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Big Data Analytics

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Presentation by Ben Fountain at The Cyber Academy

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Big Data Analytics

  1. 1. Big Data Analytics Ben Fountain May 2016
  2. 2. 2 What is Big Data? “The dynamically linked super set of multiple significant scale discrete data sets.” -Oscar Wilde Characteristics include • Large volumes, typically adding terabytes of data daily • Aggregation of many historically discrete data sets • Dynamic links between the data sets Consequently • Any analysis is a point in time position
  3. 3. 3 Why care? • Better intelligence which can be leveraged in business, healthcare etc. to target efforts; • Cost of a DNA analysis has reduced by around 5 orders of magnitude since the process became possible, making personalised medicines a reality in the near future. • If you are investing in Big Data projects, the risk of data loss doesn’t necessarily change. The Volume of loss is potentially colossal with impacts that aren’t understood for an extended period. • Customers hold concerns about companies taking a role of Orwellian Big Brother.
  4. 4. 4 There’s no Best Practice…yet Breaches • Snowden showed that Government organisations with specific focus on security struggle to control Big Data and the associated risks. • Panama Papers showed that legal firms with an inherently high level of confidentiality in their practices struggle. Compliance issues • Harder to define the purpose of data exploration. • Big Data breaches tend to be….bigger. • Regulators will expect technology to be used equally to exploit and control Big Data.
  5. 5. 5 Key Controls for Big Data 1. Track all access that collects, views, and manipulates sensitive data, and ensure that it is encrypted at each point. 2. Encryption keys for sensitive data can't be stored at the same location as the data. 3. All access and processing of data must be logged. These logs must be subject to human and automatically monitoring. 4. Use automated scanning to constantly monitor systems for vulnerabilities and malware. 5. Monitor network egress for anomalies in traffic. 6. Create a number of "false flag“ records. Configure alerts and blocks to identify and prevent data breaches.
  6. 6. 6 How to use Big Data Analytics? Prescriptive Analytics How can we influence the future? Predictive Analytics How can we plan for the future? Diagnostic Analytics Why did this happen? Descriptive Analytics Do we know what happened? AnalyticsMaturity HistoricalAnalyticsProactiveAnalytics
  7. 7. 7 Police use of Predictive Analytics The California city of Fresno is just one of the police departments in the US already using a software program called “Beware” to generate “threat scores” about an individual, address or area. As reported by the Washington Post in January, the software works by processing “billions of data points, including arrest reports, property records, commercial databases, deep web searches and the [person’s] social media postings”. Photo: Nick Otto/For The Washington Post Quote :https://www.theguardian.com/technology/2016/feb/04/us-police-data-analytics-smart-cities-crime-likelihood-fresno-chicago-heat-list
  8. 8. 8 How to do it well Staff appropriately • Specialist Skills are in demand; • Big Data • Data Management • Have a plan to recruit and retain them! Data Quality • Big Data Leaders show maturity in data quality
  9. 9. 9 Final Point Big Data is a pre-requisite of the desire for better analytics, the desire to better understand. Of itself, its just a large data set waiting to breach.
  10. 10. 10 Points of contact Ben Fountain Senior Consultant M: +44 (0) 7545 503 311 E: ben.fountain@nccgroup.trust NCC Group Blogs https://www.nccgroup.trust/uk/about- us/newsroom-and-events/blogs/ TED Talks on Big Data https://www.ted.com/search?q=big+data
  11. 11. 11 Experiment “The dynamically linked super set of multiple significant scale discrete data sets.” -Oscar Wilde Well that’s a lie.
  12. 12. 12 NCC Locations Europe Manchester - Head Office Amsterdam Basingstoke Cambridge Copenhagen Cheltenham Delft Edinburgh Glasgow Leatherhead Leeds London Luxembourg Madrid Malmö Milton Keynes Munich Vilnius Zurich Australia Sydney North America Atlanta Austin Chicago Kitchener New York San Francisco Seattle Sunnyvale

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