DataBench in a Nutshell - The market: Assessing Industrial Needs, Richard Stevens, BDV Meet-Up Riga, 27/06/2019

1. Evidence Based Big Data Benchmarking to Improve Business PerformanceEvidence Based Big Data Benchmarking to Improve Business Performance
The market: Assessing Industrial Needs
BDVA Meetup
27 June 2019, Riga
Richard Stevens - IDC

2. Outline
✓Objectives
✓Summary of analytics questionnaire
✓Use case selection
✓Case study analysis methodology
✓Preliminary insights from case study analysis
DataBench Project - GA Nr 780966 2

3. Matching Needs to Measurements

4. How to Recognize Value of Big Data Technologies
• Desk research using OECD / Eurostat / IDC data
Prepare a preliminary classification of the main business drivers and
KPIs for companies using BDT
• 700 Companies from diverse business sectors
• different types and sizes of companies
Survey businesses using BDT and representing to rank importance of
these KPIs
• Size, type and approach to analytics
Perform detailed surveys about the technical infrastructure
Correlated business data and technical parameters
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5. What’s important for companies
Respondents were asked about the 7 main KPI categories selected by the DataBench
conceptual framework as measuring the most relevant business impacts:
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Cost reduction Time efficiency
Product/service
quality
Revenue growth
Customer
satisfaction
Business model
innovation
Increase in the
number of New
products or
services launched

6. DataBench Project - GA Nr 780966 6
Desk Research
DataBench Project - GA Nr 780966 6
Thorough examination of
✓over 100 research papers centered on
measuring business performance
✓Characterization of use cases and vertical
industry
✓633 use cases in total
✓59 use cases per industry on average
Comparing data from survey with data from
the desk analysis provides mainstream vs.
innovation insights.
RESEARCH
PAPERS
ICT
VENDORS
ICT 14-15
PROJECTS

7. Dimensions and values of business features
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8. Dimensions and values of the technical
features
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9. Agriculture
• KPIs
• USE CASES
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Extremely/Very
Moderately
important
Slightly/Not
Cost reduction 022% 023% 055%
Time, efficiency 038% 025% 037%
Product/service quality 034% 037% 029%
Revenue growth 037% 035% 028%
Customer satisfaction 042% 031% 028%
Business model innovation 028% 026% 046%
Increase in # of new
products
031% 043% 026%
Mean at this interest level
all KPIs
040% 032% 028%
000%
010%
020%
030%
040%
050%
060%
070%
Agriculture KPI priority
Importance KPI
- Cost Reduction
- Time Efficiency
- Product/service quality
- Customer satisfaction
- Business Model
innovation
Use Case # Responses % Responses
Field mapping & crop scouting 44 68%
Price Optimization 42 65%
Inventory and service part
optimization
42 65%
Example: Agriculture

10. Statistical analysis: correlation matrix
03/07/2019 DataBench Project - GA Nr 780966 10
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

11. Statistical analysis: factor analysis
03/07/2019 DataBench Project - GA Nr 780966 11
• The factor analysis stresses that companies that have already obtained
and measured business benefits from BDT projects are focused on
traditional batch processing.
• In contrast, companies that experiment with more advanced real time
applications of BDTs have not yet measured business benefits.
• Moreover, companies that have not yet exploited BDTs or have a
traditional exploitation of BDTs (batch) are technology enthusiast and/or
plan to explore more innovative applications of BDTs, but do not view
future business benefits as measurable with economic KPIs at this stage
of development of BDTs.

12. DataBench Project - GA Nr 780966 1212
Use Case Analysis
ICT 14-15
PROJECT
desk
analysis
INDUSTRIAL
NEEDS
SURVEY
RESEARCH
PAPERS
desk
analysis
ICT VENDORS
desk analysis

13. DataBench Project - GA Nr 780966 13
Use case selection criteria
DataBench Project - GA Nr 780966 13
The list of use cases is based on the IDC industrial needs survey.
The list of DataBench use cases was defined by:
✓using one use case from state of the art use cases list -> to be able to assess the
business KPIs,
✓using one use case from the desk analysis -> to account for research and emerging use
cases,
✓preferring use cases specific to the industry -> to make it easier to identify pilots with
quantitative business KPIs,
✓keeping some cross industry use cases, e.g., supply chain optimization is a topic in
manufacturing as well as in retail.

14. Case study analysis methodology
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The follow-up interview should cover the
aspects/perspectives missed by the first interview
by:
✓ involving respondents with a more specific
profile,
✓ focusing on the collection of quantitative
business KPIs.

15. San Raffaele
Hospital
Whirlpool-
BOOST
H2020 e2mc
Research
case studies
03/07/2019 15
H2020 EWShopp
•Evaluating performances of Arango
and Orient DB
•Expected measured KPIs mainly focus
on customer satisfaction
ESA
•Gaia is the only mission surveying the
complete sky with unprecedented
precision and completeness
•Processing 100 GBs of raw data every day
IDEKO
•Enhancing descriptive
analytics and experimenting
anomaly detection using ML

16. Airbus
Event
Registry
Cerved
Industrial
case studies
03/07/2019 16
TravelBird
• Predictive analytics on
event data
• Revenue growth
Intel
•Real-time predictive analytics
(mainly anomaly detection)
•Avoid revenue loss due to
malfunctions of the equipment
Pam
112
eGeos

17. Case study
analysis: cases
by industry
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•Pittarosso
•Pam
•H2020 EW-Shopp
Retail & Wholesale
•eGeos
•ESAAgriculture/EO
•INTEL
•Fater
•Whirlpool
Manufacturing
•San Raffaele HospitalHealthcare
•TravelBird
•Event Registry
•Ideko
Business / IT Services / AI
•Siemens
•112Transport and Logistics
Financial Services
Telecom/Media
Utilities / Oil & Gas

18. Early Insights
• From the evidence that has been collected so far, an important lesson learnt is that most
companies believe that technical benchmarking requires highly specialized skills and a
considerable investment. We have found that very few companies have performed an
accurate and extensive benchmarking initiative. In this respect, using DataBench like
Solutions grants them with an easier access to a broader set of technologies that they can
experiment with.
• On the other hand, they acknowledge the variety and complexity of technical solutions for
big data and envision the following technical risks:
• The risk of realizing that they have chosen a technology that proves non scalable
over time, either technically or economically.
• The risk of relying on cloud technologies that might create a lock in and require a
considerable redesign of software to be migrated to other cloud technologies.
• The risk of discovering that cloud services are expensive, especially as a
consequence of scalability, and that technology costs are higher than business
benefits.
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19. Evidence on business KPIs from case
study analysis
• We have evidence of business KPIs for case studies where we have reached the pilot
stage according to our case study methodology.
• Evidence is aligned with results from survey (business benefits are in the 5-8% range).
• We have already performed 4-5 case studies at pilot stage confirming results
• Business KPIs are seldom quantified in cases studies from the literature (most projects
are at POC level).
• From the desk analysis, multiple business KPIs are affected simultaneously and the
benefits from a single project are often difficult to isolate from other factors affecting
the same business KPI.
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20. Sample case study: intelligent fulfilment
in retail
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• Automatic replenishment optimization is a fundamental process in the retail
industry, it involves multiple departments, e.g., logistics, order management,
etc., and affects sales through stockouts and, thus, revenues and customer
satisfaction.
• Currently, in the analyzed case study the replenishment process is carried
out manually by the point of sale (POS) employees that periodically check for
the presence of a sufficient number of items/products to fulfill the expected
demand for the following days.
• The manual process has evident drawbacks, as it is error prone and suffers
of the bias introduced by the judgment of the employee.

21. Sample case study: intelligent fulfilment
in retail
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• The company is piloting an automatic replenishment procedure that will optimize the order
scheduling process by using a set of sensors to detect the number of items on the shelves and by
adopting a machine learning algorithm to forecast products demand.
• Overall, the main goals of the case study are to improve the quality of the service provided to
customers and to improve the efficiency of the replenishment process.
• From a technical perspective, innovative aspects of the new automatic replenishment procedure
include:
✓ the adoption of an ad-hoc prediction algorithm for product demand forecasting,
✓ the positioning of a set of image sensors able to monitor in real-time the number of items on
the shelves,
✓ the adoption of an image recognition algorithm able to identify the number of products on
the shelves, and consequently to identify mis-placed items, items positioned inaccurately,
etc.

22. IT architecture: intelligent fulfilment in
retail
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23. Blueprint
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24. Business KPIs: intelligent fulfilment in
retail
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• Business benefits carried by the intelligent fulfillment are manifold.
• General indicators, useful to assess the effectiveness of the process, include:
✓ revenue growth due to avoided lost sales,
✓ customer satisfaction,
✓ improvement in the efficiency of the fulfillment process, that results to be more structured and
organized,
✓ more specific indicators useful to assess the efficiency of the intelligent fulfillment process
include number of stockouts, inventory turnover and, with a focus on logistic efficiency, mean
time between orders. In this context, the inventory turnover measures the time spent by an item
in the warehouse, high levels of storage represent an undesirable condition because they
increase storage management costs.
• The case study is still in its piloting stage and it has not yet delivered quantitative business benefits.
Nonetheless, it is providing deep insights to the whole POS management and efficiency.
• Economic scalability issue has been recognized: cameras acquiring images have to be placed on
shelves in 250 POS (over 40 Km of cameras).
• Technical scalability issue: is the lambda architecture scalable with image processing? At what costs?

25. DataBench Project - GA Nr 780966
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