With the expertise of our CEO, we've put together a webinar about MVP readiness. If you're low on time, budget, and resources, build a lean solution. A minimum viable product has enough design and development to launch within a shorter time frame. Not only do you save time and money, you'll be able to make iterations and versions post-launch. See how to prepare for an MVP with Ali Allage, the CEO of Boost Labs. For more about MVPs, contact us!

1. Meeting Agenda
Webinar: MVP Readiness
05.16.19
Who We Are
Core Services
Why Boost Labs?
Case Study
Case Study Assets
Case Study Finished Product

2. 2
Who We Are
Since 2009, we’ve helped industry disruptors monetize data by creating unique
data analytic software products. Over the years, our continued partnerships with
extraordinary customers have led us to endless successful product development
that produces real return on investment (ROI), implements compliance, and more.
We have a growing team of incredible data specialists, developers, and information
designers who know how to extract key insights from big data to create valuable
data analytic products inline with business objectives. Data is the fuel to our
product development, and we are able to offer full stack product creation services
from raw data analytics to product launch.

3. 3
Core Services
Our company focuses on creating end-to-end revenue generating data analytics
products for our customers. This full stack service offering tackles everything from
data ingestion, management & infrastructure to data analytics product development
& deployment.

4. 4
About Me
● As the CEO of Boost Labs, I participated in creating a strong track record of
success in working with organizations (small to large) and their product needs
(ex. TrueCar, Vanguard, Census, etc).
● Worked with many different types of expertise under one focus of data
analytics product creation (data, technology, design. and business).
● Our organization is a full stack service provider, which means I have
experience starting with raw data to product launch.

5. 5
Agenda
● Objective
● Data Talk
● Product Vision & Validation
● The Build
● Case Study
● Q/A

6. 6
Objective
The objective of this webinar is to provide a basic guidance on how to best
approach your next Data Analytics MVP (Minimal Viable Product) project. These
basics won’t address all possible challenges, as it is just meant to be a starting
point. If you have a specific challenge not addressed in this webinar, please free to
reach out to me directly.

7. 7
Data Talk
● Access & Permission challenges – Do you have access to all the data needed
for the MVP? What data services are available for the MVP to use for access?
● Structure/Format – What format are the data sources? Will it require additional
work to properly structure the data in order for it to be useable?
● Data Cleanliness – How clean is the data? Are you working with an accurate
data set?
● Data Accuracy & Ingestion – Is the data correct? How does new data get
added?

8. 8
Data Talk (continued)
● Data Security & Privacy – Is there a security policy around the data? What sort
of privacy measures need to be in place?
● Insights – Is there a visualization platform in place? What sort of insights are
being pulled from the data?

9. 9
Product Vision & Validation
● Idea / vision understood and vetted. Is the idea and vision approved by
internal stakeholders?
● Defined requirements. Are there functional, non-functional, technology,
business, and/or compliance requirements being thought thru?
● Target demographic identified. Is there a clear understanding of who this
vision is focusing on?
● User Interviews & User Personas. Are there identified use case scenarios?

10. 10
Product Vision & Validation (continued)
● User Flows / Flow Charts. How will the user flow thru the product? Were flow
diagrams created to show process within the product?
● Wireframes / Mockups / Clickable Prototypes / Proof of Concept. What tools
are being used to bring the idea into reality with visual examples?
● Branding or Style Guidelines. Is there a document defining logo usage, brand
colors, typography, etc.?
● Data is ready and complete?
● Data is accessible ?

11. 11
The Build
● Budget defined. Has budget been allocated and approved by internal
stakeholders?
● Compliance and/or security requirements understood. Are there any internal
security restrictions?
● App development resources identified and rallied. What resources are
available internally? What will need to be outsourced?
● The skill sets needed for the build have been identified and the individuals are
gathered. Is the team new to working with each other? Is there any overlap of
skills amongst the group? How experienced is everyone?

12. 12
The Build (continued…)
● Approved timeline and related milestones. Is there a sprint schedule or project
plan laid out? Are all of the key stories or requirements included within the
project plan?
● Internal goals have been set with an approved timeline. Are all of the key
stakeholders aware of the project and know when they need to participate
within the project timeline?

13. 13
Case Study - Consumer Behavioral Application
Our challenge was to create an application that
allowed users to navigate through hierarchical data
while at the same time making complex boolean
rules for campaign creation. Data included in the
project was focused on Internet user activity and
survey related data.
We created, what we called, a“Rainbow Matrix
Sphere Grid” design to help solve our client’s
challenge. Users can select multiple items for a
campaign, visualize relationships between all
items, see the population that the campaign can
target, and create interlinking boolean rules.

14. 14
Case Study - Project Goals
● Architect new data taxonomy to support the application
● Setup a new data distribution environment with dictionary to support the
application
● Design and develop a custom interface with visualization to help support
end-user workflows
● Guidance in technical solutions for business analysis, data warehousing, and
data analysis

15. 15
Case Study - End Result
● Data Analytics Product - Created a data analytics product that allows end
users to build advertising campaigns on audiences that share common
interests.
● Data Environment - Worked with the client on the design and construction
of a subscription database that distributes data to the applications.

16. 16
Case Study - Return on Investment
● New application allows the end user to gain more intelligence through an
improved interface/visualization
● Scalable solution that reduces the cost of feature enhancements through
version rollouts
● Increased revenues with subscriptions and custom report generation

17. 17
Case Study - Project Assets

18. 18
Case Study - Project Assets

19. 19
Case Study - Project Assets

20. 20
Case Study - Project Assets

21. 21
Case Study - Finished Product

22. 22
Case Study - Finished Product

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Questions ?