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The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutureofIT)

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Jan Henderyckx is the founder and managing partner of Inpuls cvba, a consulting company focussing on enabling data driven, compliant and sustainable business value creation. He talks about how to become data-centric and unlock the potential of compliant value creation

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The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutureofIT)

  1. 1. © All Intellectual Rights Reserved 2018 Inpuls cvba 1 THE FUTURE IS DATA-CENTRIC, unlocking the potential of compliant/trusted value creation 1 JAN HENDERYCKX INFORMATION STRATEGIST AVOIDING THE #FAKEDECISION PITFALLS
  2. 2. © All Intellectual Rights Reserved 2018 Inpuls cvba DISCLAIMER 2 EXAMPLES MAY LOOK FAMILIAR
  3. 3. © All Intellectual Rights Reserved 2018 Inpuls cvba 4 Managing Partner, Senior Advisor and Trainer with Inpuls cvba Publications Database Magazine, IDUG journal, CA journal, BMC journal, Information Seminars and workshops IT Works, Adept Events, IRMUK Involvement in non-profit initiatives § President of DAMA Belux Chapter (http://dama-belux.org) JAN HENDERYCKX YOUR PRESENTER CDMP,DGSP TWITTER/JANHENDERYCKX /INPULS_INFO LINKEDIN/JANHENDERYCKX YOUTUBE INPULS CHANNEL Making organisations Data Centric
  4. 4. © All Intellectual Rights Reserved 2018 Inpuls cvba AGENDA 9 • WELCOME • SETTING THE SCENE • EFFECTIVE INFORMATION STRATEGIES • TOWARDS A POLICY BASED APPROACH • REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE • CONCLUSIONS
  5. 5. © All Intellectual Rights Reserved 2018 Inpuls cvba FROM: DATA MART TO INSIGHT TO ACTION SETTING THE SCENE DATA PROPULSED? 10 Object Events M EASURE NEXT BEST ACTION A CT Object Events Events Events U NDERSTAND SUSTAINABLE TRUSTED INSIGHT?
  6. 6. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE 11 What is the definition of “right”
  7. 7. © All Intellectual Rights Reserved 2018 Inpuls cvba CAN DATA LEAD TO ACTION? 12 12 Value creation by detecting potential high LTV customers from sales transactions
  8. 8. © All Intellectual Rights Reserved 2018 Inpuls cvba CAN DATA LEAD TO ACTION? 13
  9. 9. © All Intellectual Rights Reserved 2018 Inpuls cvba CAN DATA LEAD TO ACTION? PRACTICAL EXAMPLES FROM THE FIELD 14
  10. 10. © All Intellectual Rights Reserved 2018 Inpuls cvba Clayton M. Christensen Harvard Business Review Press; 1st edition (May 1, 1997) Disruptive innovation is innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market leaders and alliances. The term was defined and phenomenon analyzed by Clayton M. Christensen beginning in 1995. THE DYNAMICS OF DISRUPTION
  11. 11. © All Intellectual Rights Reserved 2018 Inpuls cvba Non-linear correlation between investment/effort and revenue THE DYNAMICS OF DISRUPTION
  12. 12. © All Intellectual Rights Reserved 2018 Inpuls cvba THE DYNAMICS OF DISRUPTION © Rachel Botsman THE DYNAMICS OF DISRUPTION ?
  13. 13. © All Intellectual Rights Reserved 2018 Inpuls cvba THE FLIPSIDE OF THE COIN CAN DATA CAUSE PROBLEMS? 18 Shares down 1,8% Agenda 9
  14. 14. © All Intellectual Rights Reserved 2018 Inpuls cvba CAN DATA CAUSE PROBLEMS?THE FLIPSIDE OF THE COIN 19
  15. 15. © All Intellectual Rights Reserved 2018 Inpuls cvba THE FLIPSIDE OF THE COIN CAN DATA CAUSE PROBLEMS? 20
  16. 16. © All Intellectual Rights Reserved 2018 Inpuls cvba 21WHY BOTHER CYBER SECURITY / DATA PROTECTION = HOT TOPIC
  17. 17. © All Intellectual Rights Reserved 2018 Inpuls cvba 22WHAT IS NEW? PRIVACY = HOT TOPIC Current privacy legislation No strong enforcement or infringements New Rights for individuals New Obligations for companies Stronger enforcement of infringements Right to be forgotten Data portability right Protection of children profiling Location data Explicit consent Data protection assessments Documentation requirements Data protection officer Data breach notification Processor obligations Privacy by default & by design Heavy sanctions Shift burden of evidence Stronger agencies accountability Territorial data transfers Class actions
  18. 18. © All Intellectual Rights Reserved 2018 Inpuls cvba INCREASING REGULATORY PRESSURE 23 Regulators move from reports to data points!
  19. 19. © All Intellectual Rights Reserved 2018 Inpuls cvba SOME HISTORIC PERSPECTIVE Wall Street's Speed War High-Frequency Trading SPEED IS RELATIVE 1964 New York Stock Exchange installs IBM computer system; automated quotations appear in 1965. 1990 Datek introduces “the Watcher,” a PC-based program that capitalizes on split-second discrepancies in the big exchanges’ small-order trading systems. Anation of day traders is born.
  20. 20. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE ACTING ON DATA 25
  21. 21. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE ACTING ON DATA 26
  22. 22. © All Intellectual Rights Reserved 2018 Inpuls cvba FINTECH AND DATA SCIENCE OFFER NEW (DISRUPTIVE) OPPORTUNITIES ENHANCING PERFORMANCE AND ENABLING COSTS REDUCTION 27
  23. 23. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE RESULT ORIENTED? 28 Value from data is a VERB The key is not to OWN but to ACT
  24. 24. © All Intellectual Rights Reserved 2018 Inpuls cvba Data Excellence Ability to Measure Ability to Get Insight Ability to EngageAbility to Capture Ability to make trusted decision Ability to Sustain Ability to Comply SETTING THE SCENES CURRENT CHALLENGES 29 Short time-to-market of change requests (reduce backlog) Insight into available data across the organisation Easy access to available data Leveraging Data & Analytics skills and practices Easy access to reliable metadata Insight in data lineage and data usage Adequate (scope of) governance on data & info products Advance daily of information delivery Insight into Data Quality in relation to its purpose Fact: Automation has lead to reasonably mature administrative data and functions But Support for data propulsed automated decision taking and transversal data usage remains challenging
  25. 25. © All Intellectual Rights Reserved 2018 Inpuls cvba AGENDA 30 • WELCOME • SETTING THE SCENE • EFFECTIVE INFORMATION STRATEGIES • TOWARDS A POLICY BASED APPROACH • REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE • CONCLUSIONS
  26. 26. © All Intellectual Rights Reserved 2018 Inpuls cvba What is effective? BEING DATA PROPULSED IBM IBV 2013
  27. 27. © All Intellectual Rights Reserved 2018 Inpuls cvba What’s the bottleneck? BEING DATA PROPULSED
  28. 28. © All Intellectual Rights Reserved 2018 Inpuls cvba WHAT’S THE EFFECTIVE? 33 GOVERNING INNOVATION AND VALUE CREATION UNDERPIN YOUR INFORMATION STRATEGY WITH THE PROPER CAPABILITIES EMBED THE CHANGE IN THE ORGANISATION
  29. 29. © All Intellectual Rights Reserved 2018 Inpuls cvba ELEMENTS THAT CAN SCALE NON-LINEAR Capture data Run AlgorithmStore the data INFORMATION STRATEGY ELEMENTS Use the data
  30. 30. © All Intellectual Rights Reserved 2018 Inpuls cvba CAPTURE DATA DO WE HAVE THE DATA? 35 Ability to Measure Ability to Get Insight AAbility to Capture Ability to make trusted decision Ability to Sustain Ability to Comply I Think Do I have the data to back up my decision?
  31. 31. © All Intellectual Rights Reserved 2018 Inpuls cvba CAPTURE DATA DO I HAVE ENOUGH CONTEXT 36 CONTEXT MATTERS
  32. 32. © All Intellectual Rights Reserved 2018 Inpuls cvba USE THE DATA 37 Focus on the quality of your small data You can’t “statistical relevant” yourself out of the quality of master and reference data. Can we do self-service if we can’t trust the data?
  33. 33. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE DIGITAL TWIN 38 Really don’t need new slippers
  34. 34. © All Intellectual Rights Reserved 2018 Inpuls cvba SETTING THE SCENE DIGITAL TWIN 39 Expected LTV 10.000 € Real LTV x €
  35. 35. © All Intellectual Rights Reserved 2018 Inpuls cvba THE DATA, INFORMATION AND INSIGHT SPACE 40 Self Service insight will fail without proper Information – and data governance Gartner Says, By 2018, Half of Business Ethics Violations Will Occur Through Improper Use of Big Data Analytics COMPLIANCY COMMITMENT & TRANSPARENCY
  36. 36. © All Intellectual Rights Reserved 2018 Inpuls cvba STORE THE DATA 41 Diversify your data management platform: Value is not always proportional to value But how many tools do we really need?
  37. 37. © All Intellectual Rights Reserved 2018 Inpuls cvba GOVERNANCE MODEL Be cost effective GOVERNANCE MODEL Are we putting the right person on the right job?
  38. 38. © All Intellectual Rights Reserved 2018 Inpuls cvba Data Analytics Data Scientist Ad-hoc Data Sources Data Exploration Area Data Integration Governed Data Sources Information/Insight visualisation Put the TOOLS and PROCESSES in place to allow people to be EFFECTIVE and EFFICIENT GOVERNANCE MODEL
  39. 39. © All Intellectual Rights Reserved 2018 Inpuls cvba GOVERNANCE MODEL Stay true to your values GOVERNANCE MODEL Are we going to monetize the data?
  40. 40. © All Intellectual Rights Reserved 2018 Inpuls cvba GOVERNANCE MODEL Is MYdata special? AGILE DATA GOVERNANCE MODEL How much common ground do we need between producer and consumer? Can we survive with only SCHEMA ON READ?
  41. 41. © All Intellectual Rights Reserved 2018 Inpuls cvba GOVERNANCE MODEL INNovate and INDustrialise GOVERNANCE MODEL Is there any value if only a small percentage of your company can apply the outcome?
  42. 42. © All Intellectual Rights Reserved 2018 Inpuls cvba EFFECTIVE INFORMATION STRATEGIES IDENTIFY THE GAPS 47 47 Is the information correct? Do we have the data? Are we allowed to use the data? Can we use the information? Data Excellence Ability to Measure Ability to Get Insight Ability to EngageAbility to Capture Ability to make trusted decision Ability to Sustain Ability to Comply Short time-to-market of change requests (reduce backlog) Insight into available data across the organisation Easy access to available data Leveraging Data & Analytics skills and practices Easy access to reliable metadata Insight in data lineage and data usage Adequate (scope of) governance on data & info products Advance daily of information delivery Insight into Data Quality in relation to its purpose
  43. 43. © All Intellectual Rights Reserved 2018 Inpuls cvba EFFECTIVE INFORMATION STRATEGIES APPROACH 48 Business Capability What should we be able to do? Process Business Product How do we create business value Outcome Information Domain What information is required? Information How will we steer the process Insight GAPS ? CONNECTED CUSTOMERS CUSTOMER CENTRICITY COMPLIANCY COMMITMENT & TRANSPARENCY
  44. 44. © All Intellectual Rights Reserved 2018 Inpuls cvba AGENDA 49 • WELCOME • SETTING THE SCENE • EFFECTIVE INFORMATION STRATEGIES • TOWARDS A POLICY BASED APPROACH • REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE • CONCLUSIONS
  45. 45. © All Intellectual Rights Reserved 2018 Inpuls cvba 50 Governance
  46. 46. © All Intellectual Rights Reserved 2018 Inpuls cvba 51 Minimum Viable Protection Policy Most regulations apply to a specific type of data! Governance
  47. 47. © All Intellectual Rights Reserved 2018 Inpuls cvba COLLECT USE IMPACT ON VALUE CHAIN PRODUCER AND CONSUMER MODELS 52 PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Ingestion Usage
  48. 48. © All Intellectual Rights Reserved 2018 Inpuls cvba PRODUCER AND CONSUMER MODELS • Can they be loosely coupled? IMPACT ON VALUE CHAIN 53 DATA POINT Request Contract Ingestion ? Consent Lawfulness Send Marketing Message ? Lawfulness Contractual necessity CONSUMER Send Signed Contract Usage ?
  49. 49. © All Intellectual Rights Reserved 2018 Inpuls cvba PRODUCER AND CONSUMER MODELS • Can Schema on Read be applied without Policy on Ingest? IMPACT ON VALUE CHAIN 54 Policy on Read? Policy on Ingest? DATA POINT DATA POINT Need to assure sufficient metadata to apply the policies is captured at ingestion Need to have minimal schema to understand the applicability of the policies DATA POINT DATA POINT
  50. 50. © All Intellectual Rights Reserved 2018 Inpuls cvba IMPACT ON VALUE CHAIN PRODUCER AND CONSUMER MODELS 55 PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Ingestion Usage CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER Need to manage the entire supply chain, not just the first step
  51. 51. © All Intellectual Rights Reserved 2018 Inpuls cvba IMPACT ON VALUE CHAIN PRODUCER AND CONSUMER MODELS 56 Chief Data Officer Collateralized Debt Obligation PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Ingestion Usage CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER CDO
  52. 52. © All Intellectual Rights Reserved 2018 Inpuls cvba AGENDA 57 • WELCOME • SETTING THE SCENE • EFFECTIVE INFORMATION STRATEGIES • TOWARDS A POLICY BASED APPROACH • REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE • CONCLUSIONS
  53. 53. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 58 Need to move from ASSET centric to DATA centric DCAP
  54. 54. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 59 COLLECT STORE USEPREPARE ANALYSE Create Value
  55. 55. © All Intellectual Rights Reserved 2018 Inpuls cvba IMPACT ON VALUE CHAIN COLLECT, STORE, ANALYSE AND USE COLLECT STORE ANALYSE USE PREPARE 60 For What Purpose? Do we really need this? Does the data subject agree to the processing? Is the data correct? Preparation = processing How long can we keep the data? Can I trace back the origin? Can I link to the context? For What Purpose? Does the data subject agree to the processing? Is the data correct? Is the processor applying suitable safeguards? Does the data stay in the EER? Do we have consent? Is the data still PII? …. Can we apply profiling? Is there BIAS in my model? Is the data S-PII
  56. 56. © All Intellectual Rights Reserved 2018 Inpuls cvba PRIVACY ENHANCING AND/OR RISK REDUCING SOLUTIONSARCHITECTURAL REQUIREMENTS AND CONTROLS 61 Share External Delete Use Serve Archive Access External Attacker Attacker, Insider Store Policy Anonymise Data Receiver Pseudononymise Encrypt Share External Delete Use Serve Archive Access External Attacker Attacker, Insider Store Policy Anonymise Data Receiver Pseudononymise Encrypt Data Masking Data Access Pseudonymize Encrypt Anonymise
  57. 57. © All Intellectual Rights Reserved 2018 Inpuls cvba POLICY ON INGESTARCHITECTURAL REQUIREMENTS AND CONTROLS 62 DCAP: What data am I ingesting? COLLECT ISO 27000 Classify Label
  58. 58. © All Intellectual Rights Reserved 2018 Inpuls cvba POLICY ON INGESTARCHITECTURAL REQUIREMENTS AND CONTROLS 63
  59. 59. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 64
  60. 60. © All Intellectual Rights Reserved 2018 Inpuls cvba POLICY ON INGESTARCHITECTURAL REQUIREMENTS AND CONTROLS 65
  61. 61. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 66 COLLECT STORE USEPREPARE ANALYSE PRODUCER CONSUMERDATA POINT Ingestion Terms & Conditions DATA SHARING AGREEMENT DATA POINT Ingestion Terms & Conditions DATA SHARING AGREEMENT PRODUCER What Metadata should I capture? What quality rules should be applied ? PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Ingestion Usage CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER CONSUMER DATA SHARING AGREEMENT DATA POINT Usage PRODUCER
  62. 62. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 67 STORE PREPARE Provision PII? Purpose Specific Marts? Anonymous Data? Share External Delete Use Serve Archive Access External Attacker Attacker, Insider Store Policy Anonymise Data Receiver Pseudononymise Encrypt Pseudonymize Encrypt Anonymise DCAP: How do we reduce the risk?
  63. 63. © All Intellectual Rights Reserved 2018 Inpuls cvba ARCHITECTURAL REQUIREMENTS AND CONTROLS 68 USEANALYSE GDPR: Art. 30 Self-Service For What Purpose? Does the data subject agree to the processing? Ability to Filter records based on Processing grounds? Purpose Specific Marts? Anonymous Data? Include Consent in Data Access Layer? Data Masking Data Access
  64. 64. © All Intellectual Rights Reserved 2018 Inpuls cvba AGENDA 69 • WELCOME • SETTING THE SCENE • EFFECTIVE INFORMATION STRATEGIES • TOWARDS A POLICY BASED APPROACH • REFERENCE ARCHITECTURE FOR (BIG) DATA GOVERNANCE • CONCLUSIONS
  65. 65. © All Intellectual Rights Reserved 2018 Inpuls cvba CONCLUSION 70
  66. 66. © All Intellectual Rights Reserved 2018 Inpuls cvba •Use Policy based approach •Make the distinction between data- and information governance •Make the policy executable through capabilities •Have the proper metadata architecture to support the approach •Work risk based CONCLUSIONS 71
  67. 67. © All Intellectual Rights Reserved 2018 Inpuls cvba 72 Sustainable Information Readiness INFORMATION READINESS Gather Serve Dispose Maintain Govern Steer This Information is safe to run processes Define This Information is safe to take decisions Industrialise Refine ValidateProvision Define Hypothesis testing Governance
  68. 68. © All Intellectual Rights Reserved 2018 Inpuls cvba 73 Information Governance Information and Sustainable insight at your fingertips, of the right quality, at the right time, in the right context, using the right form, for the right person This Information is safe to take decisions This Information is safe to run processes Operational Insight INFORMATION READINESS Information Data Informatie Analyse Inzicht Creatie Performance Management Hypothese Toetsing Budgetting & Forecasting Metrics & Scorecards Purchasing Order Mgt. Vendor After Sales Customer Conclusion
  69. 69. 74 Q & A SESSION WE’LL BE ANSWERING QUESTIONS NOW Q A& THANKS FOR LISTENING YOUTUBE INPULS CHANNEL TWITTER/INPULS_INFO T +32 3 443 17 43 F +32 3 443 17 49 INFO@INPULS.EU DUWIJCKSTRAAT 17, 2500 LIER BELGIUM LINKEDIN/INPULS
  70. 70. © All Intellectual Rights Reserved 2018 Inpuls cvba 75
  71. 71. Contact us: https://www.linkedin.com/in/itworks https://twitter.com/itworks www.itworks.be Presented at: The Future of IT 20 September 2018 in Brussels

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