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The value of big data analytics

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My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals

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The value of big data analytics

  1. 1. The Value from Big Data Analytics
  2. 2. Vint 2012
  3. 3. A Boeing 747 generates 30 TB of data in 1 flight
  4. 4. Vint 2012
  5. 5. EIU 2012
  6. 6. 16 www.isaca.org
  7. 7. Big Data—Characteristics Very large, distributed aggregations of loosely structured data – often incomplete & inaccessible: • Petabytes/Exabytes of data, • Millions/billions/trillions of records, • Loosely-structured, distributed data, • Flat schemas with complex interrelationships, • Coming from diverse sources, • Social networks, • Sensor networks, • Servers (files, logs, chats, video), • etc., • Often involving time-stamped events, • Applications (Transactional or Analytical)
  8. 8. Big Data—Characteristics
  9. 9. Fourth ‘v’: Validity = data quality & integration connected to collaborate data governance. Fifth ‘v’: Veracity = data integrity & the ability for an organization to trust the data and be able to confidently use it to make crucial decisions Big Data—Characteristics
  10. 10. Vint 2012
  11. 11. Big Data top requirements
  12. 12. Big Data project challenges
  13. 13. Big Data project difficulties
  14. 14. The dark side of Big Data
  15. 15. The dark side of Big Data
  16. 16. Big Data a priority??
  17. 17. Main management questions on big data: 1. Where should we store our data? 2. How are we going to protect our data? 3. How are we going to use our data safely & lawfully?
  18. 18. Core element on big data: The full life cycle of information needs to be considered!
  19. 19. Big data perception issue: Addressing 
 big data risk & concerns 
 can NOT be seen exclusively 
 as an IT exercise!
  20. 20. Key elements to take into account with Big Data 1. Data Quality: shift from “quest for perfect data” to “fit for use data” ●Add practicality ●Reduce time, cost and effort 2. Data Volume: shift from “shiny object syndrome” to “managed analytics” 3. Project Budgets: shift from “business determining needs” to “business & IT working together” 4. User Proficiency & Knowledge Transfer: shift from “specialist expert analytics know best” to 
 “business using tools & experts”
  21. 21. Data Quality Sub-dimensions
  22. 22. WEF 2012
  23. 23. Key risk with Big Data: privacy & security Privacy-related laws must be considered for Big Data projects. Responsibility for applying data privacy & security techniques falls on the individual capturing, using data & performing analysis: determine & document how data privacy & security issues will be addressed prior to a Big Data project. 1. What is the goal of the Big Data project? 2. How will this data be used? Anyone who will access & hold this data during analysis must adhere to appropriate data governance standards. 3. Who will be able to access, review and analyze this data? Which roles & responsibilities of users within organization will have access to personal & sensitive information. 4. How will this data be secured to prevent unauthorized access? 5. How will this data be updated?
  24. 24. Source: COBIT® 5, figure 15. © 2012 ISACA® All rights reserved.
  25. 25. Principles, policies &
  26. 26. Information
  27. 27. Processes
  28. 28. Corporate governance : ERM = COSO Organisational structure
  29. 29. Services, Infrastructure,
  30. 30. Culture, Ethics, Behaviour
  31. 31. People, Skills, Competencie
  32. 32. Big data control categories: 1. Approach & Understanding 2. Quality & Integrity 3. Confidentiality & privacy 4. Availability
  33. 33. Top 10 types of Big Data Analytical Usage
  34. 34. Role of Internal Audit in Managing Big Data Check the extent of data assets and deep dive into what all is available. Data that is redundant or unimportant may be identified and reduced. To manage data holdings effectively, an organization must first be aware of the location, condition and value of its research assets. Conducting a data audit provides this information, raising awareness of collection strengths and identifying weaknesses in data policies and management procedures. The benefits of conducting an audit for managing big data effectively are: Monitor holdings and avoid big data leaks. Data hacking, social engineering and data leaks are all concepts that plague a company – an audit can help a company identify areas where there is a possibility of leakage. Manage risks associated with big data loss and irretrievability. Data which is not structured and is lying untouched may never be retrieved; an audit can help identify such cases. Develop a big data strategy and implement robust big data policies. Big data requires robust management and proper structurization. Improve workflows and benefit from efficiency savings. Check where there are complex and time- consuming workflows and where there is a scope of improving efficiencies. Realize the value of big data through improved access and reuse to check if there are areas that have not been used in a while. Source: http://www.data-audit.eu/docs/DAF_briefing_paper.pdf
  35. 35. Complex Big Data Big Data Security Big Data Accessibility Big Data Quality Big Data Understanding Managing Big Data through Internal Audit Most companies collect large volumes of data but they do not have comprehensive approaches for centralizing the information. Internal audit can help companies manage big data by streamlining and collating data effectively. Issues of big data that internal audit can help mitigate: Maintaining effective data security is increasingly recognized as a critical risk area for organizations. Loss of control over data security can have severe ramifications for an organization, including regulatory penalties, loss of reputation, and damage to business operations and profitability. Auditing can help organizations secure and control data collected. Giving access to big data to the right person at the right time is another challenge organizations face. Segregation of Duties (SoD) is an important aspect that can be checked by an IA. The more data one accumulates, the harder it is to keep everything consistent and correct. Internal audit can check the quality of big data. Understanding and interpretation of big data remains one of the primary concerns for many organizations. Auditors can effectively simplify an organization’s data effectively. Source: http://www.acl.com/pdfs/wp_AA_Best_Practices.pdf, http:// smartdatacollective.com/brett-stupakevich/48184/4-biggest-problems-big-data
  36. 36. Conclusions
  37. 37. Big Data ! Big data = valuable asset & powerful tool with far reaching impacts. Big data initiatives need to have visibility at board level & executive level sponsors. ! Success of enterprises will depend on how they meet & deal with big data challenges & impacts. ! To harness value & deliver resilient and faster analytic solutions, enterprises must implement big data solutions using repeatable frameworks & processes coupled with good governance & risk management framework.
  38. 38. Any organization’s data = one of most valuable assets. Without a way to obtain, cleanse, organize and evaluate its data, an organization is left with a vast, chaotic pool of 0 & 1. Big Data results can be used to: ◦ improve business efficiencies; ◦ verify process effectiveness; ◦ identify areas of key risk, fraud, errors or misuse; ◦ influence business decisions. Maximum benefits from Big Data can be achieved for any organization if: ◦ the approach is aligned to the business, ◦ the (privacy & security) risks are managed, ◦ the process is effectively planned, designed, implemented, tested and governed. Big Data
  39. 39. “Even with infinite knowledge of past behaviour, we often won’t have enough information to make meaningful predictions about the future. 
 In fact, the more data we have, the more false confidence we will have… 
 The important part is to understand what our limits are and to use the best possible science to fill in the gaps. All the data in the world will never achieve that goal for us.” Peter Fader, co-director of the Wharton Customer Analytics Initiative at the University of Pennsylvania and Professor Marketing at Penn’s Wharton School of Business in MIT’s Technology Review, May 2012 http://www.technologyreview.com/news/427786/is-there-big-money-in-big-data/
  40. 40. Marc Vael
 President ISACA http://www.isaca.be/ For more information… marc@vael.net http://www.linkedin.com/in/marcvael @marcvael
  41. 41. www.isaca.org/knowledgecenter
  42. 42. 61
  43. 43. Big Data Governance questions 1. What principles, policies and frameworks are we going to establish to support the achievement of business strategy through big data? 2. Can we trust our sources of big data? 3. What structures and skills do we have to govern and manage IT? 4. What structures and skills do we have to govern big data privacy? 5. Do we have the right tools to meet our big data privacy requirements?
  44. 44. Big Data Governance questions 6. How do we verify the authenticity of the data? 7. Can we verify how the information will be used? 8. What decision options do we have regarding big data privacy? 9. What is the context for each decision? 10. Can we simulate the decisions and understand the consequences? 11. Will we record the consequences and use that information to improve our big data information gathering, context, analysis and decision-making processes?
  45. 45. Big Data Governance questions 12. How will we protect our sources, our processes and our decisions from theft and corruption? 13. Are we exploiting the insights we get from big data? 14. What information are we collecting without exposing the enterprise to legal and regulatory battles? 15. What actions are we taking that create trends that can be exploited by our rivals? 16. What policies are in place to ensure that employees keep stakeholder information confidential during and after employment?
  46. 46. Big Data Governance potential answers ! Senior management buy-in and evidence of continuous commitment ! Data anonymization/sanitization or de-identification ! Adequate, relevant, useful and current big data privacy policies, processes, procedures and supporting structures ! Appropriate data destruction, comprehensive data management policy, clearly defined disposal ownership and accountability ! Compliance with legal and regulatory data requirements ! Continuous education and training of big data policies, processes and procedures
  47. 47. Big data control category: 
 Approach & Understanding ! “Right tone at the top”. ! Data policy. ! Inventory of all data sources ! Identify vulnerabilities in the data flow including internal & external data sources, automated & manual processes. ! Data deficiency governance process for analysis of impact and probability, escalation to senior management where necessary, and a strategic or tactical resolution. ! Each vulnerability needs a data owner. ! Materiality criteria to identify most relevant data sets. ! Escalation path for data deficiency management.
  48. 48. Big data control category: 
 Confidentiality / Privacy ! Through the data risk management process, all sensitive data should be identified and appropriate controls put in place. Rules & regulations govern how sensitive data should be secured in storage & transit. ! Logical & physical access security controls are needed to prevent unauthorized access to sensitive data. This includes classic IT General Controls (password settings, masking or partially masking sensitive data, periodic user access review, firewalls, server room door security, server access logs, administrative access privileges and screen saver lockout.) ! Encryption technologies must be used to store & transfer highly sensitive information within + outside the enterprise.
  49. 49. Big data control category: 
 Quality ! Assess data against accuracy, reliability, completeness and timeliness criteria defined in the data policy & associated standards. ! Data sourced from third party: contractually bound process to gain confidence over data quality through an independent validation of data quality controls at the third party or through independent checks on any material data received. ! Ownership & responsibilities associated with each material data set should be assigned. Appropriate training should be rolled out to all relevant personnel to make them aware of their data-related responsibilities.
  50. 50. Big data control category: 
 Availability ! Reliable (tested) disaster recovery arrangements should be in place to ensure that data are available in accordance with ◦ data recovery point objective (RPO) criteria and ◦ recovery time objective (RTO) criteria defined in a business impact analysis (BIA).
  51. 51. Assurance considerations with Big Data By using the same risk-based approach used to determine the audit schedule, potential target areas may be identified in following areas: 1. Determine operational effectiveness of the current control environment. 2. Determine effectiveness of anti-fraud procedures & controls. 3. Identify business process errors. 4. Identify business process improvements & inefficiencies in the control environment. 5. Identify exceptions or unusual business rules. 6. Identify fraud. 7. Identify areas where poor data quality exists.

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