So You Need a Marketing Dashboard—Now What? by BECKON

HOW TO CHOOSE THE RIGHT PARTNER
FOR DATA MANAGEMENT AND REPORTING
SO YOU NEED A MARKETING
DASHBOARD—NOW WHAT?

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WWW.BECKON.COM HELLO@BECKON.COM
CONTENTS
INTRODUCTION 3
A COOL, REFRESHING ANALOGY 4
MAKING DATA SAFE FOR MARKETERS 5
PARTNERS FOR DATA MANAGEMENT AND REPORTING 8
CHOOSING THE RIGHT PARTNER 13
40 QUESTIONS TO ASK PROSPECTIVE PARTNERS 15

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INTRODUCTION
It’s time—you’ve decided you need a marketing dashboard. You want to be
able to see all your omnichannel data in one place. You want to track how
well you’re hitting your targets. You want your entire team to have access to
a repository of marketing intelligence so they can make decisions based on
the data. You want an easy way to communicate how well you’re performing
against marketing and business goals.
You’re not alone. As marketers race to boost their data analytics capabilities,
they’re making dashboards and scorecards—tools for data-based marketing
management—a top priority. It’s the right instinct and very timely. But getting
insight from raw marketing data takes much more than just booting up some
data visualization software. To understand why, let’s begin with an analogy that
offers illuminating parallels: the public water system.
69%
Sixty-nine percent of marketers anticipate stepping
up their use of data-driven marketing over the next
three years.
Source: Forbes Insights

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A COOL, REFRESHING ANALOGY
Clearly, installing a new faucet isn’t what makes the water flow. Delivering
water fit for human use depends on a series of steps that happen long before
you turn on the tap. We can break it down into five stages.
HOW DRINKING WATER GETS TO YOUR TAP
1. COLLECTION. First we have to gather up all the water in its natural state.
At this stage, we’re taking the water in whatever form it comes—dirty,
muddy and potentially full of pollutants.
2. FILTRATION AND DISINFECTION. Using various straining techniques, we
filter and cleanse the water.
3. AUGMENTATION. We then add things to our water that enhance its value
for human consumption—fluoride, for instance.
4. STORAGE. Next, we set aside the clean, safe water in a repository, ready
to flow.
5. DISPENSATION. Finally, having done all this preparatory work, we can
pipe the water directly to end users. They can do the dishes, take a
shower, wash the dog—any task that requires clean, safe water.
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1. COLLECTION
Capture all the water
we can. Muddy,
dirty, things floating
in it—just take it all.
2. FILTRATION AND
DISINFECTION
Get rid of all the junk.
3. AUGMENTATION
Add fluoride and
other enhancements.
4. STORAGE
Put the clean, safe
water in a repository,
ready to flow.
5. DISPENSATION
Pipe the water into
faucets and nozzles
on-demand.

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MAKING DATA SAFE FOR MARKETERS
Turning raw marketing data into true marketing insight follows a process very
similar to how drinking water gets to our taps. Here, though, we have six stages
instead of five.
HOW MARKETING INTELLIGENCE GETS TO
YOUR TEAM
1. Data intake. This is the collection stage, where we gather up all our raw
data in its natural form. Like the first stage of water management, the
data we gather is “dirty”. Nothing goes together. It’s a hodgepodge
of likes, clicks and TV gross ratings points. It’s spend, survey and sales
data. Some data is in rows. Some is in columns. And it’s scattered across
dozens of tabs. The campaign names and IDs are all different. Some stuff
is measured in days and other stuff in weeks or quarters. And it’s in five
different currencies.
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1. DATA INTAKE
Capture all the data we
can. Scattered, messy, in
different formats— just
take it all.
2. DATA CLEANSING
AND NORMALIZATION
Filter out all the junk and make
the data consistent.
3. DATA ENRICHMENT
Add tagging and other
enhancements.
4. DATA STORAGE
Put the clean, safe
data in a repository,
ready to flow.
5. REPORT DESIGN
Decide the marketing
questions dashboards
and scorecards
should answer.
6. DATA VISUALIZATION
Pipe the data into
dashboards and scorecards
on-demand.
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2. Data cleansing and normalization. Next we filter the junk out of our raw
data as well as make it all consistent. This includes de-duping, applying
uniform naming conventions and campaign IDs, and normalization of the
data so that disparate units (days vs. weeks, dollars vs. euros, ZIP codes
vs. DMAs) are coherent.
3. Data enrichment. Similar to adding fluoride to water, we can enrich and
transform our data to make it more useful. For example, tagging metrics
as awareness, engagement or outcome lets us readily assess the health of
the purchase funnel.
4. Data storage. After we’ve cleansed, normalized and enriched our data, we
store it in a robust and secure repository as an on-demand asset.
5. Report design. This stage doesn’t have a precise parallel in our water
analogy, but think of it as being where we choose our faucet design.
However, instead of choosing between chrome or nickel finish and a wide
or narrow spray, we ask ourselves what marketing questions we want our
dashboards and scorecards to answer.
6. Data visualization. Now we turn on the tap and clean, treated data flows
into our marketing dashboards and scorecards. It’s the last stage of
the process, where end users can log in, explore datasets and see the
charts that deliver the real-time marketing intelligence needed to guide
decisions. How is the campaign performing versus plan? Let’s create a
line chart showing plan versus actual by week. Which content should we
put the most paid dollars against? Let’s stack-rank all our content pieces
by engagement rate. What are the most cost-effective tactics to employ?
Let’s render a table that lists cost per lead by marketing channel.
And dashboards offer more than just decision support for marketing teams.
They’re a simple and elegant way to report marketing performance to the
extended team and across the wider organization.
We can see that these processes fall into two broad categories. The first four
represent data management, which takes place behind the scenes. The last two
represent data visualization and reporting, which is up front and visible. If we
want dashboards (the data visualization layer) to give us intelligence we can
trust and defend, then we must have good data management practices in place.

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NAIL DATA MANAGEMENT TO GET
MEANINGFUL DATA VIZ
Now that we have a deeper understanding of data management and data
visualization—and what it takes to achieve both—we can turn to the obvious
next question: What do the various marketing dashboard partners actually do?
STAGE PROCESS DESCRIPTION
DATAMANAGEMENT
1 DATA INTAKE
Bring all marketing’s data sources
together in one place.
2
DATA
CLEANSING AND
NORMALIZATION
Filter out the junk—get rid of
duplicate metrics and irrelevant data.
Align disparate data by applying
consistent naming conventions and a
marketing-specific taxonomy.
3
DATA
ENRICHMENT
Apply enhanced metadata, or
tagging, to further categorize data
for analysis.
4 DATA STORAGE
Add the cleaned, normalized,
enriched data to a secure repository
as an on-demand asset.
DATA
VISUALIZATION
ANDREPORTING
5 REPORT DESIGN
Determine which marketing
questions dashboards and scorecards
must answer.
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DATA
VISUALIZATION
Render charts, dashboards and
reports to share with the team and
the wider organization.
Just one in eight organizations have a current
capability to work with big data or unstructured data
in the context of their integrated marketing efforts.
Source: eConsultancy

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PARTNERS FOR DATA MANAGEMENT
AND REPORTING
Dashboard partners generally fall into one of four categories, based on their
functionality and scope.
SPREADSHEETS
EXAMPLES: MICROSOFT EXCEL, GOOGLE DOCS
It’s a little odd to call a spreadsheet a partner, but we start here because
despite all the technological advances over the past 10 years, simple,
straightforward Excel is still the most commonly used data analysis and
reporting tool.
PROS: Spreadsheets are inexpensive, ubiquitous and have a number of basic
formulas and chart types already built in. Designed for ultimate flexibility, they
allow marketers to slice and dice data for custom views. So, many marketers
limp along with spreadsheets as a default solution.
CONS: Spreadsheets are terrible data-storage mechanisms. Across all channels,
marketing has a lot of spend and performance data. Spreadsheet files, used for
storage, get large fast. And every spreadsheet user knows that when they get
big, they get brittle—they’re slow to open, formulas and macros start to break,
they can’t be emailed, and truly giant spreadsheets can actually crash people’s
laptops. Not what you want in a storage system.
Spreadsheets are also pretty awful collaboration tools. If everyone is feeding
data into a spreadsheet, there are sure to be saving errors, version control
issues and all the rest. And while the formulas and macros may have been
perfectly clear to the person who created them a couple years ago, for
somebody new stepping into that spreadsheet it’s like trying to decipher some
bizarre code.
For all these reasons, spreadsheets are a poor choice for creating a trusted
source of data storage, reporting and collaboration.
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GENERAL DATA VISUALIZATION
AND EXPLORATION TOOLS
EXAMPLES: DOMO, TABLEAU
As the name implies, general data visualization tools only address the final
stage of turning raw data into intelligence: data visualization and reporting.
Think of them as the faucets.
PROS: Data visualization tools enable analysts to explore structured datasets
in interesting ways via great visuals. They’re designed for maximum flexibility
in order to meet the basic needs of any number of departments, from HR to
operations. For enterprises with centralized analytics functions that serve the
entire organization, data viz tools are often the partner of choice.
CONS: Data viz partners assume that we’re starting with a perfectly
aggregated, cleaned, tagged, structured database in neat rows and columns.
For marketers—who typically work with messy, disparate datasets—using a
data viz partner means having to resource and manage what these tools don’t
cover: data intake, cleansing, normalization, enrichment and storage. If we
don’t take care of these data management pieces up front, we’ve got a “junk in,
junk out” situation.
Recently, some data viz partners have added basic ETL toolkits that allow
a marketing team’s in-house analysts and data engineers to clean up raw
data before visualization. (ETL stands for extract, transform and load—it’s
a standard IT term for moving data from one system to another while
transforming it in the process.) But to take advantage of these ETL toolkits,
we’d need to have said analysts and data engineers at our disposal—a rarity for
marketing departments.
Data visualization tools aren’t designed for data storage. They aren’t
attempting to create a “source of truth” for data. Instead, they typically
visualize “snapshots” of data—any number of people can upload any file and
render an image based on the data it contains. If the numbers conflict across
datasets, the data viz tool doesn’t notice (or care).
Lastly, because data viz tools are designed to serve the widest possible
audience, they don’t have marketing-specific KPIs and reports built in.

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FULL-SERVICE BUSINESS
INTELLIGENCE (BI) PLATFORMS
EXAMPLES: QLIK, BUSINESSOBJECTS, MICROSTRATEGY
These are platforms designed to take you on an end-to-end journey from raw
data to business intelligence.
PROS: BI platforms provide a toolkit for performing the full gamut of data
services, from intake, ETL and storage all the way through to visualization
and reporting.
CONS: BI platforms are designed for database architects and IT resources to
use—technical specialists with advanced data manipulation skills. Implementing
the ETL toolkits and data modeling functionality involves the use of scripting
languages. In short, BI platforms are heavy duty, and require heavy-duty skills
to operate.
Because BI platforms are designed for IT—and IT is a centralized function in
most companies—they have no marketing-specific features. For marketers to
get value out of a BI platform, we’d need to tell IT how to build a marketing
data framework from the ground up, and then provide detailed requirements
for what we’d like to see in reports.
Through 2016, less than 10 percent of self-service
business intelligence initiatives will be governed
sufficiently to prevent inconsistencies that adversely
affect the business.
Source: Gartner
In other words, more than 90 percent of companies out
there haven’t done the data cleanup required to use BI
correctly—and as a result they’re making decisions that
HURT the business.
10%

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MARKETING INTELLIGENCE (MI) SOLUTIONS
EXAMPLE: BECKON
Marketing intelligence partners match BI tools in scope—providing all aspects
of data management as well as data visualization and reporting—but are
designed specifically to serve marketing’s unique needs.
PROS: MI platforms are preconfigured to handle and clean up marketing’s
data sources. By relieving marketers of the burdens of data intake, cleansing,
normalization, storage and enhancement, MI systems deliver very fast time to
value.
Unlike full-service BI solutions, which require advanced technical teams to
configure and manage them, MI platforms are built for marketers to use
themselves. They offer easy ways to get data in, templates for visualization and
reporting based on marketing best practices, and intuitive interfaces for asking
ad hoc questions of data to gain specific insight. What’s more, they can ingest
and display campaign calendars, media plans, creative briefs, images, videos
and other types of content critical for telling the story of marketing’s impact
on the business.
Although MI platforms are marketing-specific, the data can serve more than
the marketing org. Good MI partners provide APIs and full data portability,
allowing marketing data to be merged with other datasets or piped into other
analytics tools for cross-functional analysis and reporting.
CONS: In the same way that Salesforce is designed for sales and Workday is
designed for HR, MI platforms are purpose-built for the marketing function—
they aren’t designed to deliver performance insights for other departments.

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CHOOSING THE RIGHT PARTNER
Armed with all the information above, you’re ready to figure out the
type of dashboarding partner that works best for you. Start by considering
these questions.
1. What is the current state of your data? Do you already have a system
in place for taking in, cleaning, normalizing, enriching, and storing your
data? A single “source of truth” for all the data that matters? Or are you
starting at square one?
2. How strong is your in-house data management capability? Do you have
the database engineers, analysts and data scientists needed to build out
your data model from scratch and use the advanced ETL functionality
certain partners require?
3. Are your intelligence needs marketing-specific? Who are your end users?
A team of marketers who will use the platform themselves to make
insightful optimization decisions? Or an advanced analytics team that
provides reporting to marketers?
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This handy decision tree maps it all out.
DECISION TREE FOR CHOOSING A MARKETING
DASHBOARD PARTNER
STARTHERE
GOOD DATA
GOVERNANCE?
Do you have a clean,
well-structured
dataset—a single
“source of truth”?
MARKETING-
SPECIFIC NEED?
Are your end users
marketers? Do you
want access to
reports and analytics
based on marketing
best practices?
IN-HOUSE DATA
MANAGEMENT
CAPABILITY?
Do you have a team
of database engineers
and analysts able to
use ETL toolkits, build
out data schema and
blend datasets?
MARKETING-
SPECIFIC NEED?
Are your end users
marketers? Do you
want access to
reports and analytics
based on marketing
best practices?
YES
NO
YES
NO
YES
NO
NO
YES
GENERAL DATA
VISUALIZATION
PARTNER
Examples: Domo,
Tableau
MARKETING
INTELLIGENCE (MI)
PARTNER
Example: Beckon
FULL-SERVICE BI
PARTNER
Examples: Qlik,
BusinessObjects

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TO SUMMARIZE, YOU SHOULD CHOOSE:
• A general data visualization partner if you have great data management
processes in place and no need for marketing-specific analytics or reporting.
• A full-service BI partner if you have a messy data situation but a strong
in-house data team who can take care of the data management functions
AND you have no specific marketing need on the data visualization and
reporting side.
• A marketing intelligence partner if you need data cleanup, want marketing-
specific analytics, dashboards and scorecards, and/or have marketers as
end users.
We can now revisit our table of data processes for a visual of what each
partner supports out of the box—and, from a marketing perspective, the gaps
you’d have to fill with other tools or teams.
PICK A PARTNER WHO OFFERS THE CAPABILITIES
YOU NEED OUT OF THE BOX
STAGE PROCESS
SPREADSHEET
(Excel, Google docs)
DATA VIZ
(Domo, Tableau)
BUSINESS
INTELLIGENCE
(BI) (Qlik,
BusinessObjects)
MARKETING
INTELLIGENCE
(MI) (Beckon)
DATAMANAGEMENT
1 DATA INTAKE

Structured
data only
Structured
data only 
2
DATA
CLEANSING AND
NORMALIZATION
 
You, using their
ETL toolkit 
3
DATA
ENRICHMENT  
You, using their
ETL toolkit 
4 DATA STORAGE
   
DATA
VISUALIZATION
ANDREPORTING
5 REPORT DESIGN
   
6
DATA
VISUALIZATION    

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40 QUESTIONS TO ASK
PROSPECTIVE PARTNERS
As you shop for a marketing dashboard, use this list of questions to vet
individual vendors. By discussing these points with your candidates in detail,
you’ll end up with a complete picture of what’s required from both parties to
get your new dashboards launched.
ASK:
1. Can you take all the data we have, or do you have a menu of APIs we
need to choose from? (If they only offer API access, be sure all the tools
you use have APIs available, otherwise you will have blind spots.)
2. How do we get data into the system? Can you pull from APIs? Can we
publish to a secure server, which you then pull down? Can you take
spreadsheets? .CSVs? Can our team email files straight into your system?
3. Do we need to upload data in neat rows and columns, fully tagged?
STAGE 1: DATA INTAKE
Give the vendor a list of all your data sources—
online, offline, business outcomes (sales), revenue
data, and brand/equity outcomes (brand tracker
or survey data).
Key objective: Determine whether there are
any restrictions on the types of data that can be
entered into the system, and how the uploading
process is accomplished.
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4. How do we know if data is in or not? Is there a confirmation process?
5. What metrics do you recommend we pull in from each of our priority data
sources and channels? Can you provide us with guidance?
6. What if a data source has a value that we don’t want to pull in? Can we
pull in some data but not other data from a single data source? How does
that work?
ASK:
7. What is our role in fundamental data cleanup? What is your role?
8. Who determines what marketing metrics and KPIs to pull in? Do we need
to define our complete and final data schema from scratch when we get
started with your tool, or do you have a starting point for us?
9. Can you transform messy Excel files into a structured format?
10. Who is responsible for de-duping, normalizing and reconciling variations
in field names, etc., before we start looking at dashboards?
11. Can you make metadata consistent? For example, if we have varying tags
such as “Southern California”, “So. Cal.” and “So_Calif” how does your
system know these are the same thing?
12. If you don’t make metadata consistent for us, do you have ETL tools
that allow our team of data architects and analysts to perform data
transformations within your application?
STAGE 2: DATA CLEANSING
AND NORMALIZATION
Key objective: Determine who is responsible for data
cleanup and normalization, and precisely what each
responsible party needs to do.
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13. If multiple datasets contain the same metric name but different values,
how do you determine the source of truth for the metric?
14. How do you ensure that metrics have common definitions and are well
understood by all users who later access the dashboards?
15. Do you maintain a glossary of all metric definitions and properties so that
metrics always mean the same thing?
16. When someone is viewing a chart, will each metric’s definition and
properties be readily available to users?
ASK:
17. Do you offer strategic advice on best practices for using metadata to
enrich our dataset?
18. How can we create aggregates of data—for example, tag all paid media
mentions as “paid” for future paid and earned media analyses? How do
you support adding metadata to underlying metrics?
19. How are formulas and derived metrics supported in your platform? Can
we create formulas (e.g., run rate, average, rate) based on underlying
metrics and KPIs in the application? Or do we need to calculate those
metrics before they go into your system?
STAGE 3: DATA ENRICHMENT
Key objective: Determine who is responsible for
data enrichment, and the specific responsibilities
each party has in the process.
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ASK:
20. Do you warehouse the data—the structured files that we upload into your
system—or do you just allow us to visualize “snapshots” of our data?
21. What happens if people upload different and conflicting datasets?
22. Can we easily export all (or subsets of) historical data from your system
in order to merge it with other datasets or run analyses outside your
platform?
23. Do you have an API so that we can pull data out of your tool?
ASK:
24. Do you offer standard reports that reflect marketing best practices? And/
or suggestions for analyses to run based on best practices?
25. Do you offer templatized reports based on marketing best practices that
we can customize?
26. How user-friendly is your chart creator/querying tool? Is it designed to be
used by marketers or advanced analysts?
STAGE 4: DATA STORAGE
Key objective: Determine how accessible your data is
and who maintains ownership and control over it.
STAGE 5: REPORT DESIGN
Key objective: Determine the reporting capabilities,
customization and training the vendor offers.

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27. Do you offer flexible permissions and roles for who can see select data in
reports?
28. Will we have an account manager who provides guidance on dashboard
and scorecard design and trains all our users? Does our account
representative have a marketing background?
29. Is training and support included in the price or does it cost extra?
ASK:
30. Are your data visualization and exploration tools designed for IT
professionals, data analysts or marketers?
31. Are there many chart types available? Does the system suggest chart
types based on the selected data?
32. Can we visualize omnichannel datasets—data that comes from many
disparate sources?
33. Will our dashboards have the ability to show us what’s happening across
the buyer’s journey in terms of awareness, engagement, purchase,
advocacy, etc.?
34. Do you offer drill-in and drill-down capabilities?
35. When looking at a dashboard, can we easily slice the dataset in a different
way or otherwise creatively explore our data, or are we limited to viewing
canned reports?
36. Are dashboards shareable, and are there varying levels of access we can
grant?
37. Can we view our dashboards on web and mobile?
STAGE 6: DATA VISUALIZATION
Key objective: Determine how flexible the vendor’s
chart options are, along with their solutions for
sharing, drilling down and exporting.

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38. Are we able to easily export dashboard charts and graphs into
PowerPoint for other types of reporting and analysis?
39. Do you support other visuals such as scorecards?
40. Do you offer easy benchmarking such as “vs. previous period”, “vs. last
year” and “vs. a target or goal”?

ABOUT BECKON
To grow your brand, you need integrated, unbiased data and insights you
can trust. You need Beckon, The Source of Truth for Marketing™. Beckon’s
rock-solid data management and real-time marketing intelligence power
better, faster decisions that let you do more with every marketing dollar.
LET’S TALK
Want to learn more? Get in touch at hello@beckon.com—we’d love
to connect.

So You Need a Marketing Dashboard—Now What? by BECKON

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