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Thriving in the world of Big Data

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Copy of presentation delivered at the CHASS 2015 National Forum in Melbourne (October 2015), The Council for Humanities, Arts and Social Sciences in Australia is the peak body supporting more than 75 member organisations in their relationships with Federal and State Government policy makers, Academia and the broader community within Australia.

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Thriving in the world of Big Data

  1. 1. Thriving in the world of big data Exploring the opportunities and challenges facing organisations. CHASS 2015 National Forum ROB LIVINGSTONE - Fellow, University of Technology, Sydney - Principal, Rob Livingstone Advisory Pty Ltd © All rights reserved. Rob Livingstone Advisory Pty Ltd.
  2. 2. Theme of this presentation Big Data….. • Easy to say • Hard to do
  3. 3. Easy to say….. • Analysts, vendors and the media are awash with impressive projected (and actual reported) compound annual growth rates (CAGR) of spending in Big Data. • Despite these compelling numbers, the reality is that the majority of organisations are still coming to terms with Big Data. “ ….only 13% of organizations have achieved full-scale production for their Big Data implementation.”* “…..only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”. * • These trends are also echoed elsewhere. *Capgemini 2015 - ‘Cracking the Data Conundrum: How Successful Companies Make Big Data Operational’ Hard to do……
  4. 4. Theme of this presentation 3 Fundamental issues: • Business + Big Data = Big Value? • Privacy + Big Data = Big Brother? • Cybercrime + Big Data = Big Risk?
  5. 5. Big Data - a brief 3600 Key attributes • Volume: Lots and lots of data – and growing exponentially. • Velocity: Data is time-sensitive. • Variety: Data in various forms, formats and from different sources. • Validity: Interpreted data has a sound basis in logic or fact. i.e. correct logical inferences. • Veracity: Conformity to facts, data quality, accuracy. • Value: Its importance, worth, or usefulness.
  6. 6. Big Data - a brief 3600 Other key considerations: • Visibility: Assured accessibility. 2 sides of the same coin: - Risk that critical data that is available but not visible – wrong decisions taken. - Included data that should have been excluded – wrong decisions taken. • Volatility: Managing change. - How adaptable is the information architecture to change? - How adaptable are your risk and governance processes in the face of change? - Recognise that change can come from anywhere.
  7. 7. Other key considerations: • Visibility: Assured accessibility. 2 sides of the same coin: - Risk that critical data that is available but not visible. - Included data that should have been excluded – wrong decisions taken • Volatility: Managing change. - How adaptable is the information architecture to change? - How adaptable are your risk and governance processes in the face of change? - Recognise that change can come from anywhere: Big Data - a brief 3600
  8. 8. Big Data - a brief 3600 Other key considerations: • Visibility: Assured accessibility. 2 sides of the same coin: - Risk that critical data that is available but not visible. - Included data that should have been excluded – wrong decisions taken • Volatility: Managing change. - How adaptable is the information architecture to change? - How adaptable are your risk and governance processes in the face of change? - Recognise that change can come from anywhere: • Vendors – Understand your need and their value proposition. - Eyes wide open! - Evidence!
  9. 9. Big Data – Opportunities for organisations • Depends on a range of factors such as: your industry, market, compliance and regulatory mandates, business strategy, risk profile / appetite. • Opportunities can include: Improved decision making (cognitive bias, accuracy,  timeliness, etc)  Internal cost, waste and inefficiencies  Time to take action  Revenue (e.g.: Monetising data, new information based value-added services or products, etc)  Model and simulate business scenarios with increased accuracy and speed (e.g. predictive analytics)
  10. 10. 1. Executive’s understanding: – Consistency of understanding at Executive level. Enterprise Big Data initiates are everyone’s job, not just IT or the vendor’s. 2. Scrutinise the business case carefully. Then repeat the process. – Expose and validate underlying business assumptions carefully. – If anyone says it’s easy, insist on a proof of concept then seek evidence 3. What’s worked over there, may not work here. – Other organisation’s success in Big Data will not automatically translate to yours. – Beware of the bias of perceived success: Organisations & vendors promote success stories, however failed projects may never see the light of day. Big Data – Challenges for organisations
  11. 11. Big Data – Challenges for organisations 4. Assess your internal IT capabilities: • Industry research* also indicates that executives’ perceptions of enterprise IT’s performance remains largely negative. – "...confidence in IT’s ability to support growth and other business goals is waning...." – "... Moreover, IT and business executives disagree strongly on the function’s overall priorities...“ 5. Got the technology strategy, architecture and operations sorted? * Arandjelovic, P., Bulin, L. and Khan, N. [2015], ‘Why CIOs should be business-strategy partners’, McKinsey & Company Internal? Hybrid? Outsource?
  12. 12. 6. Skills shortage in data science: – Expertise needed to understand the statistics, mathematics, computer and information science, operations research, etc. . – Must also be skilled in working with new technologies, data interpretation and visualisation as well as understand the business context. 7. IT architecture – Getting the range of data sources and formats to play nicely together – Ensuring security access control and identity management works! 8. Intra-organisational collaboration – Data interpretation and finding meaningful business insights cannot, for the most part, be done effectively within a functional silo. – Collaboration depends on organisation’s culture, business model, incentive schemes and structure. Big Data – Challenges for organisations
  13. 13. 9. Rate of technological change: – ‘Future-proofing’ is a challenge where underlying technologies have a short half-life. – ‘Outsourcing’ or public cloud solutions abstracts this challenge. 10. Protecting your Intellectual Property – Do you have any IP that needs protecting? Data analysis algorithms, schemas etc? – Ensuring security access control and identity management controls are secure. 11. Intra-organisational collaboration – Data interpretation and finding meaningful business insights cannot effectively be done within a functional silo. 12. Cybercrime / IT Security – Unfortunately data breaches are almost routine. – Cybercrime – State sponsored or ‘free market’ is a well resourced, highly skilled and innovative threat. Big Data – Challenges for organisations
  14. 14. • Big Data is all about extracting valuable insights from large volumes of diverse data – like mining for diamonds – and then taking appropriate action. • Can be a game changer if your organisation gets it right. • Be prepared, however to make some changes along the way, such as: – Become a collaborative organisation – redesign jobs, processes, structures. – Build a strong analytics capability internally. – Adjust incentive schemes to successful outcomes – Don’t assume ‘big-bang’ projects will succeed. Adopt lean startup principles to your Big Data initiatives. • Expect change and ensure you have effective governance in place. Big Data – In summary
  15. 15. Thank you W: www.rob-livingstone.com E: rob@rob-livingstone.com P: +61 2 8005 1972 M: +61 419 632 673 F: +61 2 9879 5004 Tw: @rladvisory ROB LIVINGSTONE - Fellow, University of Technology, Sydney - Principal, Rob Livingstone Advisory Pty Ltd © All rights reserved. Rob Livingstone Advisory Pty Ltd.

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