Paradigm Shift of Data Analysis

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21 Φεβ 2014 (πριν από 3 χρόνια και 4 μήνες)

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©2013
Lavastorm

Analytics. All rights reserved.

1

MARKET UPDATE: Following the
Paradigm Shift
of
Data Analysis

©2013
Lavastorm

Analytics. All rights reserved.

2

Agenda

Changing Data Requirements: Big, Agile, Accurate

Transforming Data Analytics
from
Search
to
Discovery

Turning Data to Information Value

Creating
an
Analytics
-
driven Culture

Analytics for Non
-
technical Executives

New Sales Opportunities from Analytics

Q&A


©2013
Lavastorm

Analytics. All rights reserved.

3

Changing Data Requirements: Big Data

Relational Database Silos,

Structured Data, Data Warehouses

New Databases
& Sources

Enterprise Data

Outside The DW

Unstructured, Semi
-
structured Documents

Big Data Analytics

Unify Silos, More Data

Traditional Analytics

Third
-
Party Data

©2013
Lavastorm

Analytics. All rights reserved.

4

The Importance of Agile Business/IT Collaboration

Organizations that have achieved lasting benefits from
formal data quality improvement programs tend to take
a holistic approach involving
people
, repeatable
processes
, and appropriate
technology
.

An
agile

approach is predicated upon
decentralization
,
moving the
ownership

of data closest to those who
understand

the data and are impacted by quality
control

over the data.

All of this requires
trust
, which is fueled by increased
agility

of analyses and
accuracy

of results.



©2013
Lavastorm

Analytics. All rights reserved.

5

A Virtuous Cycle Of Agility, Accuracy, And Trust

Agility

Accuracy

Trust

Agile collaboration
between self
-
sufficient business
SMEs and data
brokers yields
better, faster
results

Accurate results
increase trust,
lowering objections
to further
decentralization

Trust fosters collaboration between IT departments and business users,
starting with the data
-
driven requirements gathering process which is
essential to trustworthy analyses

©2013
Lavastorm

Analytics. All rights reserved.

6

What Type of Data Manager Are You?

Data Waster

Data Collector

Data
Valuer

Strategic Data User

©2013
Lavastorm

Analytics. All rights reserved.

7

How to Become a Data Strategist

Senior
-
level ownership of the organization’s data strategy





Partnership with IT

©2013
Lavastorm

Analytics. All rights reserved.

8

Analytics as an Organizational Philosophy

Constant tuning and monitoring
of processes

Requires a mix of data sleuths,
analytics software, reporting
coupled with data management
and business stakeholder
involvement

Analytics that provide process
guardrails, coupled with ability to
discover new exceptions

Ability to quickly identify and
resolve issues by business
owners


Explore
Data

Define
Analytics

Define
KPIs

Measure
KPIs

Adjust
Behavior

©2013
Lavastorm

Analytics. All rights reserved.

9

Putting Data to Work at
Fairpoint

NNE

500,000+ customers offering
services from Plain
O
ld Telephone
to Carrier Ethernet services

Converted Northern New England
Verizon territory (ME, NH, and VT)
in 2009

Shifting of revenue from voice to
DSL and Carrier Ethernet service
required advanced data analytics

©2013
Lavastorm

Analytics. All rights reserved.

10

Transforming Data Analytics
from
Search
to
Discovery

Fairpoint

NNE has evolved its
data management and analytic
capabilities over the past 3 years

1. Sync Data


Bring data
together

2. Clean Data

How does it relate
across systems

3. Data Analytics


B
ase decisions on
a single source of
data (single dept.)

4. Expanded Trust

Spread analytics to
other departments
up to the
CxO

level

5. Strategic Adoption


Drive changes at
executive level from
analytics (Book to Bill)

©2013
Lavastorm

Analytics. All rights reserved.

11

Creating an
Analytics
-
driven Culture with Clear
-
cut ROI

Data knowledge
-
> trust
-
> greater value


Show how you can relate data across systems


Demonstrate you can deliver results in a short period of time


Ex: On many occasions turning
CxO

level requests regarding order activity
or customer tendencies in 1
-
2 days
.


Led
to a change in Sales criteria: which customers to target for DSL service

Data control leads to better ROI


Easy to demonstrate value compared to a traditional Requirements, Design,
Build, Test process


Ex: Daily analysis and improvement
of data gathering regarding our
customer line
terms and promotions
with
the
CMO


Led
to a repository of customer data leveraged by many department that drives
mail campaigns, SFDC Opportunity generation, and call center activities

©2013
Lavastorm

Analytics. All rights reserved.

12

Qualifying
Analytics Potential
to
Non
-
technical
Executives

Add technology for projects with specific goals/results


Ex. Data Sync
of applications was
an initial use of Lavastorm yielding
numerous cleanup efforts increasing revenue and order flow through

Demonstrate that additional analytics can replace or improve
existing processes


Ex. Replacing 3
rd

Party “
Scorecarding
” application with one Lavastorm
graph/process

Demonstrate value over and above current process, such as:


With
the Lavastorm solution we could visually review the process, and
sample the data at any point in the process to ensure
validity


Decommissioned

the old data warehouse and OBIE solution replacing it
with the Lavastorm /
Cyfeon

solution

©2013
Lavastorm

Analytics. All rights reserved.

13

The Difference Between Data Value and Information
Value

Data value


just the facts


Ex:
R
etention
data analytics
reveals customer
trends associated with what
our customers do at the end of a term or promo
period

Information value


Extrapolation shows what the data really means


Ex: Realize people are more
likely to leave within the first 30 days after
expiration
of a promotion than at
any time following the
expiration

Business value comes from information value


Information value leads to understanding


Ex: Drive re
-
term
and promo sales initiatives
at the end of their term


we
have
a better chance of retaining a
customer

©2013
Lavastorm

Analytics. All rights reserved.

14

New Sales Opportunities from Analytics

Data management and analytics has yielded a single source of
the truth for
our company


Resulting in many
expansions
into the operating
groups within
Fairpoint


Personalized analytics
by developing a web
interface to address ongoing
analytic requests from the operating groups

Integration with CRM/
Salesforce

data ties data to sales activities


Customer
retention data
reveals customer
trends
and indicators


Use
Lavastorm

to generate
opportunities
in
Salesforce

to drive our sales
team to reach out to customers at the point we found that our customers
were leaving
us

©2013
Lavastorm

Analytics. All rights reserved.

15

Summary

Changing data requirements


bigger, more agile, more accurate


Strong data analytics foundation is the key, leading to


Information that leads to business value


Use


Trust


Expansion

Demonstrations lead to executive buy
-
in and an analytics
-
driven
culture

Analytics exposes greater insights, including new
sales
opportunities


©2013
Lavastorm

Analytics. All rights reserved.

16

Questions, Next Steps

Get Lavastorm
Analytics Engine Public Edition
(FREE)

http://www.lavastorm.com/resources/software
-
downloads
-
trials/

Contact Us




Kerry
Reitnauer

+1
603
-
656
-
8188

kreitnauer@fairpoint.com





Mark Marinelli

+1
617
-
948
-
6244

mmarinelli@lavastorm.com






Brandon Smith

+1
512
-
981
-
9408

bsmith@cyfeon.com