big data analytics and innovation
DESCRIPTION
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.TRANSCRIPT
October 2013 1
Ahmed Fattah, October 2013
Big Data Analy6cs and Innova6on
Big Data Analytics and Innovation How Big Data Analytics can spark, guide and sustain Innovation
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2 October 2013 Big Data Analy6cs and Innova6on
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
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Big Data: big talk or big promise?
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Big Data Analytics Data generated Ability to draw insights from data
Memory & storage cost Moore’s Law Network speeds Growth in structured & unstructured data
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.
What is Big Data Analytics?
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Data Mining, Machine Learning, Statistical Analysis, Operational Research, Content Analytics, Simulation, Stream Analytics, Map Reduce, …
5 October 2013 Big Data Analy6cs and Innova6on
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 Big Data is All Data in All Data Repositories
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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:
– 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.
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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.
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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?
9 October 2013 Big Data Analy6cs and Innova6on
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
10 October 2013 Big Data Analy6cs and Innova6on
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.
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Supporting Slides
For more information: www.ibm.com/software/au/data/bigdata/
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Basic analytics techniques taxonomy
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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
14 October 2013 Big Data Analy6cs and Innova6on
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