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data analytics:evidence–based decision making
CeADAR: centre for applied data analytics
CeADAR is amarket-led centre
focussed on thedevelopment and
deployment ofbig data analytics
technology and innovation for the
competitive advantage
of its member companies
CeADAR: bridging the gap
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University CeADAR Enterprise
Basicprinciplesobserved
System proventhrough fullcommercialapplication
Technologyvalidated
in lab
Systemprototypedemo’ed
in operationalenvironment
• Basic research& technology creation
• Market push• Technology driven
Product developmentand commercialisation
• Application development& proof of concept
• Business-value driven• Market pull/need-driven
CeADAR offeringsadvisory
business seminars
demonstrators
collaborative work flagship projects
bespoke solutions
customer exploration tool
A data analytics tool to allow users, without significant data analytics expertise, to explore any customer segments that they have themselves manually defined in an ad-hoc manner, e.g. for a mobile phone operator, this could be how many women between the ages of 16-20 use their services?
real-time analytics of big fast data▪ High-throughput scalable clustering for data streams▪ Live data capabilities to advanced analytics tasks▪ Solution processes > 3M entities/minute (50K/sec)▪ Implemented on Apache Storm with commodity hardware
Applications: ▪ SPAM detection in networks▪ fraudulent behaviour detection▪ identifying emerging topics in
content streams▪ detecting new user behaviour
and patterns
contact centre analytics
Text stream
Issues and
alerts
Automatic speech-to-text transcription Topic modelling
▪ Automatic topic and issue extraction over large volume audio dialogue
▪ Alerting to new and trending topics and tracking of known topics
▪ Applications– contact centre data– online video transcription data– large-volume speech audio domains
CrowdTrack: location-based analytics
DocoPoolMarket needMany businesses store pools of information-richtext in their systems – such as reports,customer reviews, user comments. Insights can be uncovered by analysing these textsources to discover underlying hidden topics or trends.However manually examining such large corporais often prohibitive
Key features• Easy-to-interpret visualisations• Drill-down on document details for deeper
analysis of topics• Specialist or domain specific word exclusions • Flexible document upload (.txt, .pdf and .docx)• ‘Save’ facilities to allow revisiting of explorations
SmartAd: optimising marketingEach advertising channel has different characteristics of form, audience, and customer interaction data. Channels & campaigns interact. Businesses need to analyse these interactions to plan advertising campaigns
SmartAd fuses the information from all channels and creates a model of the system of mutual influences
SmartAd visualises the interactions between different influencer channels and a target channelThe importance of each channel is given by its size. The distance from the target e.g. site visits represents the confidence in the result
OverviewDevelops new techniques and methods to identify users across disparate social networks based on both network-based metadata and content-based features
Applications• Social fingerprinting: identifying
individuals across different networks for user validation, user profiling, fraud prevention etc.
• Identification of domain expertise e.g. for social recruitment or expertise discovery
social media analytics
Nudge Along: changing user behaviours
ApplicationsThis work benefits any company whichwishes to make sure that theircommunications with customersproduce the required or expectedoutcome. For example, the ability tochange user behaviour is of interest toany company that wishes to see theeffects of a marketing campaign, or acampaign to get staff to save energy intheir offices by switching off lights etc.
OverviewMeasuring the success of any analytics-driven project requires looking beyond the insights that are produced to ways in which these insights are communicated and delivered to ensure that behavioural change takes place
BlockChain: comparison of realisations
deep learning▪ traditionally, pattern recognition problems have
been solved using domain knowledge and feature engineering
▪ manual feature engineering is greatly challenged by big and diverse data sets
▪ deep learning is an unsupervised feature learning paradigm
▪ goal is to assist our industry members in understanding potential uses of deep learning and when this approach is best
Synthetic dataset generator
Logo recognition (Look And Learn)
Heineken
Coca-cola
Coca-cola
No-logo
• Track brand mentions• Improved sentiment analysis• Measure sponsorhip ROI• Find visual influencers• Identify moments of consumption
Beyond the DesktopVoice-controlled access to KPIs
www.ceadar.ie/outputs/our-demos/▪ customer analytics
▪ real time analyticsfraudulent behaviour detection
identifying emerging topics
detecting new user behaviour
▪ contact centre analytics
▪ social media analyticsSocial fingerprinting
Domain expertise
▪ text analytics
▪ Location-based analytics
▪ sentiment analysis
▪ video & image analytics
▪predictive analytics
▪deep learning
data analytics:evidence–based decision making