on big data analytics - opportunities and challenges
DESCRIPTION
My Presentation in Digitalization - Key to Growth - Seminar in Espoo, Finland 24th September, 2014TRANSCRIPT
Big Data Analytics –Business
Opportunities and Challenges
24.9.2014, Espoo
Petteri Alahuhta, @PetteriA
3 24/09/2014 3
Big Data in Hype-Cycle (Gartner)
@PetteriA
Internet of
Things
Big Data
Analytics Big Data
Tools
5 24/09/2014 5
BIG DATA – ”high volume, velocity and/or
variety information assets that demand cost-
effective, innovative forms of information
processing that enable enhanced insight,
decision making, and process automation.”
(Gartner, 2012)
@PetteriA
6 24/09/2014 6
Big Data is about
increasing number of V’s
Volume – Data size
Velocity – Speed of Change
Variety – Different forms of data
sources
Veracity – Uncertainty of data
Value –Transforming data into
new value
Visualization – visualizing the
data for insights
Validity
Venue
Vocabulary
Vagueness
@PetteriA
MB GB TB PB
Batch
Periodic
Near Real
Time
Real Time
Data
Volume
Data
Variety
Data
Velocity
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Large part of available information is not well
leveraged
Machine data (IoT)
Social data
Databases, BI-data
@PetteriA
In effective use
Ineffective use
Business applications,
Master data, Data Warehouse,
data cubes, Business Intelligence
Unstructured data
semi-structured data
Open data (struct. &
semi-struct.),
API’s
Sensors data streams
8 24/09/2014 8
Data is Raw Material – Tools and people are
the key to Insights
@PetteriA
Data Tools / People
Insights
Structured - Data in rigid
formats. E.g. Databases
Unstructured - No particular
pattern/format. E.g. texts, video
Semi-structured –Unstructured
data with a format. E.g. Twitter-
feeds, tags in videos
Differentiated – Proprietary
data of Market or business – in-
house or 3rd party data
Big - Beyond current processing
capabilities
Algorithms - Rules or
equations derived from
analysis of data
Analytics - Statistical
description that
Provides overall
understanding of the
patterns in the data
Tools help to process raw
material
People to produce
insights from raw material
Industry - Expertise in the economic
production of a product or service,
e.g. Machinery sector
Discipline - Expertise in the
development of processes taht can
be applied accross cariety of
industries e.g supply chain
Technical – Expertise in the
development of processes requiring
knowledge of math and science. E.g.
Data science
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Adding value through analytics
Descriptive
Analytics
Predictive
Analytics
Prescriptive
Analytics
Value
Complexity
What
happened?
And Why?
What will
happen?
How can we
make it happen?
Hindsight
Insight
Foresight
@PetteriA
13 24/09/2014 13
Big Data –Market Drivers and Restrains
Key Market Drivers Key Restrains
Hyper connectivity and need for turning
data to intelligence boost the need for
solutions standardize visualization,
analysis and reporting of data
Shortage of talent fro analytics and
technical skills
Data-driven real-time insights provide
competitive advantage
Legacy infrastructure and lack of Big
Data implementation strategy
Availability of open source tools for Big
Data computing & processing (e.g.
Hadoop)
Significant investments in Big Data
analytics required
Examples from predictive and
prescriptive analytics in different use
cases increase demand for replicating
them in different sectors
Big Data deployments remain
underutilized because fully leveraging
them would require process and
business model changes
@PetteriA
Modified from Frost Sullivan
16 24/09/2014 16
Examples of Big Data Use Cases
@PetteriA
• Customer segmentation
• Behavior analytics
• Affinity analysis
• Customer service improvements
• Pricing analysis
• Campaign management
Customer Insights
• Fraud detection
• Cybersecurity
• Defense
• Trading analysis
• Insurance analytics
• Real estate
Security and risks
• Inventory
• Network analysis
• System performance
• Retailing
Resource Optimisation
• Sales productivity
• Operational efficiency
• Internal process improvements
• Human resource planning & mgmt
Productivity improvements
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Big Data Trends
Technology
Democratizing Big Data
Rise of Machine Learning
Democratizing of Analytics
Real-time analytics
Hadoop
Context and Sentiment
Analysis
Automated machine learning
Market
Big Data, Big Priority
Data Governance
Faster Deployment on the
cloud
Industry-Specific Solutions
Analytics for SMB’s
More C’s at the Top
@PetteriA
19 24/09/2014 19
Challenges VTT is addressing
Creating value from big data
Effectively management and analysis
of huge volumes of varying data from
different sources
Cyber and information security
@PetteriA
20 24/09/2014 20
Our areas of Expertise in Big Data
Independent
digital service
design
Capturing value
from real-time
analytics
New customer
offering from web
based services
Data science
expertise
Visualization of
data
Resource
restricted data-
analytics
Real-time data-
analytics
Distributed
data fusion
Independent
digital service
engineering
Security testing
and analyses
Security metrics,
testing and risk
analyses
Security solutions
for embedded
systems
Acquiring data
Information
integration
Data
management
Creating value
from big data
Data Science &
Analytics
Information
Management
Cyber and
Information Security
@PetteriA
21 24/09/2014 21
Final Remarks
There are surprising and valuable insights hiding in the data on hand and the
new data that are becoming available
Insights can be converted into cost-reduction and revenue-enhancing in
business processes
Succesful showcases of Big Data analytics are still rare and solutions are
unmature.
=> Experiment, Start small, Measure the impact, Build on good results,
Experiment again
@PetteriA
