JPK

Gro

upOrganizational Intelligence Forum

June 16-17 • Boston, MA

Turning Data Into a Strategic AssetEmploy data analysis and metrics across every function inthe organization by creating a data/insight-driven culture

June 17, 9:45am

View presentation online at:

https://jpkgroupsummits.com/attendee4

Don Gray – Cigna

KANRI DISTANCE

CALCULATOR™

TURNING DATA INTO AN ASSET

Mov ing Beyond the Bas ics

June 17, 2016

OVERVIEW 1 The Basics

2 Beyond the Basics

3 Purpose-Driven Insights

4 Individualization

5 Actionable

THE BASICS

What the Business Gets

CURRENT “DATA AS AN ASSET” PARADIGM

KANRI Purpose-Driven Insights 4

Creating governed, authoritative data and ultimately master data

• Improved data trustworthiness and efficiency

• Increased persistency

Distributed metrics and performance measures under the banner of

business intelligence

• Run rate trend lines

• Period-over-period performance measures

Predictive Analytics and Population Data Management

• Fitting a population to a line, forecasting against formulas

• Identifying overall data drivers

• Population proof points

WHAT BUSINESS

LEADERS NEED

PURPOSE-DRIVEN INSIGHTS

DATA AS AN ASSET: Beyond the Basics 6

Analytics individual

level

Contributions of

variables affecting

individuals

Individualized

outputs help to

take decisions

that are affordable

1 INDIVIDUALIZATION

2 SPEED

Easy to interpret

analytical results

1 to 2 models per

week (much faster

than traditional

analytics)

Ability to add easily

new rows and

columns to prior

analyses

3 4

Distance

measures

Overall population

drivers of the

distance

Individual distance

drivers (variables

and combination of

variables)

EASE OF USE

Simple and easy

execution

Straightforward

outputs

Root cause

analysis

prioritization

through contextual

flagging

INSIGHTS PRECISION

1 to 2 models per

week (much faster

as compared

traditional

analytics)

Actionable insights

to partners

Enhanced quality

of insights

PRODUCTIVITY GAINS

5

Key Enablers: Simple & Affordable Innovative Technology, Tools Techniques and User Talent

Embrace and adapt to change faster

Deliver differentiated value through personalized results and distance metric

INDIVIDUALIZED

INSIGHTS

KANRI Purpose-Driven Insights 9

1. Define the target (healthy) group and all

attributes for consideration

2. Development of multivariate measurement

scale with target group as the reference

3. Calculate each off-target participant’s

distance from target by running data

through KDC™

4. Perform optimization as needed to reduce

the number of variables.

5. Perform individual diagnostics through root

cause analysis and identify contributing

variables for each participant

Importance of Correlations

KANRI Methodo logy

X1=Weight

X2=Height Target Group

KANRI

Distance

Sam

X

Raj

KANRI

Distance

Sam and Raj are close to target if we look

variables independently.

With correlations they are off-target. In fact

Sam has higher degree of abnormality

A N A LY S I S O F D I A B E T E S C A R E G A P S

Total # of variables – 83

# of variables considered for analysis - 49

Sample size

- Healthy – 11739

- Abnormal – 45227

- For prediction – 21564

• Optimized set of 32 variables

• Distances of each of the 45227 abnormals

and 21564 requiring prediction

• Contribution of each variable to abnormal

distances.

Data set Approach

Var Description

X2 age

X3 gender_cd

X5 New_Race

X7 prospective_risk_num

X13 TDS_lbp_flg

X14 TDS_cad_flg

X16 TDS_osteoknee_flg

X17 TDS_osteohip_flg

X19 TDS_bngnutrn_flg

X20 Wellness_prvntn_flg

X22 Wellness_hlthyeat_flg

X24 Wellness_hyprlpdm_flg

X25 Lifestyle_Weight_flg

X26 Lifestyle_Tobacco_flg

X27 Lifestyle_Stress_flg

X28 Chronic_Weight_flg

X30 Chronic_Osteo_flg

X32 Chronic_DIA_flg

X33 Chronic_DEP_flg

X34 Chronic_CPD_flg

X35 Chronic_CHF_flg

X36 Chronic_CAD_flg

X37 Chronic_AST_flg

X40 prof_visits

X41 fop_visits

X42 er_visits

X43 inp_admits

X44 tmc_amt

X47 chronic

X48 tds

X49 wellness

X50 lifestyle

32 Variables

Average Distance

Healthy 1

Abnormal 10.7

Avg. distance

There is a good separation between healthy

group and abnormals is higher with optimal set of

variables. Higher separation is desirable.

10

It is important to note that individual drivers are not the same even though they are subset of

population drivers This underscores the importance of individualized analysis

Root Cause Analysis With Contribution Ratios – Distance Sensitivity Table

ANALYSIS OF DIABETES CARE GAPS

X1 X2 X3 X5 X7 X13 X14 X16 X17 X19 X20 X22 X24 X25 X26 X27 X28 X30 X32 X33 X34 X35 X36 X37 X40 X41 X42 X43 X44 X47 X48 X49 X50

INDIV_

ENT_ID age gender_cdNew_Raceprospective_risk_numTDS_lbp_flgTDS_cad_flgTDS_osteoknee_flgTDS_osteohip_flgTDS_bngnutrn_flgWellness_prvntn_flgWellness_hlthyeat_flgWellness_hyprlpdm_flgLifestyle_Weight_flgLifestyle_Tobacco_flgLifestyle_Stress_flgChronic_Weight_flgChronic_Osteo_flgChronic_DIA_flgChronic_DEP_flgChronic_CPD_flgChronic_CHF_flgChronic_CAD_flgChronic_AST_flgprof_visitsfop_visitser_visitsinp_admitstmc_amtchronictds wellnesslifestyle

A1 31496 11.163 0.558 62.215 0.279 0.421 0.523 0.686 0.475 0.247 0.260 0.467 0.178 0.188 0.503 1.664 0.031 0.427 0.089 0.176 0.286 0.545 0.017 0.646 0.454 0.092 0.589 0.549 1.553 0.111 0.000 13.797 0.007

A2 57537 0.010 10.003 7.073 0.219 0.426 0.250 0.278 0.288 0.218 0.315 0.406 0.102 0.266 0.424 28.467 0.351 0.636 17.614 0.205 0.377 0.394 0.192 0.355 0.018 0.062 0.314 0.559 0.342 27.969 0.831 0.488 0.509

A3 59761 0.477 0.702 0.274 0.222 0.266 0.110 0.467 0.108 0.084 0.124 0.015 0.049 0.036 0.072 0.309 0.184 0.048 0.018 0.005 0.128 0.160 0.029 0.035 94.044 0.286 0.868 0.437 0.210 0.050 0.037 0.013 0.089

A4 69444 39.622 4.683 0.472 0.269 0.382 0.243 0.404 0.226 0.213 0.272 0.292 0.279 0.089 0.004 16.018 1.883 0.247 8.282 1.272 0.281 0.252 0.290 0.288 0.029 0.488 0.179 0.245 1.797 12.056 0.011 8.561 0.222

A5 76105 0.027 0.000 0.069 0.064 0.118 0.618 0.029 0.071 0.148 0.004 96.553 0.060 0.043 0.044 0.034 0.045 0.055 0.110 0.068 0.121 0.103 0.044 0.060 0.054 0.029 0.049 0.095 0.079 0.111 0.068 0.037 0.042

A6 79558 0.587 0.013 0.164 0.086 1.436 1.428 0.148 0.794 0.380 9.478 2.962 34.663 0.122 4.673 0.275 0.000 0.738 2.359 8.035 1.564 0.208 10.126 0.970 13.188 0.001 2.186 0.014 1.899 0.032 0.015 0.996 0.110

A7 98698 0.012 0.003 0.210 0.082 0.080 0.103 0.087 0.084 0.085 0.077 0.079 0.042 0.013 0.043 0.319 96.897 0.183 0.050 0.270 0.081 0.074 0.055 0.079 0.071 0.048 0.054 0.080 0.116 0.001 0.162 0.394 0.030

A8 103039 0.865 10.435 5.760 0.201 0.348 0.265 0.320 0.316 0.311 0.333 0.267 0.028 0.346 0.330 28.727 0.266 1.128 16.882 0.242 0.411 0.456 0.330 0.434 0.005 0.026 0.446 0.353 0.279 27.922 1.026 0.317 0.591

A9 107666 0.164 0.114 0.851 0.025 0.204 0.160 2.043 0.138 0.045 3.686 0.063 0.124 0.015 0.013 0.150 0.240 2.954 0.463 0.118 0.081 0.132 0.189 0.173 0.106 0.056 0.074 85.473 1.747 0.043 0.074 0.089 0.137

A10 116634 11.512 9.712 15.077 0.680 0.854 0.287 0.095 0.831 0.418 0.540 0.044 0.006 16.470 0.212 0.412 0.098 0.000 1.455 0.006 1.062 0.504 0.302 0.305 0.419 0.560 0.405 0.721 0.506 0.592 0.439 0.173 12.764

A11 121729 3.399 7.195 0.400 0.280 0.061 1.324 0.000 0.931 1.497 0.443 0.291 1.038 0.382 0.364 5.405 2.028 0.200 0.011 0.512 1.200 0.633 0.147 1.065 15.852 23.930 1.043 26.131 0.372 0.655 0.511 0.899 1.729

A12 138354 87.517 0.104 3.747 0.063 0.340 0.119 0.191 0.077 0.090 0.221 0.218 0.265 0.033 0.093 0.770 0.269 0.103 0.000 0.235 0.169 0.226 0.166 0.201 0.001 0.388 0.094 0.175 0.799 0.077 2.170 0.850 0.165

A13 139632 4.174 34.331 44.792 0.311 0.290 0.417 0.503 0.321 0.167 0.024 0.152 0.046 0.165 0.243 1.260 0.148 0.007 0.005 0.014 0.873 0.368 0.066 0.210 0.042 0.315 0.290 0.529 1.150 0.533 0.001 7.591 0.003

A14 146940 0.433 0.662 2.188 0.110 0.175 0.236 0.208 0.118 0.098 0.065 0.031 0.428 0.081 0.197 0.172 0.164 1.318 0.705 90.678 0.209 0.143 0.142 0.200 0.009 0.145 0.102 0.164 0.076 0.208 0.002 0.375 0.032

A15 186221 0.015 0.007 0.041 0.073 0.031 0.078 1.843 83.184 0.161 0.168 0.252 0.040 0.153 0.684 2.538 0.640 0.864 0.186 0.176 0.135 0.129 3.768 0.184 0.174 0.975 0.056 0.029 0.080 0.102 3.080 0.009 0.106

A16 194970 22.963 10.629 10.758 0.584 1.228 0.816 0.434 0.266 0.135 0.246 0.499 0.334 0.230 0.492 1.173 0.352 0.244 0.036 0.053 0.793 0.361 0.438 0.549 1.204 1.233 32.901 3.751 0.828 0.497 0.016 5.342 0.048

A17 202042 0.003 0.142 0.768 0.119 0.202 0.030 0.596 0.289 0.170 0.244 0.030 0.063 0.208 0.046 0.517 0.061 0.098 0.114 0.003 0.054 0.201 0.024 0.053 93.823 0.003 0.506 0.656 0.064 0.271 0.284 0.137 0.128

A18 219945 14.768 0.039 0.027 4.276 0.533 0.596 0.681 0.389 0.342 0.647 0.040 0.171 0.732 0.001 25.532 0.072 0.541 15.653 1.249 0.332 0.293 0.723 0.176 0.035 0.275 0.016 0.064 1.369 29.432 0.437 0.025 0.508

A19 228624 31.240 10.411 0.971 0.400 0.631 0.509 0.934 0.620 0.320 0.242 0.623 0.320 0.051 0.207 3.977 0.004 0.326 0.535 0.061 0.319 0.309 0.135 0.791 0.316 0.183 0.203 0.278 0.856 0.196 0.013 41.896 0.127

A20 251635 0.368 2.475 0.048 0.249 0.545 0.148 0.048 0.494 0.468 0.645 0.076 0.004 30.344 0.034 0.097 0.000 0.049 1.122 0.067 0.419 0.225 0.014 0.095 0.323 0.000 0.132 0.544 0.453 0.248 0.113 0.004 25.467

A21 252269 0.018 0.001 4.868 0.141 0.176 0.006 0.246 0.013 0.516 0.100 0.193 0.093 0.024 0.334 0.214 0.015 0.123 0.059 0.138 0.415 0.449 90.428 0.337 0.013 0.038 0.216 0.226 0.213 0.131 0.072 0.039 0.052

A22 259781 0.117 5.468 0.008 0.115 0.183 0.129 0.134 0.122 0.141 0.067 0.130 0.133 0.765 0.106 15.334 24.530 0.164 35.850 0.110 0.131 0.114 0.200 0.123 0.150 0.127 0.101 0.140 0.137 12.228 0.198 2.289 0.003

A23 265805 0.532 1.112 0.201 0.208 0.289 0.148 0.094 0.285 0.262 0.004 0.008 0.938 2.980 0.280 0.362 1.154 1.937 0.038 72.871 0.450 0.493 0.147 0.113 0.003 0.133 0.080 0.197 0.015 0.174 2.278 3.405 1.957

A24 274688 5.647 0.052 2.554 0.838 0.107 56.843 0.000 2.669 0.298 0.021 0.462 0.772 0.749 0.118 1.361 4.978 1.273 0.290 0.055 0.421 0.052 1.372 0.256 3.159 2.360 0.266 0.406 0.279 2.011 6.473 2.715 1.095

A25 277406 0.117 0.010 0.057 0.057 0.050 0.144 0.588 0.271 0.222 9.801 0.001 0.029 0.070 0.091 0.019 82.716 0.107 0.260 0.209 0.207 0.219 0.119 0.012 0.018 0.062 2.478 0.260 0.241 0.379 0.145 0.075 0.000

11

T R A N S A C T I O N A L N E T P R O M O T I O N A L S C O R E ( t N P S ) S T U D Y

S u m m a r y

Approach

KDCTM

Analysis

1394 variables

Target & Off-Target

observations

16 Variables Personalized-

Results

Impact tables

What has been done? Personalized Results (Sample Output)

KDCTM Methodology executed for tNPS data with a target

group of more than 38000 people and 17225 off-target

people

Distances of all 17225 off-target people

Overall population drivers of distance

Personalized distance drivers

Distances of 17225 off-target people

Contribution of each variable to all 17225 off-target

people

Impact analysis (based on individual variables and

combinations of variables)

Entire analysis done in less than 2 days

KDC™ has ability to add any new variables

KDC™ has ability to add customers even if they have not

taken tNPS survey

12

ACTIONABLE

ACTIONABLE INSIGHTS

KANRI Purpose-Driven Insights 14

Deliver data that explains performance gap root causes

Drive insights down to the individual customer/participant level

Simple, understandable output in terms on distance from target and root

cause drivers in percentages

Straight forward methodology that does not require a team of data

scientists to define and kick off

w w w. k a n r i - i n s i g h t s . c o m