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Managing explosion of data


Customer experience expectations are converging on the brand, not channel

• Consistent across all channels and lines of business

• Contextualized to present location and circumstances

• Personalized to reflect preferences and aspirations

• Relevant in the moment to their needs and expectations


The new pathway is much more fragmented and dynamic … These varied shopping channels open up thousands of potential new purchasing pathways, more than five times the number available in the brick-and-mortar world.


Key business challenges

Personalization expected Dynamic markets Digital landscape


Customers across industries

HealthcareRetail

Telecommunications Public sector

…Financial services


✓ Breaking down data silos

✓ Sharing data in accordance with

privacy and regulatory policies

✓ Becoming iterative, lean and

leveraging knowledge across the

business

Creating true Customer 360


The challenge: managing customer data

Data can come from a variety of “siloed” sources

• Massive volumes of customer interaction data

• Some data from 3rd party data sources

• Can come in real-time or batches

• Generated from a variety of data sources

• Diverse data structures and schemas

• Some of it may be perishable

Combining customer data with contextual data

(social media, 3rd party) is the key to customer

insight


Key technology limitations

Data silos Data volume

New data sources Cost of processing


Traditional data flow diagram

Location

Social

Clickstream

ETL / stored procedures

Enterprise Data Warehouse

Segmentation & churn

analysis

BI tools

Marketing offers

Data Marts / Aggregations

Billing /

ordering

CRM/ profile

Marketing

campaignsOther / new data

sources – mobile,

sensors, apps, network

logs, files

Does not model

easily into traditional

database schema

Limited

processing

power

Limited

processing

power

Storage scaling very

expensive. Not

designed for ELT

Loss in Fidelity

Manual work. Few

automated system feeds.

Based on sample

/ limited data

Workflow

• Extract data from source

• ETL / aggregate source data for common schema

• Load aggregated data into EDW models

• Serve modeled data via BI tools and reports

10

“Oil doesn’t generate more oil […] the product of data will generate more data”

Piero Scaruffi


The data-driven enterprise

Explosion of data and devices

(IoT)

30Bconnected

devices

440x more data

Transformation of IT infrastructure

open source

cloud

machine learning

$200Btotal

market1

1 IDC Worldwide Big Data and Business Analytics Market Through 2020


We believe data can make what is impossible

today, possible tomorrow


We empower people to transform complex data into clear and actionable insights

DRIVE CUSTOMER INSIGHTS

CONNECTPRODUCT & SERVICES (IoT)

PROTECTBUSINESS


We deliverthe modern platform for machine learning and

analytics optimized for the cloud

ENTERPRISE GRADE

SecurePerformantCompliant

SCALABLE

ElasticCost-effectiveLower TCO

RUNS ANYWHERE

CloudMulti-cloudOn-premises


On-demand Big Data analytics for business users

.

Fast

For business

Easy

To manage

Secure

Without

Compromise

Make Big Data

Accessible

Deliver Big Data

In Context

Keep Big Data

Relevant

Faster and higher Big Data ROI


We empower people to transform complex data

into clear and actionable insights

DRIVE CUSTOMER INSIGHTS

CONNECTPRODUCT & SERVICES

(IoT)

PROTECTBUSINESS

In the next 5 years, 42%

of CMO’s say analytics

will be a core competency

in marketing

53% of CMOs have

increased pressure to

deliver revenue growth

this year


Solution areas for customer insight

Acquire & retain Cross sell &

upsell

Improve customer

experience

• Churn analysis

• Marketing spend

analysis

• Customer lifetime

value

• Next best offer

• Smart promotions

• Basket analysis

• Omni-channel

optimization

• Sentiment analysis

• Customer care

analytics


Joint customer examples


Consolidate your data sources to build a true 360


Customer insight – the data value chain

Data sourcesData ingest &

storageData processing Machine learning Analytics / BI

• Diverse data sources

• 3rd party data

• Batch/ real-time

• Diverse schema/

formats

• Real-time data ingest

• Big-data platform

• Unlimited storage

• Low cost / TB

• In-memory processing

• Batch vs. real-time

• Data enrichment

• Data contextualization

• Machine learning

libraries

• Unsupervised M/L

• Supervised M/L

• Iterative data modeling

• Data visualization

• Integration to BI

tools

• SQL queries

• Search


Qlik + Cloudera

11 Points of integration

Fast & flexible BI & analytics Go beyond SQL Enterprise ready

App on demand w/ Impala(In memory user generated data slices

)

Direct query w/ Impala(Data stored in Parquet or Kudu)

Complex data types w/

Impala(Maps, arrays, and structures )

Writeback with Kudu(Interactive analytics)

IOT and Kafka integration(Event driven / streaming analytics)

Solr integration (In-memory apps built on Solr data)

Qlik Solr-API app on demand(Search + QAP + D3js)

Advanced analytics demo(Integration with Spark/Python/R)

Cloudera metrics dashboard(REST API based management console for CM)

Security – New SSO support(Kerberos delegation / SSO pass-

through)

Data lake browser(Data Concierge for Cloudera)


http://cloudera.qlik.com/


All the data is there Proven platform duo Leverage and refine

No time like the present


Visual analytics that enables everyone to analyze

and explore information, generate insight and

make better decisions

A robust, flexible foundation that can adapt to a

changing infrastructure landscape

+Accelerating Big Data ROI


Thank you

managing explosion of data - qlikgo.qlik.com/rs/497-bmk-910/images/vyw - managing... · (data...

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