Artificial Intelligence (AI) and Big Data Analytics (BDA) in Telecomm Industry, 2021 Update – Market Overview, Technology Ecosystem, Telco Use Cases and Monetisation Strategies
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Big Data Analytics (BDA) examines large data sets to uncover patterns, variable correlations, and market trends that can be turned into actionable insights. BDA has become vital in helping businesses gain real-time insights and make better informed strategic decisions. Traditional BI usually answers basic descriptive questions about operations and performances whereas BDA involves complex algorithms translating large volumes of data and performing predictive and prescriptive analytics to maximize future opportunities and optimize business actions. BDA supports digital transformation and industry 4.0, enabling the processing and analysis of large data volumes in different data structures, at high speeds and with low latency. Big data sources include M2M/IoT, customer behavioral data, network data, transactions data, and log files. Processing big data requires robust computing power and extensive server infrastructure which leads enterprises to run their BDA applications in many cases on the cloud as a flexible and cost-effective setup. Edge computing is also starting to emerge in the big data ecosystem, with the technology bringing the analytical power of a distant data center directly to the site of data collection – reducing the time taken to gain actionable insights.
AI enables enhanced automation, quicker reaction to data sets, and a multitude of use cases in several consumer and corporate areas as well as industry verticals, ranging from personalized services to improving business performance and agility. AI’s accuracy and performance is being boosted by cheaper processing power available on the cloud, large amounts of available data sets, and several AI algorithms including ML and DL. Telcos are utilizing AI to drive digital transformation, advanced automation, and innovation throughout their organizations, including launching customer-facing smart services. The utilization of AI has further paved the way for Industry 4.0 and quantum computing. This next level of AI follows the technological development of quantum computing, which leverages the properties of quantum states to perform computation, with standard computers operating in binary language whereas quantum computers are the opposite – operating in a state of superposition. Quantum computation allows AI that can perform tasks and calculations faster than standard AI, with better performance for computer vision, NLP, and robotics.
The BDA value chain enables a stream of processed data inputs into AI platforms and applications. BDA also provides essential inputs into the AI platform enabling the processing of high volumes of data at high speed, with low latency and from different data structures. BDA and AI, along with cloud and IoT, power Industry 4.0 enabling a multitude of use cases in several consumer and corporate areas, ranging from personalized services to improving business performance and agility. AI and BDA, along with IoT, cloud, 5G, and cybersecurity, are enabling a multitude of use cases in several consumer and corporate areas; ranging from personalized services, chatbots, AI and BDA platforms to AI integration to improve business performance and agility. BDA is enabled by the increasing number of real-time data sources, such as call logs, geographic data and consumer purchasing habits. AI is enabled by key technologies, such as conversational platforms, computer vision and context-aware computing – which can be further powered by machine learning (ML) and/or deep learning (DL) algorithms, to supplement the AI’s built-in understanding.
Telcos can innovate upon their existing services through the use of AI, building AI into their network management, fiber rollouts and digital services. In addition, telcos are able to develop and sell their own AI applications or provide businesses with the platforms to develop their own specific to their use case. Moreover, telcos can build more in-depth BDA capabilities and turn these into solution portfolios – e.g., BDA platforms, vertical-specific BDA solutions, and consultancy services. This can be achieved in-house, by creating new specialized divisions and/or through partnerships with other ecosystem players.
What are the key technologies in Artificial Intelligence?
Machine Learning: Enables algorithms to self-learn, when exposed to large amounts of data, without being explicitly pre-programmed to do so.
Deep Learning: Subclass of ML that can scale algorithm output accuracy and performance to a large number of data input types in order to conduct more complex and demanding tasks, often involving specialized hardware.
AI predictive modelling: Involves in building, testing, and adjusting an algorithm to best predict an outcome using ML and/or DL.
Context-aware computing: Refers to systems that adapt their behavior according to contextual information including location, temperature, light, humidity, and hand movements.
Computer vision – includes all technology that captures and interprets images or videos in a useful way
Conversational platforms: Employs a variety of technologies – e.g., NLP, speech recognition via NLP, contextual awareness, and ML to enable human-like interactions.
Robotics and drones: Can act autonomously and work safely around humans, based on the interpretation of data derived from an array of sensors.
What are the key segments in big data value chain?
Every industry generates big data – extremely large, diverse data sets that when analyzed can reveal patterns, trends, and associations. Big data can be of value to consumers and enterprises if it is reliable, robust, and secure. The four main segments of the big data value chain are as follows:
Big data generation and collection: Big data is produced by digital footprints including call records, emails, sensor activity, payments, social media, posts, photos, and videos.
Big data management and development: It includes data integration, data aggregation, data storage and data processing. The process comprises of standardizing data structures and copying the data from multiple sources into a data warehouse, summarizing the data in a presentable format, structure data in such a way that it can be easily retrieved and transforming raw data into useful information
Big data product insights: It includes data analysis and data BI. Data analysis uses statistical and analytical techniques to derive insights. BI is a set of technologies, processes, architecture, and methodologies that convert raw data into actionable insights.
Big data product development and consumption: Big data is developed and consumed to power several sectors and business areas
Who are the major players in the BDA and AI ecosystems?
Major players of the BDA ecosystem include NEX, ScienceSoft, Dell EMC, IBM, Google, Oracle, FICO, AWS, and Telefonica. Telcos have access to an enormous amount of customer data which positions them well to use BDA beyond gaining insights to improve their internal processes, marketing campaigns, digitize their customer touchpoints, and yield deeper customer engagement. Telcos can resell, white label, and provide as a service their in-house developed BDA capability to external enterprise customers. Telcos have the opportunity to provide consultancy services to several sectors where big data can enable the optimization of business decisions.
Major players of the AI ecosystem are IBM, Google, Apple, Microsoft, Oracle, Netcracker, SAP, Tableau, Infosys, Alibaba Cloud, Cortana, Siri, and Amazon Alexa. Beyond using AI for their own internal digital transformation and to enhance products, telcos can harness AI to drive new revenue streams. Telcos can play a role in the AI value chain by leveraging their data center and cloud assets and upgrading these with AI chips/GPU/TPU-based processing and containerization to possibly host and run third-party AI solutions in-country. Telcos can resell, white-label, and provide as a service their in-house developed AI capability.
Market report scope
| Key Companies in AI | IBM, Google, Apple, Microsoft, Oracle, Netcracker, SAP, Tableau, Infosys, Alibaba Cloud, Cortana, Siri, and Amazon Alexa |
| Key Companies in BDA | NEX, ScienceSoft, Dell EMC, IBM, Google, Oracle, FICO, AWS, and Telefonica |
| Value Chain of BDA | Big data generation and collection, big data management and development, big data product insights, big data product development and consumption |
The report provides several BDA & AI value chain positioning options that telcos can adopt to drive new revenue streams. It also includes BDA & AI players’ ecosystem map and value chain and summarizes the service use cases that can be harnessed by telcos to transform and produce value.
Reasons to Buy
- This global outlook report provides an extensive examination of the BDA & AI ecosystem to help businesses harness both technologies and related business model levers required for telcos’ internal transformation and to help them capture new revenue streams in the BDA & AI value chain.
- Access to case studies providing insights into telecom operators’ BDA & AI implementations and solution portfolios; this will help industry executives understand the drivers and benefits BDA & AI can create for them as well as the business & partnership models telcos can adopt to get access and deploy BDA & AI capabilities.
- The report maps the BDA & AI ecosystem players’ landscape, specifying the players’ role in the BDA & AI value chain. It also provides a number of value chain positioning options to be considered and assessed by telcos in order for them to play an active role in the BDA & AI space and monetize the arising opportunity.
Accord
Acumos
Aegis
Affirm
Air Europa
Airtel
Alibaba
alteryx
Amazon
AMD
Amdocs
Apache Spark
Apple
arm
AT&T
Atlas Edge
Aura
Baidu
Bharti Airtel
broscorp
C3 AI
Caffe
China Unicom
Cisco
Clarifai
Cloudera
codecoda
Cognigy
Consagous
data iku
data.world
DataSpark
Dell
Deutsche Bahn
Deutsche Telekom
DialogFlow
docomo
DSSTNE
Ericsson
ET Brain
FAYRIX
Fico
Genesys
GFAIVE
Graphcore
Hadoop
HANA Bank
HiAI
Huawei
Iberia
IBM
Iflytek
indium
Informatica
Infosys
Intel
itsvit
JD Digits
Keras
KNIME
La Ligua
Leonardo
Liberty Global
Magic Leap
MathWorks
Microsoft
MicroStrategy
MindMeld
mlpack
myEinstein
National Computer Systems (NCS)
NBN
Netcracker
NEX
nGraph
Nokia
NTT Docomo
Nuance
nVIDIA
Oracle
Orange Business Services
PaddlePaddle
Proximus
QlikView
Qualcomm
Salesforce
SAP
sas
ScienceSoft
Sensetime
SetuServ
SGAnalytics
Singtel
SK Telecom
snowflake
Softbank
Software AG
SoundHound
South Korea Telekom (SKT)
,Swisscom
tableau
Tech Mahindra
Telefonica
tellius
Tencent
TensorFlow
TensorRT
Tibco
torch
T-Systems
Tupl
Veridas
Verizon
Vier (Four)
VMWARE
Vodafone
Wipro
XenonStack
Xiaomi
Xplenty
Zain
zestfinance
ZTE
Table of Contents
Frequently asked questions
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What is the value chain of BDA ecosystem?
Value chain of big data ecosystem includes big data generation and collection, big data management and development, big data product insights, big data product development and consumption.
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What are the key companies in artificial intelligence?
Key companies in artificial intelligence include IBM, Google, Apple, Microsoft, Oracle, Netcracker, SAP, Tableau, Infosys, Alibaba Cloud, Cortana, Siri, and Amazon Alexa.
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What are the key companies in big data?
Key companies in big data include NEX, ScienceSoft, Dell EMC, IBM, Google, Oracle, FICO, AWS, and Telefonica.
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