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.

1. 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

2. 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

3. Big Data: big talk or big promise?
October
2013
Big
Data
Analy6cs
and
Innova6on
3

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Supporting Slides
For more information:
www.ibm.com/software/au/data/bigdata/
October
2013
Big
Data
Analy6cs
and
Innova6on
11

12. Basic analytics techniques taxonomy
October
2013
Big
Data
Analy6cs
and
Innova6on
12

13. 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

14. 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