Birst Webinar Slides: "Build vs. Buy - Making the Right Choice for a Great Data Product"

Build vs. Buy: Making the Right Choice
for a Great Data Product

2
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Presenter
ksmith@birst.com
@kevinmsmith
kevinmichaelsmith
Kevin Smith
VP, Embedded Solutions
ksmith@birst.com

‹#›
What is a data product?

‹#›
A story of building analytics gone wrong

‹#›
Our mission
Make our
existing
SaaS
application
more
engaging by
adding
analytics

‹#›
We have smart
Engineers…
Let’s build it
ourselves and
save some
money!

‹#›
We had resources.
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me!
I’m an
analytics
user story.
Please
implement
me! + +

‹#›
We had a vision.
$ $
$ $ $

‹#›
Why was this so painful?

‹#›
The Truth
about Buy
vs. Build for
Embedded
Analytics

‹#›
The BI bar has
been raised
1

‹#›
Time has changed the analytics game
It’s on
the
web?
NICE!
1990
It’s only
30 days
old?
NICE!
1995
I can sort
by
column
headers?
NICE!
2000
A chart?
In color?
NICE!
2005
Real-time
data?
NICE!
2010
I can’t drag this
chart to a new
location, apply
filters and have it
notify me when it
exceeds the
targets I
uploaded?  
FAIL.
2015

‹#›
Because table stakes & delighters aren’t
static
Table Stakes
• Expected
• Can’t compete here
• Your competition
has them
• Can’t charge for this
• Increases over time
Delighters
• Unexpected
• The place to
compete
• Useful for
differentiation
• Can charge
• Transition to table
stakes over time

‹#›
Delighters become table stakes
Table Stakes
• Nice looking visuals
• Drill down
• Filter
• Dimensions
Delighters
• Personal settings
• Customize
• Notifications
• Trends
• Targets
• Predictive
• Annotations

‹#›
It will take longer
& cost more than
you expected
2

‹#›
We can build it for less!
• Pay for Highcharts

• Cost to build ETL

• Cost to build SSO

• Cost to build pages
Build

it
year 1 year 2 year 3
• Possibly buy
more storage

• Possibly buy
more bandwidth
maybe $150K? +20K?
• Possibly buy
more storage

• Possibly buy
more bandwidth
+20K?
Our cost to build = $190,000
over next 3 years

‹#›
Buy

it
Buying is expensive!
$250K
• Pay platform fee

• Pay for
implementation

• Pay for training
• Pay platform fee• Pay platform fee
$100K $100K
Our cost to buy = $350,000 over
next 3 years

‹#›
Our cost to buy = millions and
millions over an infinite timeframe
Buy

it
Buying is, like, SUPER expensive!
$250K

• Pay for
implementation

$100K $100K
infinity
$100K
times infinity

‹#›
• Pay for Highcharts

• Cost to build ETL

• Cost to build SSO

• Cost to build pages
Build

it
Buy

it
• Possibly buy
more storage

• Possibly buy
more bandwidth
• Possibly buy
more storage

• Possibly buy
more bandwidth

• Pay for
implementation

$190K  
(but
probably
even less)
Inﬁnite
money
Clearly, we should build it!

‹#›
What we all think we need to do…
Buy charting package
Build ETL
Build Charts
Build Dashboards
Connect via SSO

‹#›
In reality, there’s a bit more.
Buy charting package
Build data load
Build Charts
Build Dashboards
Theming
Aggregate data
Build roll-ups
User permissioning
Admin pages
Multi-tenancy
Connect via SSO Filters
DimensionsTarget setting
Target setting
Transformations
UI controls
Drill down
Drill Across
QA

‹#›
$
What are you
skipping in order
to build?
3

‹#›
Cost to build
analytics
Cost to
support
analytics
Cost of NOT
working on your
core
application
The cost of missed core product
value

‹#›
Your most talented people should
work on unsolved problems.
50% of companies base their decision to build on the fact that they
have the necessary talent to build analytics 
From Wayne Eckerson, “Embedded BI: Putting Reporting and Analysis Everywhere”, TechTarget, December, 2014.

‹#›
Where can we add the most
differentiating value?
Ask
Core Product Analytical Platform
• Do we have all the features we
need to solve the customers’
needs?
• Could we build features that
differentiate us from the
competition?
• Could we build functionality that
would be hard to copy?
• Is analytics where we want to
compete?
• Do we need to build the
infrastructure in order to
achieve this?
• Can we build BI functionality
that is differentiating?

‹#›
Can you build what you need down the road?
Category Types of Analytics Questions Answered
Prescriptive
• Optimization

• Randomized testing
• What’s the best that can happen?

• What happens if we try this?
Predictive
• Predictive modeling/forecasting

• Statistical modeling
• What will happen next?

• What is making this happen?
Diagnostic
• Data exploration

• Intuitive visuals
• Why did this happen?

• What insights can I gain?
Descriptive
• Alerts

• Query/drill-down

• Ad hoc reports/scorecards

• Standard reports
• What actions are needed?

• What is the problem?

• How many, often, where?

• What happened?
SOURCE:
Disambiguating Analytics, July 2, 2013, Sanjeev Kumar,
International Institute for Analytics
SOURCE:
Magic Quadrant for Business Intelligence and Analytics Platforms, February 5, 2013,
Analyst(s): Kurt Schlegel, Rita L. Sallam, Daniel Yuen, Joao Tapadinhas
Capability
Easy (er) to build
Hard to build
Much harder to build
YOU have to build this

‹#›
It’s hard to build
fast enough to
diﬀerentiate
4

‹#›
Two ways to compete on analytics
Diﬀerentiate
(we’re the leaders!)
Neutralize
(we’ve got BI too!)
Core Value Key Metric Main Challenge
Separation Unmatchable How far?
Comparability Good enough How fast?
Framework adapted from Reaching Escape Velocity, Geoffrey Moore, 2012

‹#›
Diﬀerentiate
Neutralize
Can you build fast
enough to
differentiate?

‹#›
Build fast enough outrun the
competition… 
and STAY ahead

‹#›
Diﬀerentiate
Neutralize
Are you willing to cede
your development
roadmap to the
competition?

‹#›
The risk: your competition dictates your pace

‹#›
You need to
make a balanced
decision
5

‹#›
It’s an equation, not a single number
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
Cost Side Strategy Side

‹#›
It’s an equation, not a single number
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?

‹#›
1 The cost to buy embedded analytics
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?

‹#›
2 The real cost to build
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?

‹#›
Capture all of the true costs
Task Type Task Title Description
Licensing Buy the software to make the visuals
Purchase of the software to make the charts + maintenance & support for Hi Charts (10 developer
license) -- this ONLY includes production
ETL Build connector to data source Create processes which will connect the charting software to the data source(s)
ETL Perform transformations Transform the data into an analytic ready state for charting
Data Modeling Create data aggregations Perform the roll-ups of data so that you can compare to previous yrs , qtrs, etc.
UI Create dashboard page Create the page which will contain your analytics
QA Perform QA Inspect the analytics and all calculations for accuracy
UI Create dimensions Create the dimensions by which measurement can be examined
Data Modeling Create filters Create the filtering element to include/exclude data by dimension
Data Modeling Build drill-down/across paths Link analytics together so that users can drill down and across to explore causes
Security Build multi-tenancy model Develop model to ensure that customers can't see each other's data
Security Build security model Develop model to ensure that users see only the data they are allowed to see
Data Create data model for targets Build a model to store targets for the metrics
UI Build UI for target setting Create an interface to allow for the setting of targets by metric
UI Build UI for alerts Create the interface for setting alers and notifications for user self-service
Data Modeling Create visualizations Build the visualizations to display the data such as bar charts, line charts, infographics, etc.
Data Modeling Create reports
Build the pixel perfect reports that use the metrics and dimensions to display the data in a tabluar
format with rollups, sub-groups, totals, etc.
Administrative Build user mangement capabilities
Create the functionality that allow you to add and remove customers and companies from the
analytical functionality
Administrative Build monitoring
Develop the monitoring capabilities so that you can see the total usage by customer (for billing
purposes)

‹#›
And calculate both money & time
Variable Value
Hourly rate $150.00
# of data sources 2
# of visualizations 15
# of reports 2
# of metrics 30
# of dashboards 1
# Dimensions/metric 2
Task Type Task Title Description QuantityHours per Item Total Hours Total Cost for Task
Licensing
Buy the software to make
the visuals
Purchase of the software to make the charts +
maintenance & support for Hi Charts (10 developer
license) -- this ONLY includes production 1 n/a n/a $3,600.00
ETL
Build connector to data
source
Create processes which will connect the charting
software to the data source(s) 2 20 40 $6,000.00
ETL Perform transformations Transform the data into an analytic ready state for charting 30 10 300 $45,000.00
Data Modeling Create data aggregations
Perform the roll-ups of data so that you can compare to
previous yrs , qtrs, etc. 30 10 300 $45,000.00
UI Create dashboard page Create the page which will contain your analytics 1 20 20 $3,000.00
QA Perform QA Inspect the analytics and all calculations for accuracy 30 5 150 $22,500.00
UI Create dimensions
Create the dimensions by which measurement can be
examined 60 5 300 $45,000.00
Create the filtering element to include/exclude data by
The Powered by Birst Buy vs. Build Calculator
* not including the time to manage the project
$226,350
Building your dashboard in-house would cost
at least:
that's 1485 hours or 0.67 FTE years not
working on your core product
How much does it REALLY cost to build dashboards for your product on your own?
Your cost to build using these parameters
Our expected cost to build:

‹#›
3 Opportunity costs & risks of building
Total
cost to
buy
analytics
-
Total
cost to
build
analytics
≥
Opportunity
cost of
building
+
Risk of not
being able to
execute now &
future
What’s the
TCO for
purchasing
analytics
What’s the
real cost to
build
What aren’t we
doing if we build
and how
important is it?
Will we be able
keep up the
development pace
for the foreseeable
future?

‹#›
Four parts to this side of the equation
Can we build it FAST

enough?
What ELSE could we
build with the time?
Do we want to KEEP

building it?
Can we build it GOOD

enough?
1 2
3 4

‹#›
Can we build it FAST
enough?
• Do you have the resources to build it?
• Can you build it quickly enough to
meet demand?
• Can you build it fast enough to outpace
the competition?
1

‹#›
Can we build it GOOD
enough?
• Do we have the talent to build this?
• Can we get to the “delighter” functionality in
the near term?
• Will we be able to meet the “table stakes”?
• Do we know what our customers need?
2

‹#›
Do we want to KEEP
building it?
• Will we have the resource to continue to
support this?
• Will we have the resources to continue to
develop this?
• Will we be able to meet one-off requests and
future table stakes?
3

‹#›
What ELSE could we
build with the time?
• Is this as or more important than our core
functionality?
• Are we willing to delay core product
functionality to build (and maintain) analytics?
• Is this the best use of our resources - is this
why customers buy our product?
4

‹#›
Low Risk Medium High Risk
Can we build it fast
enough?
We’ve got a development team
dedicated to analytics, fully-
trained in the entire stack, and
can build quickly.
We have resources, but may
have trouble building quickly
enough to achieve table stakes.
We don’t have the resources/
don’t want to dedicate the
resources to build analytics.
Can we build it good
enough?
Yes — we can build all the
basics plus functionality to
differentiate ourselves from the
competition.
Maybe — we can add some
table stakes, not all. Maybe our
delighters will outweigh the
gaps in functionality.
Nope — we’d have trouble
getting to table stakes.
Do we want to keep
building?
Yes — this is where we will
compete so we’ll devote equal
resources to analytics develop
as our core app.
Maybe — we could add some
functionality over time but it
would secondary in importance
to the core app.
No — we’d prefer to use our
resources on other things.
Could we be doing
other things?
No — analytics are the app for
us. We consider this to be the
core of what we do.
Maybe — analytics are
important and our core app
roadmap is not full.
Yes — we can add more value
by working on our core
application.
The Buy vs. Build Decision Matrix

‹#›
Low Risk
(1 point)
Medium
(3 points)
High Risk
(5 points)
TOTAL
Can we build it fast
enough?
We’ve got a development
team dedicated to analytics,
fully-trained in the entire stack,
and can build quickly.
enough to achieve table
stakes.
We don’t have the resources/
don’t want to dedicate the
resources to build analytics. 3
Can we build it good
enough?
differentiate ourselves from the
competition.
table stakes, not all. Maybe
our delighters will outweigh
the gaps in functionality.
Nope — we’d have trouble
getting to table stakes.
3
Do we want to keep
building?
compete so we’ll devote equal
resources to analytics develop
as our core app.
would secondary in
importance to the core app.
No — we’d prefer to use our
resources on other things.
2
Could we be doing
other things?
No — analytics are the app for
us. We consider this to be the
core of what we do.
Yes — we can add more value
by working on our core
application. 2
GRAND TOTAL
(possible 20 points)
10 points

‹#›
Low
(1 point)
Medium
(3 points)
High
(5 points)
Our Rating Importance
(1=low to 3=high)
TOTAL
Can we build
it fast
enough?
We’ve got a development
team dedicated to analytics,
fully-trained in the entire
stack, and can build quickly.
enough to achieve table
stakes.
We don’t have the
resources/don’t want to
dedicate the resources to
build analytics.
5 2 10
Can we build
it good
enough?
differentiate ourselves from
the competition.
table stakes, not all. Maybe
our delighters will outweigh
the gaps in functionality.
Nope — we’d have
trouble getting to table
stakes. 5 3 15
Do we want
to keep
building?
compete so we’ll devote
equal resources to analytics
develop as our core app.
would secondary in
importance to the core app.
No — we’d prefer to use
our resources on other
things. 3 2 6
Could we be
doing other
things?
No — analytics are the app
for us. We consider this to
be the core of what we do.
Yes — we can add more
value by working on our
core application. 2 3 6
GRAND TOTAL
(possible 60 points)
37
points
x =

‹#›
The Buy vs. Build Decision Spectrum
Consider building your
own analytics
You likely will be able to
build fast enough and
keep building fast enough
to hold off the
competition
Consider buying your
analytics
It is unlikely you will get
to market fast enough or
be able to stay ahead of
your competition
Consider a combination
strategy
You may be able to build
fast enough and keep
building fast enough to
beat the competition in
select areas
Low Risk High Risk
The red zone
Medium Risk
0 - 20 points 21 - 40 points 41 - 60 points
The yellow zoneThe green zone

‹#›
Weigh the pros & cons to make the
decision that’s right for your situation
Cost Side
May save $53K
Strategy Side
Medium High risk
to build & keep
building

‹#›
In summary: don’t use “internal” criteria
4
Make a balanced decision
The BI bar has been raised
It will take longer & cost more than you expected
You can’t let up on the pace for your strategy
What are you skipping in order to build?3
5
2
1

‹#›
Get the e-book
at birst.com
©2015 Birst, IncBEYOND THE TECHNICAL
The complete guide to designing, pricing,
& launching embedded analytic products
BEYOND THE TECHNICAL
©2015 Birst, IncBEYOND THE TECHNICAL
BEYOND THE TECHNICAL

‹#›
Thank you!
ksmith@birst.com
@kevinmsmith
kevinmichaelsmith

Birst Webinar Slides: "Build vs. Buy - Making the Right Choice for a Great Data Product"

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