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Building a Bridge between
Technical and Business
Benchmarking
By Gabriella Cattaneo, Richard
Stevens, IDC
BDVe – Databench Webinar, October 9, 2018
10/10/2018 DataBench Project - GA Nr 780966 1
10/10/2018 DataBench Project - GA Nr 780966 2
Main Activities
• Classify the main use cases of
BDT by industry
• Compile and assess technical
benchmarks
• Perform economic and market
analysis to assess industrial
needs
• Evaluate business
performance in selected use
cases
Expected Results
• A conceptual framework linking
technical and business
benchmarks
• European industrial and
performance benchmarks
• A toolbox measuring optimal
benchmarking approaches
• A handbook to guide the use of
benchmarks
Building a bridge between technical and
business benchmarking
WP2’s role in Databench Workflow
3© IDC
3© IDC
Technical BenchmarksBusiness Benchmarks
Where Magic Happens
10/10/2018 DataBench Project - GA Nr 780966 4
DataBench Project - GA Nr 780966 5
How to link technical and business benchmarking
WP2 – ECONOMIC MARKET AND
BUSINESS ANALYSIS
WP4 – EVALUATING BUSINESS
PERFORMANCE
Top-down
Bottom-up
• Focus on economic and industry analysis
and the EU Big Data market
• Classify leading Big Data technologies
use cases by industry
• Analyse industrial users benchmarking
needs and assess their relative
importance for EU economy and the
main industries
• Demonstrate the scalability, European
significance (high potential economic
impact) and industrial relevance
(responding to primary needs of users)
of the benchmarks
USE CASES = Typologies of technology
adoption in specific application domains
and/or business processes
▪ Focus on data collection and
identification of use cases to be
monitored and measured
▪ Evaluation of business
performance of specific Big
Data initiatives
▪ Leverage Databench toolbox
▪ Provide the specific industrial
benchmarks to WP”
▪ Produce the Databench
Handbook, a manual
supporting the application of
the Databench toolbox
10/10/2018
Leading Business Use Cases by Industry
10/10/2018 DataBench Project - GA Nr 780966 6
Finance (exc.
insurance)
Fraud prevention and detection
Customer profiling, targeting, and optimization of
offers
Portfolio and risk exposure assessment
Accom. Optimize price strategies Cross-sell and upsell at point of sale Store location (either physical or digital)
#1 #2 #3
Manuf. Analysis of operations-related data
Factory automation, digital factory for lean
manufacturing
Analysis of machine or device data
Telecom Network analytic and optimization Network investment planning Customer scoring and churn mitigation
Transport Logistics optimization Customer analytics and loyalty marketing
Prevent and respond to public security
threats
Oil&Gas Maintenance management Sensor-based pipeline optimization Natural resource exploration
Prof. Services
Customer profiling, targeting, and optimization of
offers
Ad targeting, analysis, forecasting, and
optimization
Predictive maintenance
Education Student recruiting Back-office process optimization Course planning and costing
Health
Compliance check and reporting on quality of
care
Illness/disease progression
Organization resources utilization and
turnover
Media Customer scoring Audience analysis Marketing optimization
Utilities Customer behavior and interaction analysis Field service optimization Energy consumption analysis
Retail/
Wholes.
Optimize price strategies and price
management
Increase productivity and efficiency of
DCs/warehouses
Customer data security, and privacy (fraud
prevention)
Govt. Personalize citizen services Increase efficiency of internal processes Prevent and respond to natural disaster
Source: IDC's European Vertical Markets Survey, November
2016 (n = 1,872)
Preliminary Analysis of KPIs and Benchmarks
10/10/2018 DataBench Project - GA Nr 780966 7Source: Polimi, October 2018
INDUSTRY USE CASE BUSINESS KPI TYPE OF DATA TECHNICAL BENCHMARKING AREA
Agriculture
Yield monitoring and
prediction
Revenue Growth Image (satellite)
data
Limited time to process
Very big data
Quality of data (missing values, outliers …)
Banking
Fraud prevention and
detection
Cost Reduction Transactional Data Near real time processing paradigm
Business or
Professional Services,
excluding IT Services
Automated customer
service
Revenue Growth
Time Efficiency
Text Data
Natural Language Processing (NLP) quality
benchmarking
Energy
Energy consumption
analysis and prediction
Cost Reduction IoT Data Real time streaming data processing
Healthcare Quality of care optimization Product/Service Quality IoT Data Real time streaming data processing
Manufacturing
Inventory and service parts
optimization
Time Efficiency IoT Data Real time streaming data processing
Media Social media analytics Customer Satisfaction Linked Data
Graph-processing platforms benchmarking (linked
data).
Retail Trade Targeting Revenue Growth
Transactional Data
Text Data
IT architectural cost optimization
Transport and
Logistics
Connected vehicles
optimization
Product/Service Quality IoT Data Real time streaming data processing
Utilities
Field service optimization
Cost Reduction IoT Data Real time streaming data processing
Early Results from the
Databench Business users
Survey
10/10/2018 DataBench Project - GA Nr 780966 8
10/10/2018 DataBench Project - GA Nr 780966 9
Source: Databench Survey, IDC, Interim results, 401 interviews, October 2018
Users recognize the relevance of business
benchmarking…
41%
37%
22%
Respondents by Type of Use of BDA
UsingEvaluating
Piloting
DRAFT
Big Data is Worth the Investment
© IDC
10
Nearly 90% of businesses saw moderate or
high levels of benefit in their Big Data
implementation
Source: IDC DataBench Survey, October 2018 (n=401 European Companies)
Adopting Big Data Solutions increased profit
and revenue by more than 8%, and reduced
cost by nearly 8%
DRAFT
© IDC
Big Data implementation focus
11
Source: IDC DataBench Survey, October 2018 (n=401 European Companies)
Overall, Big Data preference is for growth – with new products and
markets – rather than improve efficiency and save cost
Quality and Customers are the
two most important KPI’s
But implementation is balanced
across all business units
DRAFT
Big Data – Key Use Cases
© IDC 12
Source: IDC DataBench Survey, October 2018 (n=401 European Companies)
Final results to be presented at the European
Big Data Value Forum and in the Databench
report due in December 2018
DRAFT
• Provide methodologies and tools to help assess and
maximise the business benefits of BDT adoption
• Provide criteria for the selection of the most appropriate
BDTs solutions
• Provide benchmarks of European and industrial significance
• Provide a questionnaire tool comparing your choices and
your KPIs with your peers
DataBench Project - GA Nr 780966 13
What can DataBench do for you?
What we want from you?
▪ Expression of interest to become a case study and
monitoring your Big Data KPIs
▪ Answer a survey on your Big Data experiences
10/10/2018
gcattaneo@idc.com
rstevens@idc.com
Evidence Based Big Data Benchmarking to
Improve Business Performance

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BDVe Webinar Series: DataBench – Benchmarking Big Data. Gabriella Cattaneo. Tue, Oct 9, 2018

  • 1. Building a Bridge between Technical and Business Benchmarking By Gabriella Cattaneo, Richard Stevens, IDC BDVe – Databench Webinar, October 9, 2018 10/10/2018 DataBench Project - GA Nr 780966 1
  • 2. 10/10/2018 DataBench Project - GA Nr 780966 2 Main Activities • Classify the main use cases of BDT by industry • Compile and assess technical benchmarks • Perform economic and market analysis to assess industrial needs • Evaluate business performance in selected use cases Expected Results • A conceptual framework linking technical and business benchmarks • European industrial and performance benchmarks • A toolbox measuring optimal benchmarking approaches • A handbook to guide the use of benchmarks Building a bridge between technical and business benchmarking
  • 3. WP2’s role in Databench Workflow 3© IDC 3© IDC Technical BenchmarksBusiness Benchmarks
  • 4. Where Magic Happens 10/10/2018 DataBench Project - GA Nr 780966 4
  • 5. DataBench Project - GA Nr 780966 5 How to link technical and business benchmarking WP2 – ECONOMIC MARKET AND BUSINESS ANALYSIS WP4 – EVALUATING BUSINESS PERFORMANCE Top-down Bottom-up • Focus on economic and industry analysis and the EU Big Data market • Classify leading Big Data technologies use cases by industry • Analyse industrial users benchmarking needs and assess their relative importance for EU economy and the main industries • Demonstrate the scalability, European significance (high potential economic impact) and industrial relevance (responding to primary needs of users) of the benchmarks USE CASES = Typologies of technology adoption in specific application domains and/or business processes ▪ Focus on data collection and identification of use cases to be monitored and measured ▪ Evaluation of business performance of specific Big Data initiatives ▪ Leverage Databench toolbox ▪ Provide the specific industrial benchmarks to WP” ▪ Produce the Databench Handbook, a manual supporting the application of the Databench toolbox 10/10/2018
  • 6. Leading Business Use Cases by Industry 10/10/2018 DataBench Project - GA Nr 780966 6 Finance (exc. insurance) Fraud prevention and detection Customer profiling, targeting, and optimization of offers Portfolio and risk exposure assessment Accom. Optimize price strategies Cross-sell and upsell at point of sale Store location (either physical or digital) #1 #2 #3 Manuf. Analysis of operations-related data Factory automation, digital factory for lean manufacturing Analysis of machine or device data Telecom Network analytic and optimization Network investment planning Customer scoring and churn mitigation Transport Logistics optimization Customer analytics and loyalty marketing Prevent and respond to public security threats Oil&Gas Maintenance management Sensor-based pipeline optimization Natural resource exploration Prof. Services Customer profiling, targeting, and optimization of offers Ad targeting, analysis, forecasting, and optimization Predictive maintenance Education Student recruiting Back-office process optimization Course planning and costing Health Compliance check and reporting on quality of care Illness/disease progression Organization resources utilization and turnover Media Customer scoring Audience analysis Marketing optimization Utilities Customer behavior and interaction analysis Field service optimization Energy consumption analysis Retail/ Wholes. Optimize price strategies and price management Increase productivity and efficiency of DCs/warehouses Customer data security, and privacy (fraud prevention) Govt. Personalize citizen services Increase efficiency of internal processes Prevent and respond to natural disaster Source: IDC's European Vertical Markets Survey, November 2016 (n = 1,872)
  • 7. Preliminary Analysis of KPIs and Benchmarks 10/10/2018 DataBench Project - GA Nr 780966 7Source: Polimi, October 2018 INDUSTRY USE CASE BUSINESS KPI TYPE OF DATA TECHNICAL BENCHMARKING AREA Agriculture Yield monitoring and prediction Revenue Growth Image (satellite) data Limited time to process Very big data Quality of data (missing values, outliers …) Banking Fraud prevention and detection Cost Reduction Transactional Data Near real time processing paradigm Business or Professional Services, excluding IT Services Automated customer service Revenue Growth Time Efficiency Text Data Natural Language Processing (NLP) quality benchmarking Energy Energy consumption analysis and prediction Cost Reduction IoT Data Real time streaming data processing Healthcare Quality of care optimization Product/Service Quality IoT Data Real time streaming data processing Manufacturing Inventory and service parts optimization Time Efficiency IoT Data Real time streaming data processing Media Social media analytics Customer Satisfaction Linked Data Graph-processing platforms benchmarking (linked data). Retail Trade Targeting Revenue Growth Transactional Data Text Data IT architectural cost optimization Transport and Logistics Connected vehicles optimization Product/Service Quality IoT Data Real time streaming data processing Utilities Field service optimization Cost Reduction IoT Data Real time streaming data processing
  • 8. Early Results from the Databench Business users Survey 10/10/2018 DataBench Project - GA Nr 780966 8
  • 9. 10/10/2018 DataBench Project - GA Nr 780966 9 Source: Databench Survey, IDC, Interim results, 401 interviews, October 2018 Users recognize the relevance of business benchmarking… 41% 37% 22% Respondents by Type of Use of BDA UsingEvaluating Piloting DRAFT
  • 10. Big Data is Worth the Investment © IDC 10 Nearly 90% of businesses saw moderate or high levels of benefit in their Big Data implementation Source: IDC DataBench Survey, October 2018 (n=401 European Companies) Adopting Big Data Solutions increased profit and revenue by more than 8%, and reduced cost by nearly 8% DRAFT
  • 11. © IDC Big Data implementation focus 11 Source: IDC DataBench Survey, October 2018 (n=401 European Companies) Overall, Big Data preference is for growth – with new products and markets – rather than improve efficiency and save cost Quality and Customers are the two most important KPI’s But implementation is balanced across all business units DRAFT
  • 12. Big Data – Key Use Cases © IDC 12 Source: IDC DataBench Survey, October 2018 (n=401 European Companies) Final results to be presented at the European Big Data Value Forum and in the Databench report due in December 2018 DRAFT
  • 13. • Provide methodologies and tools to help assess and maximise the business benefits of BDT adoption • Provide criteria for the selection of the most appropriate BDTs solutions • Provide benchmarks of European and industrial significance • Provide a questionnaire tool comparing your choices and your KPIs with your peers DataBench Project - GA Nr 780966 13 What can DataBench do for you? What we want from you? ▪ Expression of interest to become a case study and monitoring your Big Data KPIs ▪ Answer a survey on your Big Data experiences 10/10/2018
  • 14. gcattaneo@idc.com rstevens@idc.com Evidence Based Big Data Benchmarking to Improve Business Performance