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 âsuper-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  22.Definitions of components in an analytics framework 
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  35.Analytics and business intelligence tool vendors  36.Analytics and business intelligence tool vendors  37.Analytics and business intelligence tool vendors  38.Analytics and business intelligence tool vendors  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: TelefÃ³nica 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