Tackle the common challenges facing today's organizations in building a corporate knowledge repository-- from the volume of data captured both internally and externally. We will outline best practices for acquiring and preparing data in such a way that the people using that data have the confidence that it is reliable, secure, and easily accessible.

1. BIG DATA ANALYTICS
designing the knowledge repository
Tracey Moon | Naresh Agarwal

2. Tracey Moon, CMO
twitter - @ tmoonlive
linkedIn - linkedin.com/in/traceymoon
email - tracey.moon@brillio.com
Naresh Agarwal
Head of Information Management & Big Data
twitter - @ naresh2204
linkedIn - linkedin.com/in/naresha
email - naresh.agarwal@brillio.com
Today’s Brillio Panel
@BrillioGlobal

3. The platform
is the enabler,
and the
experimentation
mindset is the
methodology
to become
the data driven
company
Setting the Context
KEY COMPONENTS FOR BECOMING DATA DRIVEN COMPANY
- THE PLATFORM a.k.a. KNOWLEDGE REPOSITORY
- THE EXPERIMENTATION MINDSET
Remember
- its the mindset
- not all experiments will yield positive ROI
- make it real
- once proven, make it scale

4. In this session, you will learn
Designing THE PLATFORM “the enabler” aka ‘Knowledge Repository’
Key considerations for each layer
Reference Architecture for the knowledge repository

5. 4 Delivery
3 Discovery
2 Management
1 Foundation
The scalable technology infrastructure for
storing and acquiring your data
KNOWLEDGE REPOSITORY: Conceptual view
The tagged knowledge repository for all your data
The environment for analysis, modeling,
testing and running experiments
The interface for delivering insight to
operational systems or visualization tools

6. 1 Foundation
KNOWLEDGE REPOSITORY: Foundation
Self provisioning and resilient storage
and processing infrastructure
Ingest bulk as well as streaming data.
Ability to tag metadata
data sources |
structured unstructured streaming
metadata
Infrastructure |
scalable self provisioning monitored
secure
Design the
foundation to
process data at
storage instead
of moving it

7. 2 Management
KNOWLEDGE REPOSITORY: Management
data performance layer |
data marts OLAP cubes semantic layer
enterprise knowledge layer
data warehouse master data store
staging layer |
data lake
Metadata
is the key
difference
between a
good and
bad data
platform
Abstracts the data ingestion rate from data preparation
Abstracts the atomic data from business process
Facilitates business view representation in data

8. 3 Discovery
KNOWLEDGE REPOSITORY: Discovery
modelling |
machine learning streaming analytics
data mining
sandbox environment
analytical discovery rapid experimentation
Easy provision new sandbox
environment per user requirements
Support both interactive and
customer analytics tool

9. 4 Delivery
KNOWLEDGE REPOSITORY: Delivery
services |
visualizations business intelligence
dashboards app interfaces
Tools for human insights as well as customizable
app interfaces

10. 4 Delivery
3 Discovery
2 Management
1 Foundation
services | visualizations dashboards app interfaces
Modeling | machine leaning streaming analytics data mining
sandbox environment | analytical discovery | rapid experimentation
data performance layer | data marts OLAP cubes semantic layer enterprise
knowledge layer | data warehouse master data store
staging layer | data lake
data sources | structured unstructured streaming metadata
Infrastructure | scalable self provisioning monitored secure
KNOWLEDGE REPOSITORY: Reference Architecture
OPERATIONAL SYSTEMS, VISUALIZATION TOOLS
ANALYSIS, MODELING, TESTING
TAGGED REPOSITORY FOR ALL DATA
SCALABLE TECHNOLOGY INFRASTRUCTURE

11. Questions?
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