Volume:The size of the data

● By 2025, the amount of digital data created worldwide will be 163 zettabytes, ballooned by 75.44 billion Internet of Things (IoT) connected devices, whereas in 2020 there will be 6 billion cell phones. source: IDC Data Age 2025 and Statista - The Statistics Portal

Velocity:The speed at which data is generated

● By 2025, more than a quarter of all data created will be real-time.source: IT Pro Portal

Variety:The different types of data

● By 2020, there will be 5,200 gigabytes of data on every person in the world, collated from multiple data sources. source: ComputerWorld

Veracity:The accuracy of the data

● By 2025, the global indoor LBS (location-based service) market size is expected to reach USD 18.74 billion, rising at a CAGR (compound annual growth rate) of 37.8% during the forecast period. LBS data loses relevance if it lacks veracity even by a small margin. source: Grand View Research, Inc.

Value:The monetization of the data

● By 2027, the worldwide Big Data market revenues for software and services are projected to increase from USD 42 billion in 2018 to USD 103 billion, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. source: Wikibon

OVERVIEW

The digital transformation of telecommunications represents a 2 trillion USD opportunity for industry and society, and the CSP-s (Communications Service Provider) make the major gains. source: World Economic Forum

Let Neural Technologies help you benefit from this opportunity!

Global Statistics on Explosion of BigData:

The use of digital technology to solve problems is called Digital Transformation. It involves the harnessing of fast-changing digital technology to focus on the customer experience; to make the operational processes well–defined, streamlined, and transparent; to achieveclear integration between data and process; and to stress on “value” and not mere “activities”.

Cloud computing is an example of digital transformation. It shifts reliance from user owned hardware to subscription-based cloud services.

In whichever state of digitization your business is, you can always opt for digital transformation. Wherever you are, that’s where you start, because everyone starts from a different place. Digital transformation is a strategy that is long-term, and it is not a tactic that is short-term. You must bring about lasting cultural and technological changes in your organization for bringing lasting organizational and business success. The transformation can either be disruptive, but you can also embrace a more organic process of evolutionary, incremental, and iterative progress. The art is to stay ahead of the next wave of cataclysmic change of disruption.

Why do Digital Transformation projects fail?

● Complexity of Event Data integration ● Complexity of high volume & velocity computing (see

expectations above) ● Complexity of Event Data analysis and information delivery ● Complexity of Event Data retention

McKinsey believes that developing or possessing a digital business platform will help companies to exper-iment, fail, and scale quickly, and get products to market faster than their peers – think weeks, instead of months or years. Gartner sees digital business platforms as fundamental to scaling a business. Scale often requires ecosystems, so digital business platforms should support it with connections between processes and functions to build these ecosystems.

Finally, Aragon Research adds that digital business platforms are essential to prepare and deliver for the customer of future. “These platforms adapt to changing customer needs through the resources that service customers, processes, and applications that dynamically adapt and optimize in a predictive manner.”

Why is Digital Transformation Platform crucial for success?

OverviewWorking in more than 50 Operators around the Globe, we support Telecom Operators with our Digital Transformation Platform for:

● Enabling Automated Data Transformation and preparation for AI/ML and automation use cases

● Enabling Automated Data Orchestration and Mediation for any BSS/OSS and Big Data Systems

● Omni Channel Enablement for improving the customer experience

● Enabling Data Repository for any type of system through Data Portal, thus reducing the connections to data source systems, improve time to market and reduce the cost

● IoT Enablement for multi domain use cases (car parking etc.)

● Enabling Automated Dunning ● Enabling Charging/Rating as service

● Enabling Customer and Churn Analytics ● Data orchestration (mediation, Data Portal,

Middleware, Streaming Analytics) ○ Bringing Efficiency ○ Realtime Data ○ Improving the time to market ○ Robustness (minimum problems), ○ Scalable (microservices) ○ Configurable ○ Ready to use adapters ○ Cost saving

● Digital transformation platform: ○ Stream Data Integration ○ Middleware ○ IoT Data Orchestration ○ Data Mediation ○ Data Portal / Data Cache ○ IoT Data Orchestration

What Is Digital Transformation?

Digital Transformation of the Telecom Industry

Our Value Proposition

2 X times faster time to Market: Swift product and services rollout. Lower TCO in deployment & operation: you do not have to spend time or pay a fortune for data integration and any adjustment (new vendor release, new interface, configuration change etc.) could easily be done through the platform. We have seen a typical of 22% lower cost for data integration compared to traditional way of integration.

Easy to use: Data Orchestration is made easy by low code user interface.

Robust platform: All the integrations will be standard and any changes could be managed through a single platform.

Faster time to market for Marketing Campaigns: Since all mediation system will be managed by the platform, any change required for a Campaign introduction will be able to be done in days rather than weeks.

Scalable: The system is already handling billions of Event Logs for Operators like T- Mobile US, Sprint, Zain Group and so on.

The whole ecosystem of business has gone digital. The Telecom Operators and enterprises need to keep pace with the change sweeping over the customers, channels, content and competitors. For this, the Operators need to change at a very basic level.

The most tectonic shift in CSP business model is the new supremacy of data usage over voice call usage. With the advent of 4G and emerging 5G, messaging services such as WhatsApp, OTT (over the top streaming) such as Netflix, the borders between industries are disappearing, where cable, internet, calling and wireless services all coming under the service portfolio of CSPs. CSPs are now competing with companies dealing with traditional telephone, cable, wireless, satellite TV, apps and devices and VoIP (voice over internet protocol).

Latest Telecom Operator market trends: ● Data: exponential growth. ● Digitization: cloud services. ● Convergence: discrete services coming

together. ● Customer behavior: going fully wireless and

massive data consumption.

1. From reactive product-specific security to uniformly orchestrated security

2. From limited data exploitation to a uniformly orchestrated data-centric enterprise

3. From closed management systems to an Open API platform architecture

4. From a limited portfolio of traditional services, to a diverse portfolio of digital services

5. From managing a limited set of suppliers, to existing in a vibrant ecosystem of partners

6. From a limited set of business models, to utilizing multiple business models in core and adjacent markets

7. From a traditional telco organization and culture, to a digital organization and culture

8. From focusing on traditional channels, to adopting multiple channels to market

9. From one dimensional management of customer relationships, to 360-degree omnichannel management of the customer experience

The Optimus platform enables any type of IT and Network System (BSS/OSS) data to be integrated through an automated system easily. The Optimus Platform:

The 9 Digital Transformation Journeys Of The Telco

FEATURES

https://www.neuralt.com info@neuralt.com

Optimus leverages Streaming Integration for Digital TransformationStreaming integration is the real-time continuous collection and movement of any enterprise data, handling extreme volumes, at scale, with high throughput and low latency. Processing, analysis, correlation, and delivery of data happen in flight, giving data value and visibility, in a reliable and verifiable fashion.

Real TimeThere is no delay between data being created, collected, processed, delivered, or viewed such as might be present in traditional Extract, Transform, and Load (ETL) systems or any architecture that uses storage as an intermediary.

Easy to useData Orchestration is made easy by low code user interface.

Continuous CollectionIt involves collecting data as soon as possible after it is created and before it ever hits a disk.

Continuous MovementThe result of this continuous collection is a set of data streams. These streams carry the data in real time through processing pipelines and between clustered machines, on-premises and in the cloud.

Any Enterprise DataData is generated and stored in a lot of different ways within an enterprise, and requires a lot of different techniques to access it. These include databases, files, message queues, and IoT devices, data warehouses; document, object, and graph databases; distributed data grids; network routers; and many software as a service (SaaS) offerings. All of these can be on-premises, in the cloud, or part of a hybrid-cloud architecture.

Extreme VolumesData volumes are considered in terms of the rate at which new data is generated.

At ScaleStreaming integration solutions need to scale up and out.

High ThroughputThis involves the collection of huge amounts of data as it’s generated, then moving, processing, and delivering at the same rate to eliminate any lag with respect to the source data.

Low LatencyA goal of streaming integration is to minimize latency while maximizing throughput and limiting resource consumption.

ProcessingDuring processing, some data might need to be eliminated, condensed, reformatted, or denormalized. These tasks are achieved through processing the data in memory, commonly through a data pipeline using a combination of filtering, transformation, aggregation and change detection, and enrichment.

FilteringFiltering can range from simple (only allow error and warning messages from a log file to pass through), intermediate (only allow events that match one of a set of regular expressions to pass through), to complex (match data against a machine learning model to derive its relevance and only pass through relevant data).

TransformationIt involves applying some function to the data to modify its structure.

Aggregation and Change DetectionIt is the common term for condensing or grouping data, usually time-series data, to reduce its granularity, involving basic statistical analysis, sampling, or other means that retain the information content, but reduce the frequency of the data.

EnrichmentIt involves joining real-time data with some context (about devices, parts, customers, etc.), to turn into valuable information.

AnalysisThis analysis falls into a few broad categories:

● Time-series and statistical analysis ● Event processing and pattern detection ● Real-time scoring of machine learning

algorithms

CorrelationIt joins this data from multiple sources together, based on the relationship between multiple data streams, such as the way it is correlated through time, data values, location, or more complex associations.

Continuous DeliveryAfter data has been collected, processed, correlated, and analyzed, the results almost always must be delivered to a filesystem, database, data warehouse, data lake, message queue, or API, either on-premises or in the cloud. The only exception is when the data is being used solely for in-memory analytics.

Value Data is a collection of unprocessed facts, whereas information is data processed in such a way as to give it value.

Visibility It is the way in which we can present data to the user, often in an interactive fashion, like visualizations in the form of charts and tables, combined together in dashboards.

Reliable To be reliable, the system must do what you expect it to do, operate continuously, and recover from failures.

Verifiable The principle is that you need to track data from genesis to destination and verify that it has successfully been written to any targets.

Our Offering

Our Digital Transformation Platform consists of Data Orchestration Platform and Customer Engagement Platform for accelerating the Digital Transformation Journeys for MNOs and Enterprises.

Our Data Orchestration Includes: ● Big Data Orchestration ● Streaming Data Integration (e.g. Kafka) ● Middleware ● IoT Data Orchestration ● Data Portal / Data Caching

● AI/ML Data Orchestration/Preparation

Customer Engagement includes ● Omni Channel Enablement ● Mobile Campaign Management & Best

Next Offer ● Customer Aware Mobile Marketing ● Eligibility and Product Catalog ● Automated Collections (Dunning) ● Customer Analytics

Digital Charging & Rating ● Charging /Online Charging ● Rating/Online Rating ● Charging /Rating over the Cloud/ SAAS

We are also among market leaders in AI/ML powered Optimus Revenue Protection solutions for Fraud Management, Revenue Assurance, Credit Risk Management, Anti Money Laundering (KYC) and Application Risk.