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

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

Workflow and Partners Roles © IDC 3

Where Magic Happens

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

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: http://idcitalia.com/ Atos: https://atos.net/ Sintef: https://www.sintef.no/ Polimi: www.polimi.it JSI: www.ijs.si Frankfurt: www.uni-frankfurt.de Source: IDC's European Vertical Markets Survey, November 2016 (n = 1,872) 15/05/2018 DataBench Project - GA Nr 780966

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 50-499,500-999,1000+ 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 780966

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 780966

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 780966

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