Audit and evaluation of Data Visualization services and practices for possible adoption by advertising agencies considering using open source, going with the SaaS route, adapting a platform, or building one in house.

1. Data Visualization
Enterprise Landscape 2016
adoption use cases for agencies
Image Source

2. agenda
• vendor landscape
1. Digital Marketing Oriented Platforms
2. Business and Analytics Applications
3. Specialized Software and White Glove Services
4. Agency Built Portals
• takeaways

3. digital marketing reporting platforms
• ExamplesExamples

4. digital marketing reporting platforms
Characteristics
Platforms built specifically for digital marketing data
• Typically have API connectors directly to major platforms such as Doubleclick, Sizmek,
Facebook Ads, Google Adwords, and other popular vendors that handles the ETL
relatively seamlessly for general reporting (eg. just campaign outputs and not
additional layers of modeling).
• Often has pre-built Dimensions and Measures from its ETL process to be easily drag
and dropped in building from a pre-defined HTML5 visuals
• Visuals tend to look much more sleek than other platforms and usually are built with
an HTML5 and Javascript engine
• Likely have created their own visual libraries using raw Javascript to customize to
their platform and data model
• Tends to be a three-part platform, one that takes care of data storage, data clean-up,
and visualizations/reporting
• Landscape increasingly commoditized and cluttered but not near consolidation

5. digital Marketing reporting platforms
• Usually built in HTML5 with very
attractive graphics and sleek interfaces
• Some are able to automatically populate
dimensions and measures from data, eg.
dates and clicks without work from end
user
• Reports are close to pre-built for standard
campaign reporting
• Have social and collaboration functions
• Most easy to use for rank-and-file
• Good for repetitive builds of the same
report
• Visual and dashboard customization are
lacking from a pure features perspective
(eg. hard to do client branding or
customizing visuals with additional data
pivots) and not good for creating visuals
that explore data
• Handing over data security and storage to
an outside company and won’t be “on
prem”
• Can be very costly
• Can need a lot of time with managed
service layer to customize before being
useful
Strengths Weaknesses

6. datorama example

7. analytics and business applications
• ExamplesExamples

8. analytics and business applications
Characteristics
Software, usually Desktop rather than Browser-based, for working with data and
performing analytical tasks that aren’t specialized toward a particular industry
• Typically are used across verticals and adapted for different purposes
• Able to do both exploratory and explanatory data visualization, which the other
platforms tend to not be able to do very well
• Purposes are for more than data visualization, data visualization tends to only be one
component or an important feature of the tool.
• Typically are not explicitly designed for visualizations or for use as a reporting
platform even though they are commonly used for those purposes
• Have ability to do analysis or integrate mathematical models either natively or run
external packages
• Common features are statistical outputs, eg. ANOVA window with outputs, ability
to do forecasting, correlations, regression, etc.
• Able to run packages written in R, Python, and possibly other scripting languages

9. analytics and business applications
• Able to connect to a wide
variety of databases natively
with strong security features
• Has a broad range of features
for analytics, such as statistical
modeling and able to do
exploratory data analysis
• Most customizations of
individual visuals if not entire
dashboards
• Most flexible in terms of scope
• Visuals can look more clunky and
dated since their build usually isn’t
browser-based
• Not specialized for any industry
• Can require more time to master.
Features can be overkill for smaller
agencies.
• Likely does not have strong
collaborative tools or capabilities
out-of-the-box compared to other
platforms
• Can be time-consuming to use
Strengths Weaknesses

10. powerbi example

11. white glove or specialized platforms
• ExamplesExamples

12. White glove and specialized software
Characteristics
Dashboards or visuals created for a very narrow purpose or scope (even more than the
digital marketing-oriented platforms). Tend to look the most stylish and specialized
• Have a narrow customer base and purpose, such as specializing in showing an output
for one client
• Reporting or visualization dashboards that only cover a few API connectors and
data types, but do them really well, eg. displaying Tweets or Google Analytics
outputs
• Usually shown at roadshows or at events
• Typically are less mature products
• Used by smaller or specialized agencies and businesses
• People love the way they look
• Likely don’t have an ETL layer and plug directly into APIs and datasets
• Tend to be a black box to some degree and not quite plug-and-play

13. White glove or specialized platforms
• Have the biggest wow factor and
work well for external
presentations
• Highly specialized for a particular
set datasets and handles
transformation and pre-populates
dimensions and measures, but not
as broadly as digital marketing
platforms
• Does a few things extremely well
and needs high-touch teams
• Can often be black boxes in terms
of workflow
• Usually single-purpose
• Products don’t have a very long
history or even shelf-live
necessarily
• Can take long to provision and
customize, making it unsuitable for
any broad purpose
• Lack of complex analytics
Strengths Weaknesses

14. Tickr example

15. agency built portals
Characteristics
Dashboards or reporting portals built from scratch within agencies to meet business
intelligence needs. Usually specialized to exactly what agency thinks is important
• Has exactly what agency wants
• Can be complementary to other agency products
• Looks like nothing else on the market and matches to each agency’s needs and
brandings
• Usually built with a mix of open source libraries and tools
• Sits on top of an agency data layer or connects to another data source
• Agency has complete control over the data
• Can sit on prem
• Can often not be as stable or scalable as other platforms due to lack staffing,
specialization, external support issues, and lack of prioritization and buy-in within
organizations

16. agency built portals
Strengths
• Tailored to each individual
agency’s needs
• Complete control of data, ETL,
and visualization/reporting layer
• Highest level of customization
possible
• Can fit well within other agency
offerings as an integrated
technology layer and experience
Weaknesses
• Software development capabilities
are agencies can sometimes not be
on par with capabilities at
enterprise companies specialized
only in deploying reporting
platforms and visualizations
• Often do not have enough internal
support to necessarily scale or be
maintained
• Costly to the organization to built
and manage

17. takeaways
• Splits in approach lie in business needs when deciding between standard
campaign reporting tools, business applications, specialized white glove
platforms, and in-house tools
• Each approach has significant pros and cons depending on needs or the
trade-offs an organization is willing to make
• Strengths and weaknesses tend to go hand-in-hand, increased
customization usually means less specification toward particular
applications and increased workload by rank-and-file but less
provisioning time to have a platform be actionable
• Eg. Anything other than a supported API connection for many of the platforms
would require custom work by a professional services group
• A main trade-off exists between ease-of-use and out-of-the-box visual
creation for using digital marketing platforms or white glove platforms
versus the work and data ownership needed to implement business
applications or in-house created agency tools

18. thank you
Image Source