1. Evidence Based Big Data Benchmarking to Improve Business Performance
Benchmarking data session
BDVe Meetup, Sofia
May 15, 2018
Gabriella Cattaneo, IDC

2. Building a bridge between technical and business benchmarking
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
© IDC 2
© IDC

3. Workflow and Partners Roles
© IDC 3

4. Where Magic Happens

5. Use Cases are the key for matching benchmarks
Why?
Top down:
Classification and measurement of frequency of use cases by industry and business process through desk and field research (2018)
Bottom up:
Case studies will focus on specific use cases and measure business impacts (KPIs) correlated with specific technologies
Linking the two approaches:
The case studies KPIs can be scaled up to industry and economy level
WP2 – ECONOMIC MARKET AND BUSINESS ANALYSIS
Top-down
USE CASES = Typologies of technology adoption in specific application domains and/or business processes
Bottom-up
WP4 – EVALUATING BUSINESS PERFORMANCE
15/05/2018
© IDC

6. Example: Leading Use Cases by Industry, 2016
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
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
IDC Italia:
Atos:
Sintef:
Polimi:
JSI:
Frankfurt:
Source: IDC's European Vertical Markets Survey, November 2016 (n = 1,872)
15/05/2018
DataBench Project - GA Nr

7. Survey of a representative sample of EU enterprises
Criteria
Europe
Sample size
800, with 100 in each of UK, France, Germany, Italy, Spain, the Netherlands, Nordics (Sweden, Denmark), and CEE (Czech Rep., Poland, Hungary, Romania, Estonia,)
Method
20-min telephone (CATI) survey. Local languages in most countries
Target companies
50+ full time employees. All IDC verticals, good balance by vertical, no hard quotas
Target repsondents
Big data and analytics decision makers and influencers. Manager-level or higher.
Quotas
Soft equal quotas by , ,1000+ size bands. In CEE sample will be skewed towards the smaller companies.
Screener
Companies must be using/piloting/planning to use or consider using big data and analytics solutions .
Verticals considered
Banking, Insurance, other financial services; Telecom; Media; Transport; Manufacturing; Energy (Utilities and oil & Gas); Retail and Wholesale; Professional Services; Government; Education and Healthcare
Margin of error  
Where n=100 the margin of error is 9.8% and for n=800 -the total sample - it is 3.5%.
15/05/2018
DataBench Project - GA Nr

8. Criteria of Classification of Use Cases
Main Classification Criteria
Main Parameters
Type of Stakeholder
Company size (large, medium, small); Industry sector; Country ; Role in the data market (Vendor/ User/ Data entrepreneur); Level of maturity in the use of Big Data
Type of Technology
Technology solution; Application domain
Type of business process
General: Operations/ Marketing and Sales / Maintenance and logistics/ Human resources / Management and Finance;
Approach to Big Data: Data driven business process vs Digital transformation oriented business process
Stage of development
Fully operational vs Emerging, pilot
Level of integration into business processes
High (real time) - Medium (mixed) - Low (batch)
Type of Data
Structured data / Time series and IoT / Geospatial and Temporal / Media. Image, Audio, Text and Genomics / Web, Graph and Meta
15/05/2018
DataBench Project - GA Nr

9. Economic and Market Analysis
Methodology
Approach
Output
Del
Economic analysis
Use economic indicators to measure the “footprint” of BDTs in European industries and relative relevance by industry and economic size
Preliminary benchmarks of European Economic significance
D.2.2
M12
Big Data Technologies (BDT) Market analysis
Classification of use cases by industry
Monitoring and measurement of frequency of use cases by industry and business process through desk and field research
Preliminary benchmarks of industrial significance
M.12
Big Data Technologies (BDT) Market analysis – data collection
Monitoring and measurement of actual and emerging needs of industrial users in terms of benchmarking the business impacts of BDT
Mapping of use cases by industry and business process
Implementation of users’ survey
Assessment of actual and emerging needs of industrial users
D.2.3
M.18
Economic and market analysis
Finalisation of European and industrial significance benchmarks
Assessment of potential scalability and economic impacts of BDT business benchmarks resulting from market analysis and case studies
Benchmarks of European and industrial significance
D.2.4
M.24
Business analysis
Identification and preliminary classification of KPIs of business impacts of BDT - Criteria of selection of case studies sample - Approach to case study evaluation
Input to assess preliminary benchmarks
Business evaluation (WP4)
Evaluation of business performance in specific case studies
Results feed into the assessment of final benchmarks
Input used by D.2.4
15/05/2018
DataBench Project - GA Nr

10. DataBench Project - GA Nr 780966
15/05/2018
DataBench Project - GA Nr