Big data analytics and innovation
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Big data analytics and innovation

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The ability to continuously innovate is crucial for business growth – and often necessary for survival. Leaders in an uncertain and fast-paced global business regularly seek innovation to revitalise ...

The ability to continuously innovate is crucial for business growth – and often necessary for survival. Leaders in an uncertain and fast-paced global business regularly seek innovation to revitalise rigid business models and processes. However, they are aware that ‘innovation is hard’ and fraught with uncertainty. I contend that Big Data Analytics – in addition to its many other business benefits – can guide the innovation process to make it more efficient, effective and predictable.
Big Data Analytics promotes the application of a data-driven mindset that ‘listens to the data’ for new insights and disrupts entrenched thinking that hinders innovation. It applies what-if analysis to assess impact of new ideas on key business metrics and uses evidence-based business performance analysis to track the impact of innovation. Integrating Big Data Analytics into the business planning and operational processes provides valuable feedback loops and enables an adaptive innovation process.
In short, Big Data Analytics can spark innovation, guide its refinement and adoption processes and sustain its ongoing implementation.

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Big data analytics and innovation Big data analytics and innovation Presentation Transcript

  • Big Data Analytics and Innovation How Big Data Analytics can spark, guide and sustain Innovation Ahmed Fattah, October 2013 October  2013   V1.5 Big  Data  Analy6cs  and  Innova6on   1  
  • Contents §  Big Data Analytics: big talk or big promise? §  What is Big Data Analytics? §  Why is it hard to innovate? §  Innovation and Big Data Analytics October  2013   Big  Data  Analy6cs  and  Innova6on   2  
  • Big Data: big talk or big promise? October  2013   Big  Data  Analy6cs  and  Innova6on   3  
  • What is Big Data Analytics? The ability to capture, move and process enormous volumes of data combined with increased sophistication and maturity of analytical capabilities enables significant economic and business value. Big Data + Analytics Data generated Growth in structured & unstructured data Ability to draw insights from data Memory & storage cost Network speeds Moore’s Law Data Mining, Machine Learning, Statistical Analysis, Operational Research, Content Analytics, Simulation, Stream Analytics, Map Reduce, … October  2013   Big  Data  Analy6cs  and  Innova6on   4  
  • Characteristics of Big Data Analytics •  •  •  •  Huge data: N è ALL Correlation before causation Messy: Errors, anomalies and outliers New & unstructured data types (not only transactions but interactions and observations) •  Predictive -- facilitates decision making •  Near real time •  Built-in performance optimisation capabilities October  2013   Big Data is All Data in All Data Repositories Big  Data  Analy6cs  and  Innova6on   5  
  • Data-driven mindset •  Data-driven mindset is a data-centric approach that “lets the data speak” which starts by identifying and collecting data needed to understand a given business area and ends with evidence-based confirmation of an improvement or a solution. •  The data-mindset can be outlined in the following activities: –  –  –  –  –  –  October  2013   Identify and collect data; Diagnose the current situation; Frame issues based on insights gleaned from the data; Identify possible solutions based on relationships between data objects; Forecast impact of candidate solutions on key business metrics; and Track business performance and contribution of implemented solution. Big  Data  Analy6cs  and  Innova6on   6  
  • Correlation before causation •  Data-driven mindset uses correlation because it is good enough for many practical purposes, for example, in product recommendations. •  Correlation fills a very important gap between implicit gut-feel models and elaborate causation models that may take excessive time and effort to build. October  2013   Big  Data  Analy6cs  and  Innova6on   7  
  • Why is it hard to innovate? Barriers: –  –  –  –  –  Power of the established model Following the experts Inability to deal with incoherence Uncertainty No champions Key questions: –  How can we come up with new novel ideas? –  How can we test new ideas for validity and impact and get them adopted? –  How can we track new ideas during and after implementation? October  2013   Big  Data  Analy6cs  and  Innova6on   8  
  • Big Data Analytics and Innovation BDA can spark, guide and sustain Innovation and thus improve its efficiency, effectiveness and predictability. •  Spark –  Disrupt current models by ‘listening to the data’. In other words, it identify issues and triggers the generation of new ideas •  Guide –  Allow modelling of what-if scenarios to understand the impact of new ideas thus allowing their continuous evaluation and so reduce risk inherent in innovation and convince sceptics via irrefutable evidence-based logic of the value of adopting innovative ideas •  Sustain –  Facilitate tracking KPIs verify impact of applying new ideas, hopefully encouraging more innovation October  2013   Big  Data  Analy6cs  and  Innova6on   9  
  • Summary and call to action •  The presentation argues that Big Data Analytics (BDA) can help overcome barriers to innovation in three ways: • Sparking innovation by promoting a data-driven mindset that listens to the data for new insights; • Guiding innovation using data-driven hypothesis testing, what-if analysis and crowdsourcing; and • Sustaining innovation by using ongoing evidence-based business performance management. •  BDA’s contribution to innovation is not just a bonus but an integral part of the essence of the new data-driven era. •  Call to action –  Apply the BDA-inspired data-driven mindset to every problem at hand to see how data can shed new light on the problem, verify the solution and track its implementation. October  2013   10 Big  Data  Analy6cs  and  Innova6on   10  
  • Supporting Slides For more information: www.ibm.com/software/au/data/bigdata/ October  2013   Big  Data  Analy6cs  and  Innova6on   11  
  • Basic analytics techniques taxonomy October  2013   Big  Data  Analy6cs  and  Innova6on   12  
  • IBM Big Data Platform New analytic applications drive the requirements for a big data platform. §  Integrate and manage the full variety, velocity and volume of data §  Apply advanced analytics to information in its native form §  Visualise all available data for ad-hoc analysis §  Development environment for building new analytic applications §  Workload optimisation and scheduling §  Security and Governance October  2013   Big  Data  Analy6cs  and  Innova6on   13  
  • Abstract (and link to paper) The ability to continuously innovate is crucial for business growth – and often necessary for survival. Leaders in an uncertain and fast-paced global business regularly seek innovation to revitalise rigid business models and processes. However, they are aware that ‘innovation is hard’ and fraught with uncertainty. I contend that Big Data Analytics – in addition to its many other business benefits – can guide the innovation process to make it more efficient, effective and predictable. Big Data Analytics promotes the application of a data-driven mindset that ‘listens to the data’ for new insights and disrupts entrenched thinking that hinders innovation. It applies what-if analysis to assess impact of new ideas on key business metrics and uses evidence-based business performance analysis to track the impact of innovation. Integrating Big Data Analytics into the business planning and operational processes provides valuable feedback loops and enables an adaptive innovation process. In short, Big Data Analytics can spark innovation, guide its refinement and adoption processes and sustain its ongoing implementation. See full paper on: Big Data Analytics and Innovation paper October  2013   Big  Data  Analy6cs  and  Innova6on   14