableau has also been a great choice for large organisations as it is a scalable tool
in both hardware and memory. Backed with its security permissions it can ensure the CIA of the data by
restricting users, projects and dashboard from those who haven’t got the privileges to see them this,
combined with their vast user forum and stellar customer services has pushed them from being
mentioned in 2010 as challenger until 2013 onwards where they have been the leader in VDD tools in
the Gartner magic quadrant.
2010 2015
Figure 11
17. Using Tableau
Tableau can be used to discover knowledge through visualising data in many different ways. Three
different examples of this have been highlighted to show how Tableau encourages user interaction with
data.
Using Tableaus forecasting feature can quickly show users predicted future trends (figure 12). In the
example below each university campus has the average pass rate for each academic year and what is
predicted for the following two years after. The forecasting feature has a number of different options
that allows the customisation of the forecast models, forecast accuracy testing and the ability to amend
the forecast length.
Figure 12
Tableau uses line graphs which can be used to show results of analysis and allow users to apply filters to
drill down into specific areas of the dataset (figure 13). The example below highlights the average pass
rate per academic year per campus. The filters encourage users to interact with the data directly by
drilling down to a specific year or campus. The application of filters in figure 13 shows the data set
drilled down to three specific academic years and two different campuses.
18. Figure 13
Figure 14
The final example (figure 15) highlights Tableaus ability to show geographical locations and the results of
the data analyzed within each location using the map feature. Tableau calculates the latitude and
longitude of each location then presents the locations in a highly visual way. The main mapping features
available in Tableau allow maps to be layered, allows import custom geocoding, and editing of locations.
By applying dimensions to the Pages shelf, and showing quick filters, Tableau can control the display of
the analysis results. This again encourages user interaction with the data.
20. Evaluating Tableau
Tableau was assessed using a System Usability Scale (SUS) which highlighted that each member of the
group had encountered a similar experience when using tableau with the majority of the group giving it
a SUS score above the average of 68. After the extensive use of the software a meeting was held where
some of the pros and cons of using Tableau for Visual Data Discovery were discussed and an agreement
was reached on what the strengths and weaknesses of tableau are and the finding will be discussed
below.
The intuitive and easy to use drag and drop interface was agreed as a major strength of the software
with feel been that this works well and made the software non-threating to non-technical users. This
interface made it very easy to use key features such as Drilling down, Slice and Dice without prior
knowledge of SQL or Business Intelligence as well as making it easy to make dashboard to increase the
impact of the analytics to the viewer. One feature which was supposed to compliment dashboards was
storyboarding but the feeling was that this feature was lacking and did not add any more benefits to the
software. The only issue the group had with the user interface was that on a project with many
worksheets tableau would become cluttered and difficult to navigate as you had to scroll left and right
along the bottom of the screen which made it quite difficult to locate a specific sheet that maybe
required.
One of the main issues the group had with the software was the lack of collaboration features built to
the software, When multiple members of the group were wanting to work on tableau at the same we
had to use separate documents which slowed us down as we had to combine the changes we all made
at a later date. Tableau had plenty of security features for enterprise environments and had the ability
to change a wide range of user permissions in the software meaning that the one project could have
users from different departments and depending on the level of access they have would allow them to
see different worksheets, dashboards or Storyboard
One area tableau excelled in was the ease of connectivity this was very easy to use and the vast amount
of data sources although unlike some other VDD providers we could not manipulate the data in the
software which made us have to go back and edit the data source via other software. Tableaus ability for
aggregations was not as intuitive as the interface as when trying to use more advanced calculations it
became hard to code tableau due to tableau having its own language and not using a standard such SQL.
Overall Tableau is a very impressive piece of software, the ability to create advanced visualisations
quickly and with relative ease meant that even users without a background in Business intelligence
could find tends and key pieces of information to improve their business.
Below are the results of the Systems Usability Scale (SUS) that was used to evaluate Tableau. Each group
member undertook the evaluation independently from the other group members. The names of the
group members have been removed from SUS.
21.
22.
23. Dashboards
This report answers fifteen questions relating to the data set used. The questions have been answered
and positioned on the relevant dashboards. To properly analyse the data set the questions are not
answered in order, i.e. 1 to 15, but in what is perceived as the "natural flow" of the questioning. It was
agreed this would be a more realistic way to present the visualised data.
Appendix 2 shows
The questions to be answered given the data set.
24. What dashboard the question is in
What work sheet answers the question.
Snapshots of each dashboard are shown below
25.
26.
27. References
1. https://support.powerbi.com/knowledgebase/articles/471664-getting-started-with-power-bi-
designer
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Appendix 2 – Questions & Related Dashboards
Use Tableau to examine the data and attempt to discover answers to the following questions through
data visualization
1) How has the pass rate for this module changed over the past 6 years?
See Cross Campus Dashboard - Pass Rate Monitoring
2) How has the number of students taking this module changed over the past 6 years?
See Cross Campus Dashboard - Student Number Monitoring
3) The UWS introduced a new attendance monitoring processes starting in academic year 2011/12 – Did
this initiative affect the pass rate?
See Pass Rate Initiatives Dashboard - Attendance Monitoring
4) UWS encouraged the use of early engagement assessments as an indicator of student engagement in
academic year 2012/13. For Database Applications this was the introduction of a multiple choice exam
in Week 5 – Did this initiative affect the pass rate?
See Pass Rate Initiatives Dashboard - Early Engagement Assessment
5) In 2013/14 the UWS introduced a rule that if a student scored zero for any assessed component of
the module, an automatic re-take (i.e. RA or RA2 code) would be assigned to the student. This means
that the student is withdrawn from the current session for the module and should re-register for next
year’s running of the module - Did this rule change affect the pass rate?
36. See Pass Rate Initiatives Dashboard - Introduction of RA2 rule.
6) UWS introduced a categorisation of the A grade to reveal A3 (>=70%), A2 (>=80%) and A3 (>=90%) -
Did this initiative affect the numbers achieving an A grade?
See Pass Rate Initiatives Dashboard - A Grade categorisation results
7) In 2014/15 the multiple choice exam was integrated into the exam mark and not the coursework
mark as in previous years – Did this change affect the pass rate?
See Pass Rate Initiatives Dashboard - Multiple choice exam
8) Does the spread of marks/grades (e.g. for coursework, the exam and the final mark) for this module
follow normal distribution? If not, why?
See Spread of Grades Dashboard -
9) The UWS aims for equality of experience across all campuses of the university. Do the results for this
module support this notion?
See Lecturer and Campus Analysis Dashboard - Equality of Experience per Campus -
10) For 2015/16, students have registered to take the DA module and there are currently 18 students
(Ayr campus), 126 students (Paisley campus), 11 students (Dumfries campus) and 15 students (Hamilton
campus). Can you predict the future for this module in terms of the numbers taking and passing this
module and the likely number of students achieving each grade at each campus based on past trends?
See Forecasting Analysis Dashboard
11) Is there any trends or patterns that emerge over the years based on student sitting the coursework
and exam over the years ?
See Module and Grade Analysis- Students Results Pattern
12)What is the forecast of pass rate % per lecturer ?
See Lecture & Campus Analysis Dashboard - Pass Rate per lecture including Forecast
13) What is the spread grades between majors ?
See Module and Grade Analysis - Spread of Grades Between Major
14)How the number of student achieving proposal codes have changed over the years ?
See Module and Grade Analysis- Proposal Code Analysis)
15) How many students took the course per Major for each year ?
See Module and Grade Analysis -Student Numbers Per Major
