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Communications service providers are going to have to deploy real-time data analytics solutions if they are to optimise their businesses. This framework report provides an overview of key analytics issues and case studies that are relevant to the telecoms sector. It also provides guidance on the different use cases and the major vendors that are active within the market.

Analytics solutions need to scale to meet the demand for delivering results in real time while using large data sets and complex models

Today's analytics tools have been developed from the business intelligence tools of the past that were concerned with reporting what has already occurred. This may include the running of complex models to provide derived information that is used within KPIs or other business measurements.

Predicative analytics tools model future outcomes based on historical patterns. Highly skilled staff are able to create models based on an understanding of the data attributors and the potential outcomes.

In-line analytics tools overcome the time constraints of running models on stored data by being updated with real-time information. This enables models to react to live information and update live processes where needed. For example, it is possible to address in real time events such as network configurations or selling services to active users at a location or on a website.

Definitions of components in an analytics framework

Segment or sub-segment: Data
Unstructured, semi-structured and structured data that is used within the analytics model. This data can be pulled from any data source and specifically measured using probes or diagnostics tools. Operational systems such as billing, customer relationship management (CRM) or enterprise resource planning (ERP) as well as network data such as IP detail records (IPDRs) or CDRs are often used, but transient data such as location are increasingly being tracked.

Segment or sub-segment: Extract, transform, load (ETL)
ETL processes are three functions often combined into a single tool.
• Extract: reads data from a specified source database and extracts a desired subset of data.
• Transform: manipulates the data using rules or lookup tables, or creates combinations with other data sources to convert it to the desired state.
• Load: writes the resulting data (either all of the subset or just the changes) to a target database, which may or may not exist as a data warehouse or enterprise data warehouse, data marts, online analytical processing (OLAP) applications or „cubes?, or other business intelligence or analytics application tools.
ETL functions are increasingly being replaced with ELT or ETLT tools to reduce data loads on the network and provide faster execution. There is also much value to being able to store the large volumes of raw data.
Sample vendors and solutions:
Informatica, IBM InfoSphere DataStage
Also, but not dedicated to the function: Ab Initio, IBM Cognos, Microsoft SQL Server Integration Services (SSIS), SAP Business Objects, SAS Institute

Segment or sub-segment: Data infrastructure
Storage, servers and associated networking infrastructure. Historically, these have been the preserve of established vendors in the market, but the advent of unstructured data has created a new class of devices and data store. The open-source Apache Hadoop processing infrastructure has become popular. This builds on established massively parallel processing, which uses multiple loosely coupled processors to work on different parts of a programme.
Solutions such as those offered by Aster (Teradata), IBM Netezza, Oracle Exadata, SAP HANA and Vertica can be used in conjunction with Hadoop.
Sample vendors and solutions:
Apache Hadoop, Cloudera, Dell, EMC, Hortonworks, IBM, MapR, SAP HANA, Teradata

Table Of Contents

Analytics framework: creating the data-centric organisation to optimise business performance
Contents

5.Executive summary
6.Executive summary
7.Big data analytics solutions address CSPs? business demands to create new revenue and „super-charge? their established operations
8.The market for big data analytics solutions is set for growth as CSP margins come under pressure and solution costs continue to decline
9.CSPs have applied analytics to a rich set of use cases across different aspects of their business, including new digital data revenue streams
10.Market maturity dictates the analytics solutions that CSPs need to deploy
11.Analytics solutions are shifting from passive batch mode reporting on historical data to predictions that operate in real time
12.Analytics solutions need to scale to meet the demand for delivering results in real time while using large data sets and complex models
13.Big data analytics is challenging established systems, and leading CSPs are investing in new infrastructure to address the challenge
14.CSPs are becoming data-driven organisations: the openness and flexibility of their data infrastructure will dictate the use cases they can support

15.Recommendations
16.Recommendations for CSPs
17.Recommendations for vendors

18.Market definition
19.Key components in an analytics framework
20.Creating support for a specific use case requires the development of each component, either bespoke or pre-developed and off-the-shelf
21.Definitions of components in an analytics framework [1]
22.Definitions of components in an analytics framework [2]

23.Business environment
24.CSPs have used analytics for years, but the declining cost of data storage and other infrastructure is widening the range of viable uses
25.Open-source tools and cloud-based data infrastructure continue to drive down the costs associated with analytics
26.The key business challenges are still based on familiar CSP business requirements
27.The business environment for analytics and data infrastructure solutions
28.Summary of analytics market drivers and inhibitors for CSPs
29.Analytics market drivers for CSPs
30.Analytics market inhibitors for CSPs

31.Vendor analysis
32.Vendors are continuing to expand into the telecoms analytics market from different perspectives
33.Vendors of general-purpose analytics tools dominate the market, but new vendors are competing with telecoms-specific applications
34.Analytics and business intelligence tool vendors [1]
35.Analytics and business intelligence tool vendors [2]
36.Analytics and business intelligence tool vendors [3]
37.Analytics and business intelligence tool vendors [4]
38.Analytics and business intelligence tool vendors [5]
39.Industry-specific analytics and business intelligence tool vendors
40.Acquisitions continue to consolidate the highly fragmented market as large vendors create complete solutions

41.Case studies
42.Analytics has a rich set of use cases that can be embedded in applications or developed on general-purpose platforms
43.Example of customer management using analytics tools
44.Case study: Telefónica Ireland uses analytics to reduce churn
45.Case study: Telecom Italia deployed analytics in order to improve service quality
46.Case study: T-Mobile USA for customer segmentation
47.Case study: Weve is using intelligent mobile data to create a new revenue stream
48.Case study: Globe Telecom addresses churn and segmentation with an analytics platform

49.Conclusions
50.Analytics solutions can enhance business performance

51.About the author and Analysys Mason
52.About the author
53.About Analysys Mason
54.Research from Analysys Mason
55.Consulting from Analysys Mason



List of figures

Figure 1: Business demand drivers for analytics tools
Figure 2: Analytics market enablers and drivers
Figure 3: Analytics use case segmentation
Figure 4: Market maturity use case segmentation
Figure 5: Evolution of analytics to real-time processing
Figure 6: The development of the modelling capability of analytics tools
Figure 7: The market maturity of analytics tools
Figure 8: The components of an analytics application
Figure 9: Analytics market taxonomy
Figure 10: The components of an analytics application
Figure 11a: Definitions of analytics components
Figure 11b: Definitions of analytics components
Figure 12: The declining cost of storage
Figure 13: Analytics market drivers
Figure 14: Analytics market inhibitors
Figure 15: Types of analytics vendor
Figure 16a: Examples of analytics and business intelligence tool vendors
Figure 16b: Examples of analytics and business intelligence tool vendors
Figure 16c: Examples of analytics and business intelligence tool vendors
Figure 16d: Examples of analytics and business intelligence tool vendors
Figure 16e: Examples of analytics and business intelligence tool vendors
Figure 17: Examples of industry-specific analytics and business intelligence tool vendors
Figure 18: Mergers and acquisitions in the analytics market
Figure 19: Analytics use case segmentation
Figure 20: Example of customer management using analytics tools

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