Big Data analytics - business opportunities and challenges.

1. Big Data Analytics –Business Opportunities and Challenges 24.9.2014, Espoo Petteri Alahuhta, @PetteriA

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24/09/2014
Big Data in Hype-Cycle (Gartner)
@PetteriA
Internet of Things
Big Data Analytics
Big Data Tools

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24/09/2014
BIG DATA – ”high volume, velocity and/or variety information assets that demand cost- effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” (Gartner, 2012)
@PetteriA

4. 6
24/09/2014
Big Data is about increasing number of V’s
Volume – Data size
Velocity – Speed of Change
Variety – Different forms of data sources
Veracity – Uncertainty of data
Value –Transforming data into new value
Visualization – visualizing the data for insights
Validity
Venue
Vocabulary
Vagueness
@PetteriA
MB
GB
TB
PB
Batch
Periodic
Near Real Time
Real Time
Data
Volume
Data
Variety
Data
Velocity

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Large part of available information is not well leveraged
Machine data (IoT)
Social data
Databases, BI-data
@PetteriA
In effective use
Ineffective use
Business applications, Master data, Data Warehouse, data cubes, Business Intelligence
Unstructured data
semi-structured data
Open data (struct. & semi-struct.),
API’s
Sensors data streams

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24/09/2014
Data is Raw Material – Tools and people are the key to Insights
@PetteriA
Data
Tools / People
Insights
Structured - Data in rigid formats. E.g. Databases
Unstructured - No particular pattern/format. E.g. texts, video
Semi-structured –Unstructured data with a format. E.g. Twitter- feeds, tags in videos
Differentiated – Proprietary data of Market or business – in- house or 3rd party data
Big - Beyond current processing capabilities
Algorithms - Rules or equations derived from analysis of data
Analytics - Statistical description that
Provides overall understanding of the patterns in the data
Tools help to process raw material
People to produce insights from raw material
Industry - Expertise in the economic production of a product or service, e.g. Machinery sector
Discipline - Expertise in the development of processes taht can be applied accross cariety of industries e.g supply chain
Technical – Expertise in the development of processes requiring knowledge of math and science. E.g. Data science

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Adding value through analytics
Descriptive Analytics
Predictive Analytics
Prescriptive
Analytics
Value
Complexity
What
happened? And Why?
What will
happen?
How can we
make it happen?
Hindsight
Insight
Foresight
@PetteriA

8. 13
24/09/2014
Big Data –Market Drivers and Restrains
Key Market Drivers
Key Restrains
Hyper connectivity and need for turning data to intelligence boost the need for solutions standardize visualization, analysis and reporting of data
Shortage of talent fro analytics and technical skills
Data-driven real-time insights provide competitive advantage
Legacy infrastructure and lack of Big Data implementation strategy
Availability of open source tools for Big Data computing & processing (e.g. Hadoop)
Significant investments in Big Data analytics required
Examples from predictive and prescriptive analytics in different use cases increase demand for replicating them in different sectors
Big Data deployments remain underutilized because fully leveraging them would require process and business model changes
@PetteriA
Modified from Frost Sullivan

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24/09/2014
Examples of Big Data Use Cases
@PetteriA
•Customer segmentation
•Behavior analytics
•Affinity analysis
•Customer service improvements
•Pricing analysis
•Campaign management
Customer Insights
•Fraud detection
•Cybersecurity
•Defense
•Trading analysis
•Insurance analytics
•Real estate
Security and risks
•Inventory
•Network analysis
•System performance
•Retailing
Resource Optimisation
•Sales productivity
•Operational efficiency
•Internal process improvements
•Human resource planning & mgmt
Productivity improvements

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24/09/2014
Big Data Trends
Technology
Democratizing Big Data
Rise of Machine Learning
Democratizing of Analytics
Real-time analytics
Hadoop
Context and Sentiment Analysis
Automated machine learning
Market
Big Data, Big Priority
Data Governance
Faster Deployment on the cloud
Industry-Specific Solutions
Analytics for SMB’s
More C’s at the Top
@PetteriA

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24/09/2014
Challenges VTT is addressing
Creating value from big data
Effectively management and analysis of huge volumes of varying data from different sources
Cyber and information security
@PetteriA

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24/09/2014
Our areas of Expertise in Big Data
Independent digital service design
Capturing value from real-time analytics
New customer offering from web based services
Data science expertise
Visualization of data
Resource restricted data- analytics
Real-time data- analytics
Distributed data fusion
Independent digital service engineering
Security testing and analyses
Security metrics, testing and risk analyses
Security solutions for embedded systems
Acquiring data
Information integration
Data management
Creating value from big data
Data Science & Analytics
Information Management
Cyber and Information Security
@PetteriA

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Final Remarks
There are surprising and valuable insights hiding in the data on hand and the new data that are becoming available
Insights can be converted into cost-reduction and revenue-enhancing in business processes
Succesful showcases of Big Data analytics are still rare and solutions are unmature. => Experiment, Start small, Measure the impact, Build on good results, Experiment again
@PetteriA

14. TECHNOLOGY FOR BUSINESS petteri.alahuhta@vtt.fi +358 40 708 4326 @petteria