A framework that discusses the various elements of Data Monetization framework that could be leveraged by organizations to improve their Information Management Journey.

1. DATA MONETIZATION
AN APPROACH
We believe an effective data monetization strategy necessitates enterprise to perceive data as an
ever evolving asset enabling creation of new information and insights.
30% of businesses will begin
directly or indirectly monetizing
their information assets via
bartering or selling
By Start of 2017…
80% of Chief Data Officers will
strive to maximize the value of
information and to minimize its
risks
Analytics will be delivered via API
Prescriptive analytics built on
cognitive computing, delivering
$60+ Billion in annual savings
By 2020…
FRAMEWORK - ELEMENTS
Explore Data – Unstructured &
Structured, both in terms of data sources
– Internal & External
Data Services
Industry Specific Insights
Cross Industry Insights
Horizontal Insights
Data Product
Internally generated data, customer or
client generated data, collaborative public,
privately acquired data
Data Classification
Differentiation
Cost to Value
Speed to Value
Value for Buyer
Understand Market Opportunity Identify Business Model
Data Privacy, Controls &
Regulations
Identify Key data Analytic Signals Data Delivery Focus
Partnerships, Alliance and
Network Effect
Enterprises are looking for innovative ways to take the data they have
generated and build revenue model around selling insights to
different third parties. We present a framework that will help
enterprises to transform the data asset into tangible revenue
While working within
confines of client’s data
platforms, we understand
the data structures, we can
apply this framework for
building new revenue
models through Insights
(and not just data)
Why Us?

2. DATA MONETIZATION
AN APPROACH
Implementing Data Monetization requires transformational approach for data sourcing,
transformation & delivery of Insights through APIs for Internal & External Business Groups
APACHE NIFI FOR REAL TIME DATA INGESTION
KAFKA FOR REAL TIME MESSAGING
APACHE SPARK FOR REAL TIME STREAMING
APACHE KYLIN FOR EXTREME OLAP – REAL TIME DATA CUBES
IN MEMORY ANALYTICS BY ALLUXIO, ROCKS DB, APACHE ARROW
INTRODUCE AGILITY IN DESIGN THROUGH MICROSERVICES
INSIGHT GENERATION THROUGH APACHE SYSTEM ML, GOOGLE TENSORFLOW,
MICROSOFT DMTK, AMAZON DSSTNE, SPARK ML
TAKE THE SET UP TO CLOUD – KEEP MODELS ON CLOUD, DATA ON PREMISE
TECHNICAL APPROACH THROUGH BIG DATA & ANALYTICS