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Problem
85% of big data projects fail
Putting Machine Learning models into production is one of the most difficult tasks in data science because it involves managing large quantities of information and stitching together multiple services that require different specialization. It is one of the main reasons why 85% of big data projects fail.
How do companies like Starbucks use customer information to help make decisions?
How Starbucks did it before Datagran
- Using 5 different tools
- Had difficulty dealing with scaling, scheduling, and building APIs
- 1 month to try to have a churn model that is in the hands of the people that will activate it like Marketing
Who benefits from solving this problem?
- Businesses:
Because it lets them make better-informed decisions faster using their data. - Data scientists:
Because it helps them deploy complex models in minutes. - Small businesses and students:
Because it gives them access to data tools that were way outside their reach before.
Solution
Collect & organize data, build machine learning models, & automate workflows
—without engineering
Datagran automatically runs your data model on info, and moves the output between your business apps effortlessly—so you can focus on what’s important.
How Starbucks changed their process with Datagran
End-to-end workflows that increased the speed to production without the need for big data teams.
What can we do for businesses
- Democratize data
- Increase speed to production
- Build ML pipelines fast, without engineering
- Build flexible visualizations in one place
- Create a dashboard for every internal “client”
- Easy collaboration boosts goal accomplishment
Product
Build workflows &
put them into production
ORGANIZE YOUR
DATA PROJECTS
Create data projects around
teams and give access to the
people who need it when
they need it. Avoid having to
download and upload your
data every time you have a
new team member or project
goal.
CONNECT YOUR
SOURCES
We integrate seamlessly
with an extensive suite of
data sources, guaranteeing
enterprise-grade security,
and best-in-class customer
success.
CREATE A HOLISTIC VIEW OF YOUR DATA WITH BOARDS. COLLABORATE WITH NOTES.
SEE REAL TIME DATA
WITH A FLEXIBLE BI
Create SQL queries or
select filters to visualize
your data in a table, pie or
bar chart. Save your
visualization as a block to
have a 360 degree view in
boards.
RUN PIPELINES
Aggregate, clean, deduplicate,
visualize, create triggers, run
algorithms and take action
with our multiple operators.
Datagran allows you to build
simple queries or run complex
machine-learning algorithms
over specific data sets without
writing a line of code.
SEND THE OUTPUT
TO BUSINESS
APPLICATIONS
Easily send the output of your
models to the applications you
use every day without having
to spend time building APIs.
Why large and small companies are using Datagran
Traction
27% growth in the last quarter
56% PY growth in Q1 of 2022. 
*Graph shows actual revenue
up until Q1 2022 which includes bookings.
Customers
Customer insights
Remi Denoyer
Data Scientist at "Plato"
Datagran makes it super easy to deploy an ML model, and since I am the only Data Scientist in the company, it helps me to keep focused.
Matt Martin
Dir. of Business Analytics at "GoDaddy"
Datagran's product is so timely. Our unit has a ton of models but there's just too many pain points to operationalize them.
Chris Sanborn
COO at "HFactor Water"
We spent 3 months trying to set up an analytics platform that could integrate from multiple data sources. Datagran did it in under 15 minutes.
Santiago Diaz
Growth performance manager at "Foody"
It's amazing the amount, and type of projects our teams can achieve using Datagran.
Javier Garcia
"Tierra y Armonia"
Our team left the old marketing way behind. Now we're doing it the Datagran way.
Other clients we're already helping
- Discovery
- Telefónica
- Subway
- Starbucks
- Domino's
- Crepes & Waffles
Business model
Bottom-up strategy
- Free forever tier. Customers can sign up for free to test and try the product. As they get more value, they will need to upgrade to paid tiers.
- Pricing based on usage. There are 3 main variables that affect pricing: number of data rows, users, and machine time.
- Two main sales dynamics: a) Self-serve, and b) Sales. Our bottom-up strategy begins with a client trying the product. Then, if we identify that the client should be enterprise, we contact that user to hand-hold them through onboarding. Our minimum enterprise pricing is $1,500 per month, which includes a set of usage limits.
Market
The ML economy is just getting started
We aim to become the default platform for students and Small and Medium businesses, which has been a market overlooked by all other players.
Global AI market size is projected to reach $266B by 2027.
Source: https://www.fortunebusinessinsights.com/industry-reports/artificial-intelligence-market-100114
Competition
Datagran's technology is patent-pending
Competitors in this field include DataRobot, Alteryx, Azure, Amazon, among others.
Currently, almost all tools in the market tackle the modeling side. Datagran focuses on Operations of the Data—what is known as DataOps or MLOps.
The market indicates that most companies will want to go into our space, but our IP is protected.
There are currently no companies that actually do end-to-end ML workflows. We like calling ourselves the zapier.com for Machine Learning. Zapier enabled developers to easily send data from one app to another. We do the same, but with the ML layer on top.
Vision and strategy
Improving and expanding
We will improve our features and expand our user base
Features
We will be adding more data sources and destinations. We will add AutoML features as well as additional capabilities to give users flexibility. For example, we will soon release a feature for Data Scientists to reduce model Drift in production—something that is very well ahead of current market solutions. Finally we will aim to become a platform where developers can create their own elements to deploy end-to-end workflows that are personalized for every need.
Expand
We will expand our sales team to acquire more enterprise users. At the same time, by maturing the product and investing in SEO and events, we expect to increase our self-serve offering.
Funding
Datagran has raised
$4.5M+ since 2017
Datagran has been through 2 rounds of funding. We’ve raised more than 4.5M from investors including Telefonica, Quake Capital, Beresford Partners, and C-level executives from Uber and Bain & Company.
Founders
Carlos Méndez
CEO
Necati Demir
CTO
About CEO Carlos Méndez
I am a believer in AI Augmentation: technology built to empower rather than replace human ingenuity.
DataGran helps companies automate ML blazingly fast.
We started the business in March 2017 and since we have helped more than 3,000 companies worldwide. Including companies like Starbucks, AB InBev, Discovery Channel, KIA, Subway, and Rappi.
I started my career in the Advertising industry at Leo Burnett Chicago and as a Planning Director at JWT Colombia. After finishing my MBA I founded Gran Comunicaciones, one of the top 20 digital and web development companies in South America.
After spending over a decade in the Ad industry and experiencing first hand the challenges & opportunities for marketers and brands in a fast paced digital world, and pursuing my Master’s in Software Engineering at Harvard, I decided to combine my professional experience, my educational background, and passion for tech to build the solution that would allow collaboration between OPs and Business Units.
About CTO Necati Demir
I've been a software developer for more than 15 years. I hold a BS and Masters in Computer Science and a PHD in Machine Learning.
