DataBench Final Event @ EBDVF2020 4 Nov 2020

1. Evidence Based Big Data Benchmarking to Improve Business Performance
Welcome to DataBench
EBDVF, 4 November 2019
Richard Stevens, IDC
Richard’s Garage – 4/2, Via Del Lido – Casalecchio di Reno, Italy

2. © DataBench 2020 2
Improving
Why Leverage Big Data Technologies

3. OK, I’ve got big data now what do I do?
3

5. Comparing and Assessing Approaches
© DataBench 2020 5

6. What’s Important for my Organisation
Respondents were asked about the 7 main KPI categories selected by the DataBench conceptual
framework as measuring the most relevant business impacts:
• Cost reduction
• Time efficiency
• Product/service quality
• Revenue growth
• Customer satisfaction
• Business model innovation
• Increase in the number of New products or services launched
09-Nov-20
© DataBench 2020 6

7. What kind of Data do I have
09-Nov-20 © DataBench 2020 7

8. What Paradigms and Techniques Do I use
09-Nov-20 DataBench Project - GA Nr 780966 8

9. Statistical analysis: correlation matrix
09-Nov-20 DataBench Project - GA Nr 780966 9
q4.KPIImpor tance.CostReduction
q4.KPIImpor tance.TimeEfficiency
q4.KPIImpor tance.ProductSer viceQuality
q4.KPIImpor tance.RevenueGrowth
q4.KPIImpor tance.CustomerSatisf action
q4.KPIImpor tance.BusinessModelInno vation
q4.KPIImpor tance.IncreaseSer viceProduct
q17.Processing.BatchProcessing
q17.Processing.StreamProcessing
q17.Processing.NearRealTime
q17.Processing.Iter ativeInMemory
q18.TechnicalPerformance.EndToEndExecutionTime
q18.TechnicalPerformance.Throughput
q18.TechnicalPerformance.Cost
q18.TechnicalPerformance.AccuracyDataQuality
q18.TechnicalPerformance.Availability
q19.Analytics.Descriptive
q19.Analytics.Diagnostic
q19.Analytics.Predictive
q19.Analytics.Prescriptive
q15.DataType.TablesStructuredFiles
q15.DataType.TextData
q15.DataType.LinkedData
q15.DataType.GeospatialTemporalData
q15.DataType.Media
q15.DataType.Timeseries
q15.DataType.StructuredText
qs6.StatusOfBDuse
q2.BusinessGoal.CustomerBeha viour
q2.BusinessGoal.Optimiz ePricing
q2.ProductInno vation
q2.ImproveMarket
q2.ImproveBusiness
q2.ImproveFacilities
q2.ImproveOperational
q2.Regulator yCompliance
q3.AbilityToBenchmark
q8.KPIExpectedImpro vement.CostReduction
q8.KPIExpectedImpro vement.TimeEfficiency
q8.KPIExpectedImpro vement.ProductSer viceQuality
q8.KPIExpectedImpro vement.RevenueGrowth
q8.KPIExpectedImpro vement.CustomerSatisf action
q8.KPIExpectedImpro vement.BusinessModelInno vation
q8.KPIExpectedImpro vement.NewProductsLaunched
q10.BusinessProcessIntegr ation
q11.RealTimeIntegr ation
q12.TechnologyInvestment.Cloud
q12.TechnologyInvestment.IoT
q12.TechnologyInvestment.BlockChain
q12.TechnologyInvestment.QC
q12.TechnologyInvestment.AI
q13.DataSize
q14.Systems.Relational
q14.Systems.ColumnarDB
q14.Systems.InMemory
q14.Systems.NoSQL
q14.Systems.Graphs
q14.Systems.NewSQL
q14.Systems.Hadoop
q14.Systems.OpenSourcePlatf orms
q14.Systems.CommercialPlatf orms
q14.Systems.Appliances
q16.ApproachToDataMngmt
q7.BDImpact.TimeEfficiency
q7.BDImpact.ProductSer viceQuality
q7.BDImpact.CustomerSatisf action
q7.BDImpact.BusinessModelInno vation
q7.BDImpact.IncreaseSer viceProduct
q5.LevelOfBenefits
q4.KPIIm
portance.C
ostR
eduction
q4.KPIIm
portance.Tim
eEfficiency
q4.KPIIm
portance.ProductServiceQ
uality
q4.KPIIm
portance.R
evenueG
row
th
q4.KPIIm
portance.C
ustom
erSatisfaction
q4.KPIIm
portance.BusinessM
odelInnovation
q4.KPIIm
portance.IncreaseServiceProduct
q17.Processing.BatchProcessing
q17.Processing.Stream
Processing
q17.Processing.N
earR
ealTim
e
q17.Processing.IterativeInM
em
ory
q18.TechnicalPerform
ance.EndToEndExecutionTim
e
q18.TechnicalPerform
ance.Throughput
q18.TechnicalPerform
ance.C
ost
q18.TechnicalPerform
ance.AccuracyD
ataQ
uality
q18.TechnicalPerform
ance.Availability
q19.Analytics.D
escriptive
q19.Analytics.D
iagnostic
q19.Analytics.Predictive
q19.Analytics.Prescriptive
q15.D
ataType.TablesStructuredFiles
q15.D
ataType.TextD
ata
q15.D
ataType.LinkedD
ata
q15.D
ataType.G
eospatialTem
poralD
ata
q15.D
ataType.M
edia
q15.D
ataType.Tim
eseries
q15.D
ataType.StructuredText
qs6.StatusO
fBD
use
q2.BusinessG
oal.C
ustom
erBehaviour
q2.BusinessG
oal.O
ptim
izePricing
q2.ProductInnovation
q2.Im
proveM
arket
q2.Im
proveBusiness
q2.Im
proveFacilities
q2.Im
proveO
perational
q2.R
egulatoryC
om
pliance
q3.AbilityToBenchm
ark
q8.KPIExpectedIm
provem
ent.C
ostR
eduction
q8.KPIExpectedIm
provem
ent.Tim
eEfficiency
q8.KPIExpectedIm
provem
ent.ProductServiceQ
uality
q8.KPIExpectedIm
provem
ent.R
evenueG
row
th
q8.KPIExpectedIm
provem
ent.C
ustom
erSatisfaction
q8.KPIExpectedIm
provem
ent.BusinessM
odelInnovation
q8.KPIExpectedIm
provem
ent.N
ew
ProductsLaunched
q10.BusinessProcessIntegration
q11.R
ealTim
eIntegration
q12.TechnologyInvestm
ent.C
loud
q12.TechnologyInvestm
ent.IoT
q12.TechnologyInvestm
ent.BlockC
hain
q12.TechnologyInvestm
ent.Q
C
q12.TechnologyInvestm
ent.AI
q13.D
ataSize
q14.System
s.R
elational
q14.System
s.C
olum
narD
B
q14.System
s.InM
em
ory
q14.System
s.N
oSQ
L
q14.System
s.G
raphs
q14.System
s.N
ew
SQ
L
q14.System
s.H
adoop
q14.System
s.O
penSourcePlatform
s
q14.System
s.C
om
m
ercialPlatform
s
q14.System
s.Appliances
q16.ApproachToD
ataM
ngm
t
q7.BD
Im
pact.Tim
eEfficiency
q7.BD
Im
pact.ProductServiceQ
uality
q7.BD
Im
pact.C
ustom
erSatisfaction
q7.BD
Im
pact.BusinessM
odelInnovation
q7.BD
Im
pact.IncreaseServiceProduct
q5.LevelO
fBenefits
−1.0
−0.5
0.0
0.5
1.0
Corr

10. BDVA Reference Model – DataBench Framework
09-Nov-20 DataBench Project - GA Nr 780966 10

11. DataBench ToolBox
09-Nov-20 DataBench Project - GA Nr 780966 11