Growth hacking in the age of Data

Growth Hacking in
the Age of Data
www. f l y d a t a . c om
Presented by:

Daniel Saito
Founder of RedRobot K.K., a boutique tech and creative
agency based in Tokyo. Held several roles in technology.
@saitodaniel
daniel@redrobot.jp
http://redrobot.jp
http://www.slideshare.net/saitodaniel
Director, Infrastructure SW & HW Engineering (Broadcast) Senior Security Engineer (R&D) Representative Director
Executive Account Manager Executive Account Manager Investor Investor
Copyright © 2014 FlyData Inc. All rights reserved.
Trademarks belong to their respective owners.
VP of Sales & Marketing

Data
At it’s heart, a single datum
is a value stored at a
specific location.

Definitions of Data

Rise of the Age of Data

Examples of where
Data is being used

Data effecting our everyday lives
- Data derived from calculating
compound interest rates by
comparing data from disparate
data sources.
- MySQL is installed on farming
tractors. Farmers can view in
real-time yield and weight.
- METADATA analysis on
telecommunication service
transactions.
- Smart data cross analysis on
identifying and financing the
next box office hit.

What is required to
work with Data?

You and big Data
Big Data is comprised of smaller bits of data from disparate data
sources.
Data is everywhere, whether if you are pulling server logs to
accessing your database in the cloud.

What is Growth
Hacking through
Data?

The Role: Life as a Data scientist
 Your next marketing VP or CIO will understand Data science
(Datalogy).
 The ability to find and interpret rich Data
sources, and manage large amounts of Data.
 Provide in-house Statistical consulting.
 Automate Data-driven processes.
 Develop Predictive Models
 Provide Useful Visuals and Summaries for Executive Management.
 Use Data to Improve Products
 Present Interesting Results to External Audiences
 According the HBR, it’s the sexiest job of the 21st Century.

Data Discovery: Finding your Data
 Big Data is comprised of smaller bits of data from disparate
data sources
 Data is everywhere, whether if you are pulling server logs
to accessing the cloud.

Data Ingestion: Extracting Data
Upon identifying usable data, the next task is to
extract it from the data source in its RAW
format.

Compute: All Data is NOT =
 All DATA is NOT equal, as we pull data from disparate sources.
 Data is required to be computed and reformatted.
 Best practice is to have your data as real time; as possible.
 After Data is computed and reformatted, it can be sent to a central
repository; either on-premise or on the cloud accessible 24/7.

Data Load: Moving Data onto the Cloud
 Sections of Data are placed into tables and then aggregated into
columnar database format.
 Aggregates are computed over large numbers of similar data
items.
 Error handling and Error Management needs to be properly
implemented. Handling Data is pretty hard.

Visualize: Presenting Your Data
 Data needs to be analyzed through the use of meta-analysis by
contrasting and combining results from different data sources.
 Querying and merging ‘smart data’ together for visualization in
human readable format.
 Using existing 3rd party tools to visualize your data.

Data visualized for humans
Gaming user behavior
analysis
Data from your browsing history
Wind Patterns

Breaking down the whole Data process
DISCOVERY
DATA INGESTION
COMPUTE DATA STORAGE VISUALIZING DATA

FlyData Automates Data Integration

FlyData Features
 FlyData Agent (on Customer’s on-premises
or cloud) + FlyData Cloud (SaaS
or in Customer’s VPC)
 Near-Real Time and Continuous Data
Integration
 Security / Data Integrity / Scalable / Error
Handling = Reduce much costs
 Once you setup, No Hassle

Real-Time Analytics for
 CASE
 Client is a leading mobile gaming company in Japan with multiple released game
titles
 Previously large amount of data was stored MySQL cluster
 MySQL often went down because of the large amount of data. Repair took weeks
of man-hours every time this happened.
 Historical analysis over multiple years was simply impossible, given the data size.
 SOLUTION
 Implemented FlyData Enterprise with JSON logs across multiple titles
 Outputs user activity by application into JSON log files
 Data is automatically fed to Amazon Redshift
 RESULT
 Engineering time is saved and real-time BI insights can be fed back to application
development cycle
 Client saves 2 weeks of man-hours every month, with added insight into user
behavior. As a result, the client continues to steadily grow its user base and its
bottom line.

Data Analytics at
 CASE
 Client is a online advertisement startup in the US with Display Ads shown across multiple websites
 User activity from the duration of engagement to the position of the cursor is all logged to measure viewer
engagement
 Client needs to save large amounts of data, and be able to query that data real-time. This data will then be used
to generate Ad Performance Reports.
 Their initial option Hadoop turned out to be too costly in terms of Engineering time. The learning curve for the
team was steep, for both query generation and maintenance of their Hadoop clusters
 SOLUTION
 Implemented FlyData Enterprise using “Extended” Apache logs
 Outputs all user activity in Apache logs with additional information appended, such as key-value pair information
for URL parameters and custom variables
 Data is automatically fed to Amazon Redshift in the appropriate columns. When appropriate columns do not exist,
the columns are added on the fly. This allows for added flexibility in table schema design
 Customer can now know the real-time effectiveness of their online advertisements through Ad Performance
Reports
 The client’s internal BI team can quickly analyze which ads are working and which are not,
in real-time and can gain insight or optimize for the best performing ads
 RESULT
 With a more cost-effective solution than Hadoop, client was able to increase revenue by steadily increasing the
quality of ads based on data gathered by FlyData and analyzed in Amazon Redshift.
 Client has an implemented scalable backend reporting system that can handle multi-TB sized ad campaigns.

Faster Feedback & Development Cycles
 CASE
 Client is a digital media startup in the US that has a website with rapid growth in user access,
becoming one of the most “Like”d pages on Facebook with more than 10 million likes.
 User activity logs are carefully analyzed and assessed both for the website content and for the
user experience
 Used log data to perform funnel analysis on customer conversion rates
 Client received user activity from its site as JSON objects, before storing it in MongoDB
 Given the nature of the queries they wanted to run, MongoDB became very slow as their user
base grew
 SOLUTION
 Implemented FlyData Enterprise using nested JSON logs
 Outputs all user activity as a JSON log file
 FlyData automatically uploads the data into Redshift, so BI team (= App Development team)
can simply query their user activity logs
 Client now can quickly perform funnel analysis on customer data
 RESULT
 Query speed dramatically improved. Queries that took 20 minutes before, now take less than a
minute, while still being able to have the flexibility of JSON.
 Faster development cycles (Build-Measure-Learn cycles) were achieved.

Contact Information
sales-jp@flydata.com
http://flydata.com
@flydatajp
www. f l y d a t a . c om
We are an official data integration
partner of Amazon Redshift

Growth hacking in the age of Data

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Growth hacking in the age of Data

Similar to Growth hacking in the age of Data (20)

Recently uploaded

Recently uploaded (20)

Growth hacking in the age of Data