accelerating insight - smart data lake customer success stories

©2017 Cambridge Semantics Inc. All rights reserved. Company Confidential

Accelerating InsightSmart Data Lake Customer Success Stories

Peter HorowitzPrincipal - AdvisoryPwC

Alok Prasad, PresidentBen Szekely, VP – Solution EngineeringCambridge Semantics

©2017 Cambridge Semantics Inc. All rights reserved.

ACCELERATING INSIGHT


Anzo Smart Data Lake: Accelerating InsightA Graph-based Platform to Solve Today’s Data and Analytics Challenges

Data Ingestion& Mapping

AutomatedETL Generation

CollaborativeMapping Text Processing

DataCataloging

Data & ModelGovernance

Active Metadata Management

Role-Based Security

Discovery & Analytics

Knowledge Discovery

Custom User Dashboards Self-Serve LiveExtracts

In-Memory MPP Query

Graph Marton Demand

ELT, Model BasedData Integration

Document Search

Anzo Smart Data LakeValue Proposition

Actionable Insights All

Data Sources

EnterpriseData Lakes

“Last Mile”Analytics

Allowing Enterprises to Rapidly Create Purpose-built Discovery & Analytics Solutions on the Cloud of Choice


Load and Map

any data……. from anywhere… at any time!

DataCataloging

Organize, Integrate, Share and Govern

any data assets.. more efficiently greater security

In-Memory MPP Query

Graph Exploration

Interconnected … trillions of facts… 100x the speed…


Ask & Answer

any questions… any combination. any user…..



Accelerated Insights – 100x faster than the nearest competitor



Enterprise Knowledge Graph


Sample Customers


Peter HorowitzPrincipal – Advisory Bankingand Capital ManagementPwC

Ben Szekely VP Solution EngineeringCambridge Semantics

Accelerating Insight – Customer Successs Story

Accelerating Insights: Using Smart Data Lakes Successfully

March 2017

PwC

Overview

$53 bn 1000%The Dilemma – Given current landscape within many financial service institutions, the standard data journey takes TOO LONG

Typical data journey

Winning Strategy - Attack every step in the process to reduce time to market.

8

Requirements Sourcing Quality

EvaluationQuality

RemediationAnalysis and

Reporting

8

PwC

Why is this so important?

Sources: Average OM% from Morningstar, Inc.; IAIG Index

For global top banks, comparing average operating margin percent for previous 5 years against index of information architecture, investment and governance demonstrates strong correlation.

Proper investment in information strategy, architecture and governance PAYS OFF!

Proper data architecture improving or in place.

Insufficient or lagging data architecture investment, execution, governance.

PwC

Old approach is Challenged

The existing landscape presents great challenges since it is based on relational databases and point-to-point mappings.

Operational Data Source


Staging DB Metadata

Aggregated Data

Detailed Data

Data Mart

Reporting

Analytics

Predictive modelingFiles

ETLData Mart

Data Mart

EDW

Integration Barriers

Restricted entry

Narrow payload

Data source mappingMulti-stage, brittle ETL

required to integrate diverse data sources

Standard model requiredEnforces a standard model, no

exceptions, leading to loss of valuable information context

Limited insightsRigidity undermines the agility required in data

analytics

Challenges

PwC

Original data and models maintainedData is loaded seamlessly without transformation or

mapping, irrespective of format

New approach

Unified model

Consolidated model unifies disparate source models into a

comprehensive and shared canonical model

The smart data lake provides a centralized data store with a flexible schema where data is first loaded and then transformed; a process known as schema-on-read.



Files

SmartData Lake

Model 1Model 2Model 3



Files

Richer insights

Cross region and domain view supports population-based analytics, hidden relationship discovery and reduces risk of siloed or inaccessible

Reporting

Analytics

Predictive modeling

Benefits

Graph Data Model(i.e. Ontology)

Graph store designs capture the nature of data relationships and supports a variety of data representations, known as “Shared Semantics”

PwC

Case Study: Next generation insider trading and fraud surveillance driven by structured and unstructured data


EvaluationQuality


Reporting

Accelerators:

Firm Wide Risk Dashboard

Employee Risk Dashboard

Analysis Overview• Ingested emails, IMs, web browser

logs, cookies, phone records, contacts, news feeds

• Ingested futures, options trades, market price data

Impact• Data was ingested and linked in

hours.• Profiles developed for employee

roles• Outlier behavior effortlessly

identified• Employees given risk ratings

allowing for prioritization of detailed compliance investigation.

PwC

Cast Study: Terabytes of data ingested and analyzed in a fraction of the time revealing complex relationships between clients and transactions

13

Analysis Overview• 5.5 billion transactions on behalf of

25 million customers for 35 million accounts.

• Based on reference data and public information, prepared “social” network of clients analyzing edges (i.e., connections) between them.

Impact• Data was ingested and linked in

hours.• Edges developed and weighted

based on Pointwise Mutual Information algorithms.

• Resulting graph database supported recursive analysis, which would have been dramatically longer using RDBS, allowing for identification of suspicious networks

Suspicious Account Bridge

Customer Network


EvaluationQuality


Reporting

Accelerators:


INDEFINITE

Drug Discovery Preclinical Product Development

FDA Review Scale-Up to Mfg.Post-MarketingSurveillance

ONE FDA-APPROVED

DRUG

0.5 – 2 YEARS6 – 7 YEARS3 – 6 YEARS

NUMBER OF VOLUNTEERS

PHASE 1 PHASE 2 PHASE 3

5250~ 5,000 – 10,000

COMPOUNDS

PRE-

DISC

OVE

RY

20–100 100–500 1,000–5,000

IND

SUBM

ITTE

D

NDA

/ BL

A S

UBM

ITTE

D

R & D Intelligence Data Lake (CI)

Clinical Trial Operations Data Lake

Product Development Data Lake (SDP)

Anzo Smart Data Lakes Across Pharma

Clinical Trial Exploratory Analytics Data Lake Source of Influence

Project Evolution Data Lake

PV & Safety Data Lake

Medical Advisory Board Analytics Data Lake

Clinical Data Standard Management

Voice of the CustomerData Lake

Real World Evidence Patient Data Lake


Anzo Smart Data Lake: Accelerating Insight

Disparate Sources

Insight

Exploratory AnalyticsKnowledge Discovery

Data on Demand

Automated Ingestion

Rich Models

Scalability

Security

Enterprise Knowledge Graph

Governance


Rules, Security, Governance

The Smart Patient Data Lake

Connectors Models Rules Analytics & Tools

Patient Data Systems

R&D

Patient Safety

Clinical Trial Ops

Health Economics

Patient Data Lake

DataOn

Demand

Auto

mat

ed In

gesti

onInsight for Decision

MakersImproving patient outcomes, safety,and comfort

Reducing the time bring medicines to patients

Lowering the cost of healthcare

Insurance Claims

Clinical Trials

Rx Data

Health Records

Genetic Data


IT B

uild

and

Dep

loym

ent

Anzo Smart Data Lake

Traditional BI

and Analytics

Tool Chains

Add

New

Dat

a

Add

New

Dat

a

Ad

d N

ew D

ata

A

dd N

ew D

ata

Disrupting the Time-to-Value Function

Tim

e an

d Re

sour

ce

Inve

stm

ents

Insights and Value

Traditional BI

and Analytics

Tool Chains

Anzo Smart Data Lake


Anzo Smart Data LakeA Graph-based Platform to Disrupt Today’s Data and Analytics Challenges

Connectors Models Rules Analytics & Tools

ASDL Customer Fingerprint - Intellectual Property



CollaborativeMapping

Text Processing

DataCataloging



Role-Based Security


Automated Query Generation

User Dashboards and Custom UI/UX

Self-Serve Live

Extracts

In-Memory MPP Query

Graphmartson Demand


Document Search

Actionable Insights

Enterprise Data Sources





Anzo Smart Data LakeSelect Capabilities

Actionable Insights All

Data Sources



Allowing Enterprises to Rapidly Create Purpose-built Discovery & Analytics Solutions on the Cloud of Choice




DataCataloging



Role-Based Security

In-Memory MPP Query

Graphmartson Demand


Document Search


Knowledge Discovery

Custom User Dashboards Self-Serve LiveExtracts


Click here to request a demo

http://info.cambridgesemantics.com/schedule-a-demo

accelerating insight - smart data lake customer success stories

Data & Analytics