Big data and analytics - Petteri Alahuhta
2 likes1,350 views
This document discusses big data analytics and its opportunities and challenges. It defines big data and explains the increasing number of "V's" that characterize big data, such as volume, velocity, variety, and veracity. It also outlines different types of analytics (descriptive, predictive, prescriptive) and how they add value. Additionally, it identifies key market drivers for big data as well as restraining factors. Examples of big data use cases are provided across areas like customer insights, security and risks, and resource optimization.
Big data and analytics - Petteri Alahuhta
- 1. Big Data Analytics –Business Opportunities and Challenges 24.9.2014, Espoo Petteri Alahuhta, @PetteriA
- 2. 3 24/09/2014 Big Data in Hype-Cycle (Gartner) @PetteriA Internet of Things Big Data Analytics Big Data Tools
- 3. 5 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
- 5. 7 24/09/2014 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
- 6. 8 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
- 7. 11 24/09/2014 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
- 9. 16 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
- 10. 17 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
- 11. 19 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
- 12. 20 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
- 13. 21 24/09/2014 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