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Ten 2015 Technology Predictions

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Ten 2015 Technology Predictions

  1. 1. Ten 2015 Technology Predictions 1 Dr. Rado Kotorov Chief Innovation Officer, Information Builders Rick F. Van der Lans Independent Analyst, R20/Consultancy BV 15 January 2015
  2. 2. 1: IoT Gains Momentum  Prediction: IoT Will expand significantly in manufacturing, energy sector, healthcare, logistics, and other industries.  Fact: GE has generated $1 billion in incremental revenues form IoT and PaaS in 2013.  Action: IoT data can be cost effectively gathered in columnar high performance databases (like Hyperstage) for quick analysis, discovery, and experimentation. 2 Imagine the possibilities in a hyper-connected world…..
  3. 3. 1: IoT Gains Momentum  Connected devices include thermostats, cars, lights, alarms, shoe insoles  Car industry example  Currently each vehicle has 60-100 sensors  Future: 200 sensors per car  2020: Total 22 billion sensors used in the automotive industry  Cisco: 37 billion new things will be connected by 2020 3 Imagine the possibilities in a hyper-connected world…..
  4. 4. 2: Dealing with the data deluge  Prediction: Most data will be analyzed before it is fully processed and put into a data warehouse. Social and unstructured data are becoming more analytically accessible.  Fact: The volume of business data worldwide, across all companies, doubles every 1.2 years.  Action: Adopt a data lake approach – access and analyze first, and integrate later. Use search-BI tools to create apps for structured and unstructured data analytics. 4 Imagine when data flows in from everywhere…
  5. 5. 2: Dealing with the data deluge  Tools must allow us to sort and find quickly  Complex, multi-step architectures are not flexible enough  Integrated solutions required to avoid reinventing the wheel 5 Imagine when data flows in from everywhere…
  6. 6. 3: Apps and self-service  Prediction: Most companies will implement different self service for different stakeholders – tools for the analysts and apps for front line employees.  Fact: BI has a less than 30 percent adoption rate in the enterprise today.  Action: Turn analysis and insights into custom InfoApps for on-the- job decision support. 6 Analysis and insights create opportunities! Operational apps create value by changing behavior!
  7. 7. 3: Apps and self-service  Self-Service for the masses  Self-service is moving upstream and must move downstream 7 Analysis and insights create opportunities! Operational apps create value by changing behavior!
  8. 8. 4: The analytics skills gap  Prediction: Companies will not be able to fill the skill gap. Therefore, CDOs and CAOs will try to commoditize analytics.  Fact: The demand for people with deep analytical skills is 10 times greater than supply.  Action: Commoditize analytics with infoapps and appstore like portals for employees. 8 Finding and hiring good data scientists…
  9. 9. 4: The analytics skills gap  Data is still considered a by- product  Data is produced for internal consumption only  Data must be regarded as a key product 9 Finding and hiring good data scientists…
  10. 10. 5: Machine learning  Prediction: To bridge the skills gap and to cope with highly dimensional data deluge companies will adopt machine learning  Fact: IBM Watson is here and ready for business  Action: Use machine learning in combination with data discovery to explore the field and provide faster time to market analytics 10 “Robots will be smarter than humans within 15 years, Google’s new chief on artificial intelligence has claimed.”
  11. 11. 5: Machine learning  Many BI systems only do reporting  ROI of reporting hard to calculate  Analytics is the way to go 11 “Robots will be smarter than humans within 15 years, Google’s new chief on artificial intelligence has claimed.”
  12. 12. 6: Master data management (MDM) 12 The quest for the golden record…  Prediction: The implementation cycles for MDM will shrink drastically from a couple of years to a few months with new and innovative approaches.  Fact: Miscoding and billing errors from doctors and hospitals totaled $20 billion in USA.  Fact: The average billion-dollar company is losing $130 million a year due to poor data management.  Action: Adopt an MDM platform with built in templates, wizards & best practices approach.
  13. 13. 6: Master data management (MDM) 13 The quest for the golden record…  MDM will only be a success if it’s setup in a flexible way, technologically and organizationally
  14. 14. Vote: How successful is your MDM Strategy? 14
  15. 15. 7: Data warehouse decline  Prediction: Unmodelled data analytics will grow due to competitive pressure. NoSQL, Columnar and in-memory offer alternatives to DW for many use cases.  Fact: Relational databases still dominate the market, but 30% to 35% of enterprises have invested in big data. Is it a tipping point?  Action: Conduct powerful analytics against columnar, in- memory, and Hadoop using standard query and analysis tools. 15 Imagine how quickly data can be analyzed if data modeling and schemas were not necessary….
  16. 16. 7: Data warehouse decline  The future is for the Logical Data Warehouse  Multiple data sources using different storage technologies together forming one logical database  Big data is too big to move 16 Imagine how quickly data can be analyzed if data modeling and schemas were not necessary….
  17. 17. 8: Tech gets personal  Prediction: The benefits of predictive analytics are great, but many companies will be lured to buy easy to use tools, ignore the pitfalls, and fail.  Fact: Deloitte research shows more than 60% of companies have experienced project failure.  Action: Implement verification processes and commoditize analytics with expert certified InfoApps. 17 Is your prediction scientifically sound?
  18. 18. Vote: What percentage of your users are accessing BI on mobile devices? 18
  19. 19. 9: Mobile workforce  Prediction: Gartner predicts that over 50% of BI users will be mobile users.  Fact: BI Scorecard: “BI adoption as a percentage of employees remains flat at 22%, but companies that have successfully deployed mobile BI show the highest adoption at 42% of employees.”  Action: Offer self-service BI with an appstore like portal and InfoApps. 19 If BI and analytics could be downloaded from an appstore?
  20. 20. 9: Mobile workforce  The ROI of mobile analytics is not clear  Mobile analytics and consumer-driven analytics could become a marriage made in heaven 20 If BI and analytics could be downloaded from an appstore?
  21. 21. Vote: What percentage of your users do you think will be accessing BI on mobile devices in 2 years time? 21
  22. 22. 10: The CIO transformed  Prediction: Successful CIOs will transform their roles into business leadership roles and eventually become CEOs.  Fact: Of 384 hospitals only one selected the CIO as the next CEO in 2014.  Fact: GE CEO says, “Every company will be a software company.”  Action: Use software to transform processes, organizational culture, customer facing experience, and to monetize data. 22 The rise of the techno-leader
  23. 23. 10: The CIO transformed  More in-depth knowledge of technology needed on c-level  What can we learn from the CEOs of Google, Facebook, and Twitter? 23 The rise of the techno-leader
  24. 24. 24 Discussion
  25. 25. Further Resources Blog post: Gartner’s 2015 Tech Trends Lead To Pervasive BI Webinar: Big Data + Enterprise Data = Big Information, 15 January 2015, 14.00 GMT / 15:00 CET 25
  26. 26. Questions? 26 Rick F. van der Lans, R20/Consultancy BV @rick_vanderlans Rado Kotorov, Information Builders @rado_kotorov
  27. 27. View a recording of the webinar online here. 27

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