Automatically Detect. Using Data Analytics to. matters in the data from. Automatically find what. Equipment Systems

Using

Data

Analytics

to

Issues,

Trends,

Faul

Equipmen

Automatically “find what 

equipment systems R D Remote Dec

o

Automatically

Detect

lts

and

Anomalies

in

nt

Systems

t matters” in the data from 

s and smart devices

b 2013 cember 2013

Our world contains bi

Producing vast qua

Producing

vast

qua

sec

ntities of data every

ntities

of

data

every

To create value from

To

create

value

from

to

find

what

mat

m our devices we need

m

our

devices

we

need

…By detecting patt

…By

detecting

patt

deviations,

anom

opportunitie

opportunitie

terns that represent

terns

that

represent

malies,

faults,

and

es for savings

es

for

savings

Too Much Data !!!

Analytics

y

is

the

Key

y

A

l ti

ft

t



Analytics

software

autom

“issues”

(things

that

matt



Equipment

faults,

deviati

performance,

actual

resu

etc



Unlike

efficiency

measure

installation

of

major

capit

works

with

the

data

avail



Relatively

easy

to

add

to

ti ll l

k f

matically

looks

for

ter)

in

our

data….

ons

from

expected

lts

vs goals

or

benchmarks,

es

that

involve

the

tal

equipment,

analytics

lable

from

existing

sources

what

we

have

The

Bigger

Picture:

An

ld

our

world…

It can change the

way we manage

our systems too!

our systems too!

Gather

data

and

find

pa

the issues

that matter

the

issues

that

matter

Device data Utility data Facility data Utility data Weather data Production data

Connect to available data Aggregate Connect to available data

sources

Aggregate available d

tterns

that

represent

data do you have?

e and normalize Detect patterns that e and normalize

data

Detect patterns that represent issues

Automatically fi

Automatically fi

correlations i

ind patterns and

ind patterns and 

in device data

The

Result:

Know what your 

Know what your systems are really 

doing

Automatically scans your 

data to find what matters Automatically generates 

views on issues detected Convert expert domain 

knowledge to rules –

your value continues to 

y

build

Explore relationships and 

l ti ld correlations you would 

not have otherwise seen

The “Spa related to

ark detail” page – shows everything o the occurrence of an issue

Vehicle/Fleet

Tracking

Concept:

Concept:

Collect

and

analyze

vehicl

GPS

data

to

determine

operational

p

issues

Examples

of

potential

ana

•

Vehicles

traveling

outsid

“fencepost

area”

•

Vehicles

stopped

for

too

•

Speed in excess of regula

•

Speed

in

excess

of

regula

g

e

alytics:

de

of

o

long

ations

ations

Vehicle Tracking Example Example

Security

Collect and analyze security sy represent threats or improp represent threats or improp conditions

Analytic example: Analytic example:

• Combine video analytic d

d t E l b l ft

data. Example: a bag left 

Expected response is disp confirm

confirm

• Analytic looks for correla response rates time to re response rates, time to re

ystem data for patterns that 

per per 

detected “events” with other 

tt d d t d

unattended at a door. 

patch of guard to area to 

tion of responses to events, 

esponse etc esponse etc.

Cold

Chain

Manageme

• Need to insure that food, drugsNeed to insure that food, drugs perishables) have been mainta temperatures – if not they mus by regulation

• Monitor temps, compressors, o times, etc.

• Track patterns and magnitude o faults – time is a major factor

• This is a ggrowingg area of regulag

• Analytics simplifies and reduce analysisy

ent

Tracking

Data

Usage

i

M2M

Applications

Concept: 

• _{Cell‐phone connected devices r} server. Each site needs to have 

sized based on initial expectatio • Buying more data capacity than

expensive

• _{Going over your plan allotment} • _{But manually analyzing usage a}

very difficult and expensive

• Analytics enabled a partner to a choose a more cost effective pl

n

report data back to a central 

a data plan. Data plans are 

ons

n needed can be very 

t can be even more expensive across thousand of devices is 

analyze actual data needs and 

Analytics

y

vs Alarms

 An alarm is when you are on the g

 An alarm is when you are on the g

Analytics are the lab tests you tak stay out of the ER

 Alarms require that you fully unde

Analytics find patterns & issues yo

 Controller‐based alarms only dea

Analytics combine operational, en corporatep  data to show patternsp  a portfolio of device data

 Correlation examples – equipm

h ff d f

weather effects, production fa

 Analytics replace the majority of n

that explain what is happening an that explain what is happening an

vs

gurney in the ER

vs

gurney in the ER

ke every year to 

erstand the issue ahead of time –

ou couldn’t have foreseen

l with control system data –

nergy, production, facility and  and correlations across youry  

ment type, age, material, vendor, 

actors, etc

non‐productive alarms with insights

nd why nd why

Summary

‐

Analytics

Summary

Analytics

You can’t control what



You

can t

control

what



Analytics

enables

us

to

equipment

systems

ar



Easy to get started W



Easy

to

get

started.

W



Use

available

data

– re



Its

time

to

generate

va

s Value Proposition

s

Value

Proposition

t you don’t measure

t

you

don t

measure

o

know

how

your

re

actually

operating

hat data do you have?

hat

data

do

you

have?

eal

time

and/or

batch

alue

from

our

data!

Fi d h

Find

wha

t

tt

™

t

matters™