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Australia - Data Analytics

BuddeComm describes big data' as looking at intelligent outcomes that can be achieved from data collaboration.

The most critical issue here is strategic management, rather than technology. Big data has become a vital tool in competition is forcing many companies to transform their organisations from a company-centric approach to a customer-centric one.

The fact that this development is being driven by data-rich organisations such as Google, Apple, Amazon, Facebook, eBay and many others operating in the digital economy is an indication that data management is a critical factor here.

In other words, if you don't have your company's data systems and structures organised in a customer-centric way you won't be able to deliver a good customer experience.

Connected information management, however, can go much further. There are many other players involved in the broader ecosystem, and by sharing and combining relevant data sets and then analysing those large data sets we can find new correlations that can be used to spot business trends, assess customer behaviour, prevent diseases, combat crime and so on.

Key emerging trends for 2018 in data analytics include: embedded analytics, artificial intelligence (AI), behavioural analytics and predictive analytics.

Key Developments

Connected information management, embedded analytics, artificial intelligence (AI), behavioural analytics and predictive analytics.


1. Synopsis
2. Big Data Data Analytics
2.1 High quality data and analytics can improve customer relationships
2.2 Data silos
2.3 Contextual intelligence
2.4 Benefits for telcos and ISPs
2.5 Social Network Analytics
2.6 Subscriber Data Management
2.7 Open data policy
2.8 6000 sets of government info goes public
2.9 Telcos and the science of big data - Analysis
3. Key trends and Developments
3.1 Emerging trends in data analytics for 2017 and 2018
3.2 NSW government's dedicated data analytics office
3.3 Connected Information Management (CIM)
3.4 Deep packet inspection
3.5 Ubiquitous Complex Event Processing
3.6 Behavioural Attitudinal Geolocation
3.7 Advanced recommendations engines
3.8 Lifetime customer relationships
3.9 Data analytics solutions for Smart Grids
3.10 Cryptography
3.11 Smart Cities and the open data dilemma
3.11.1 Case study Geelong
4. Other Reports
Exhibit 1 Real-time processing
Exhibit 2 Watson cognitive computing
Exhibit 3 - Key characteristics of contextual intelligence in customer service

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