Introduction to Cervus

• We were formed in 2013

• We come from Force Development and Collective Training

Backgrounds

• We exploit military and security training data to provide our

customers with a comprehensive understanding of their

operational performance.

• Using pioneering data capture systems, industry standard

analytics and secure data storage solutions, we provide

services to exploit your training data.

The Col lect ive Training System

• Environment System – what is

required to create the appropriate

Environment

• Scenario System – what is

required to wrap around the

environment with a logical and

consistent scenario

• Capture System – what is

required to capture appropriate

contextual data and performance

data on the training subjects

• Analysis System – that which is

required to collate, analyse and

manage data

• Exploitation System – what is

required to exploit the data for

AAR and as long term actionable

insights

MANAGEMENT RULES

SCENARIO

ENVIRONMENT

CAPTURE SYSTEM

ANALYSIS SYSTEM

EXPLOITATION SYSTEM

Commander Force Development and Training (Comd FDT) July 2011

The central idea is unlocking

power through applying a concept

of exploitation [of] what we can

learn from current operations,

from our training and from

experimentation within training.

Balancing Act

Training Experimentation

Focussed on the

achievement of

Collective Training

Objectives (CTOs)

Data capture

typically to

support an after action review

Known costs and budgeted

Limited opportunity for

repeatability

Focussed on the

experimentation

objectives and evidence

Data capture to

inform measures of

performance / effectiveness

Requiring

repeatabilityUnknown costs and

limited budget

Our Vis ion for Tra in ing Data Explo i ta t ion

Harvest All Data

LVC and C2

Objective Data

Intelligent Analysis

Analytics Broad Utility

F r o m s e l e c t i v e d a t a g a t h e r i n g t o h a r v e s t i n g a l l d a t a .

• The reduced cost, size and power consumption of

modern sensors, power storage technologies,

communications systems and data storage

devices means there is no longer a need to limit

data gathering to small data sets.

• It is now technically possible to harvest and record

virtually all data, linked to all participants

(BLUEFOR, OPFOR, wider COEFOR and

observers themselves) associated with every

training event: allowing for an exponentially richer

training data set to mine and exploit – enabled by

a “capture everything now, use anything later”

approach.

F r o m u s e o f L i v e t r a i n i n g d a t a t o t h e u s e o f L i v e , V i r t u a l , C o n s t r u c t i v e & C o m m a n d d a t a .

• Traditionally, the main focus for training data gathering has

been within the Live training environment, and then mostly

positional and firing data. However, there is a plethora of

wider useful training data that can be harvested: human

biometrics; vehicle usage, performance and health; C4I

(voice, data and meta-data); ISR; topographical and

meteorological data to name but a few.

• Also as the Live, Virtual and Constructive training

environments continue to blend ever more seamlessly into

a single synthetic training environment, there is an

opportunity to draw (and merge) training data from each of

these synthetic domains to provide a far more holistic

training data set to mine and exploit.

• Comms data and HUMS are both undervalued and

underused training data sources.

F r o m p r i m a r i l y g a t h e r i n g s u b j e c t i v e d a t a t o t h e h a r v e s t i n g o f o b j e c t i v e d a t a .

• Whilst some limited objective data is currently gathered,

new and emerging technologies allow for the routine and

automatic harvesting and storage of far more, and far

wider, objective data sets.

• This will free the training observer to concentrate more

on expertise-based subjective observation but, as

importantly, will provide a wealth of context within which

to ultimately frame far more meaningful and useful

insights from training.

F r o m m a n u a l a n a l y s i s t o a u t o m a t e d , i n t e l l i g e n t ( A I ) a n a l ys i s .

• The relentless development of ever-more ‘intelligent’

machines provides an exceptional opportunity to move

rapidly away from the resource-heavy, time-consuming

activity of manual data analysis to an automated – even

intelligent – analytical approach.

• Self-adapting algorithms, pattern recognition

technologies and machine learning approaches now

mean that the drawing of meaningful insights from

masses of data is simple, time-efficient, ever-improving,

self-teaching and increasingly affordable. The obstacles

that the manual processing and analysis of large

amounts of training data once presented are now easily

surmountable.

F r o m d e s c r i p t i v e a n a l y s i s t o p r e d i c t i v e & p r e s c r i p t i v e a n a l y s i s .

• This machine learning capability now allows for a

genuine step-change from purely Descriptive analytics

(What has just happened? What is happening now?) to

Predictive analytics (What is likely to happen next,

based upon experience?) to Prescriptive analytics

(What could/should be done about what is likely to

happen next, so as to achieve a positive outcome?).

• From retrospective after-action consideration of training

data to real-time interpretation and understanding.

• From observed facts (pure data) to informed insights

(via analysed data in context).

• From simple observation of training, mostly after the

event, to helpful, proactive interventions during (and

even before) training.

F r o m l i m i t e d t o b r o a d u t i l i t y .

• By making use of open architectures and common

standards and modern cloud-based storage and

processing technologies, the training data gathered, and

the analysis drawn from it, will be of use not just to the

immediate training community but also to individuals

and the wider field army, force development,

research, experimentation & acquisition communities

– all of whom will be able to access the data they need

whenever, and from wherever, necessary.

HIVE- a solut ion

• HIVE will be unparalleled in its adaptive ability to

harvest, categorise, store, and analyse data

from collective training environments.

• More than that, HIVE can deliver genuinely

comprehensive and exploitable insights via its

innovative machine learning engine and unique

visual reporting systems: thus, allowing

commanders to quickly spot and interpret trends

from training, gain context-derived insights from

them, and to rapidly and clearly identify

opportunities for enhancements to warfighting.

• HIVE can truly build ‘winning foundations’.

Matthew Syed

You need judgement and creat iv i ty to

determine how to f ind solut ions to what

the data is te l l ing you, but those

judgements, in turn, are tested as par t of

the next opt imisat ion loop. Creat iv i ty not

guided by a feedback mechanism is l i t t le

more than whi te noise. Success is a

complex interp lay between creat iv i ty and

measurement , the two operat ing together ,

the two sides of the opt imisat ion loop. ”

HIVE Demonst ra t ionCollective Training Event

Assessment ProgrammeId Name Start Date End Date

1

Initial

Assessment 07/05/2018 07:35 07/05/2018 12:13

2 Mission 1 07/05/2018 13:35 07/05/2018 16:13

3 Mission 2 08/05/2018 07:35 08/05/2018 10:11

4 Mission 3 08/05/2018 12:35 07/05/2018 14:10

5 Mission 4 09/05/2018 07:35 09/05/2018 12:26

6

Initial

Assessment 08/05/2018 06:55 08/05/2018 12:13

7 Mission 1 08/05/2018 12:35 07/05/2018 14:10

8 Mission 2 09/05/2018 07:35 09/05/2018 12:26

9 Mission 3 09/05/2018 14:13 09/05/2018 16:16

10Mission 4 10/05/2018 07:05 10/05/2018 14:15

11

Initial

Assessment 09/05/2018 07:05 09/05/2018 12:11

12Mission 1 09/05/2018 14:40 09/05/2018 16:43

13Mission 2 10/05/2018 07:05 10/05/2018 12:11

14Mission 3 10/05/2018 14:10 10/05/2018 16:32

15Mission 4 11/05/2018 07:09 11/05/2018 12:23

Id Msn 4 Events

1 Battle Preparation

2 Vehicle Move to LoD

3 Pre designated Fires on Enemy Target

4 Adjustment of Fires

5 Fire Support from MIV

6 Cross LoD

7 Assault Building 1 Room 1

8 Assault Building 1 Room 2

9 Assault Building 1 Room 3

10 Enemy Fires onto BLUEFOR

11

Enemy Counter Attack from Building 2 onto

Building 1

12

Exploit and Assault from Building 1 to

Building 2

13 Assault Building 2 Room 1

14 Reorganisation

15 Casualty Extraction

16 POW Extraction

17 Hot Washup

18 Training System Reset

Scenario and Environment

Fires Bde Fires

Fires BG Fires HICON Coy HQ

Dismount Sect Comd

Dismount Sharpshooter

Dismount Grenadier

Dismount Grenadier

Dismount NLAW

Dismount ASM

Dismount Sect 2IC

Dismount Rifleman

MIV AFV

Crew Driver

Crew Gunner

Crew Commander

Building Enemy Position

Building Enemy Position

Observer Mentor Primary OM

Observer Mentor Secondary OM

Observer Mentor Primary OM

Observer Mentor Secondary OM

Enemy Dismount Commander

Enemy Dismount Sharpshooter

Enemy Dismount Grenadier

Enemy Dismount Anti Tank

Enemy Fires Combat Team Fires

Simulation:

• Noise

• Smell

• Blast /Vibration

Capture Systems

• I-DES

• DFWES

• AWES

• HR Monitor

• HUMS

• Joint Fires Virtual

• I-DES

• I-DES

• Joint Fires Virtual

• AWES

• Warrior Metric

• Training

Management and

Capture Tablets

• DFWES

• AWES

• I-DES

• DFWES

• AWES

• Joint Fires Virtual

• I-DES

• Joint Fires Virtual

• AWES

Current Systems

Analysis and Exploi tat ion

Harvest All Data

LVC and C2

Objective Data

Intelligent Analysis

Analytics

Broad Utility