Research Report Analytics framework: creating the data-centric organisation to optimise business performance October 2013 Justin van der Lande
2 Contents [1] Slide no. 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 Slide no. 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
3 Contents [2] Slide no. 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 Slide no. 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
4 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
5 Executive summary Recommendations Market definition Business environment Vendor analysis Case studies Conclusions About the author and Analysys Mason
Value Analytics framework: creating the data-centric organisation to optimise business performance 12 Analytics solutions need to scale to meet the demand for delivering results in real time while using large data sets and complex models Figure 6: The development of the modelling capability of analytics tools [Source: Analysys Mason, 2013] Business intelligence tools Predictive analytics tools What will happen? In-line analytics tools What is happening now? 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 realtime 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. What happened? Time
21 Definitions of components in an analytics framework [1] Figure 11a: Definitions of analytics components [Source: Analysys Mason, 2013] Segment or sub-segment Data Extract, transform, load (ETL) Data infrastructure Definition 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. 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. 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 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 Apache Hadoop, Cloudera, Dell, EMC, Hortonworks, IBM, MapR, SAP HANA, Teradata
51 Executive summary Recommendations Market definition Business environment Vendor analysis Case studies Conclusions About the author and Analysys Mason
52 About the author Justin leads the Revenue Management, Analytics Software Strategies and CSP IT Strategies research programmes, which are part of Analysys Mason s Telecoms Software research stream. He specialises in business intelligence and analytics tools, the functionality of which cuts across all of the research programmes in this area. He also provides project management for large-scale projects within our Telecoms Software research. Justin has more than 20 years experience in the communications industry in software development, marketing and research. He has held senior positions at NCR/AT&T, Micromuse (IBM), Granite Systems (Telcordia) and at the TM Forum. Justin holds a BSc in Management Science and Computer Studies from the University of Wales.
53 About Analysys Mason Knowing what s going on is one thing. Understanding how to take advantage of events is quite another. Our ability to understand the complex workings of telecoms, media and technology (TMT) industries and draw practical conclusions, based on the specialist knowledge of our people, is what sets Analysys Mason apart. We deliver our key services via two channels: consulting and research. Consulting Our focus is exclusively on TMT. We support multi-billion dollar investments, advise clients on regulatory matters, provide spectrum valuation and auction support, and advise on operational performance, business planning and strategy. We have developed rigorous methodologies that deliver tangible results for clients around the world. For more information, please visit www.analysysmason.com/consulting. Research We analyse, track and forecast the different services accessed by consumers and enterprises, as well as the software, infrastructure and technology delivering those services. Research clients benefit from regular and timely intelligence in addition to direct access to our team of expert analysts. Our dedicated Custom Research team undertakes specialised and bespoke projects for clients. For more information, please visit www.analysysmason.com/research.
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