Introduction

The global Big Data Analytics software market size reached US$ 181.1 Billion in 2022, expecting to reach US$ 335.2 Billion by 2028, exhibiting a growth rate (CAGR) of 9.8% during 2023-2028. The Big Data industry is driven by a sharp increase in data volume. The global data consumption over telecom networks alone will nearly triple, from 3.4 million PB in 2022 to 9.7 million PB in 2027 (according to PwC & Omdia).

To make the most out of these data and their metadata, businesses leverage the functionalities of modern Data Analytics Solutions to gather, store and process huge volumes of information to further generate deep insights and proactively trigger intelligence-driven actions fast (preferably in real or near-real time). Despite all recent technological advancements that enable the operation of modern Data Analytics Solutions on larger volumes of data either in streaming and/or in batch processing mode, these frameworks pose significant business challenges to the stakeholders.

Challenges

Nowadays, stakeholders face several challenges during the selection of a new complete commercial Data Analytics Solution, or the replacement of an existing one. Indicative dilemmas affecting their final decision are illustrated below.

(Privacy) Cloud-based vs On-premises

Cloud-based solutions rely on third-party cloud service providers for infrastructure and services, but introduce concerns on privacy as data are stored on the cloud, and also their long-term cost is much more than on-premise solutions. In contrast, on-premises solutions are deployed within an organization’s local infrastructure, providing control over hardware, software, and data storage. Control over these components, however, comes with increased responsibility for maintenance, updates, and security measures.

(Cost) Licensed vs. Open-source

Licensed solutions, although fully supported by private companies, come at the expense of higher costs due to high license fees, limited customization options, lack of integration flexibility with third-party applications, and potential vendor lock-in. On the other hand, open-source solutions are built using publicly available source code, granting users the freedom to modify, redistribute, and contribute to the solutions’ development. This collaborative approach often results in rapid innovation, new products from private entities with a vast support community, and reduced licensing costs.

(Scope) Vertical vs. Horizontal

Vertical solutions are tailored to address the specific needs of a particular industry or business domain. These specialized solutions provide industry-specific data models, analytics capabilities, and integrations, enabling organizations to derive insights relevant to their domain. This may introduce limitations due to this specialization, restricting usage in some use cases from different industries. In contrast, horizontal solutions are designed to be versatile and applicable across various industries.

Our Big Data Analytics Solutions

Intracom Telecom addresses these challenges through a full suite of Big Data Analytics Solutions and its extensive knowledge on data engineering and data science, serving and guiding a variety of organizations for more than a decade. All these solutions, either built with open-source tools or licensed frameworks are horizontal and therefore cover different needs in all industries, while also satisfying divergent and vertical requisites for the development of any specialized application or data flow. In addition, they are designed to provide unmatched value with leading performance and scalability, but with minimum OPEX, for data on-premises and on the cloud, and for both batch and streaming processing applications.

Our solutions satisfy all principles of modern data architectures, such as:

• Data Warehouse: where large amounts of data from disparate data sources are aggregated and then stored structured in a unified data repository to support efficient querying, analysis, and eventually data-driven business decisions.
• Data Lake: where large amounts of structured and unstructured data in their raw, original, and unformatted form are stored in a centralized repository, leading an organization to advanced insight gains from unstructured data.
• Data Virtualization: where the applications retrieve and manipulate data without requiring technical details about the data, such as knowledge of the data format or the location where the data are physically located.
• Data Mesh: where the data ownership is distributed among different data domains within an organization; facilitating and accelerating the value extraction from data by applying domain-specific business knowledge dispersed throughout an organization.
• Data Lakehouse: where the best features from streaming and batch processing of both data warehouses and data lakes are efficiently combined, satisfying high performance and data integrity standards for advanced data analytics and machine learning workloads.

Benefits

Our comprehensive solutions empower businesses to harness the full potential of their data, make more informed decisions, and remain competitive in an increasingly data-driven world, by addressing the following critical aspects:

• Data integration and consolidation: By enabling organizations to integrate and consolidate data from disparate sources, such as databases, APIs, IoT devices, and web or document scraping, businesses can create a unified, holistic view of their operations, making it easier to identify patterns, trends, and potential areas for improvement.
• Scalability and performance: By handling the increasing volume, velocity, and variety of data generated by today’s businesses with features like horizontal scaling, distributed processing, and real-time analytics, our solutions can efficiently manage large datasets and deliver insights at the speed required for informed decision-making.
• Enhanced data analytics and insights: By offering advanced analytics capabilities, including ML, DL and AI processing, organizations derive actionable insights from their data using models to identify trends, optimize operations, and uncover hidden relationships within the data.
• Improved collaboration and data sharing: By centralizing or distributing data storage according to the organization’s needs, our solutions facilitate collaboration and data sharing among different teams within an organization. This improved collaboration allows teams to work together on projects more efficiently, fosters a data-driven culture, and ensures that everyone has access to the most up-to-date and accurate data for decision-making.
• Data security and compliance: By applying advanced built-in security measures, such as encryption, access control, and data loss prevention, to protect sensitive information from unauthorized access and misuse, our tools for data governance and compliance, assist organizations managing their data in accordance with industry-specific regulations and guidelines.

Indicative Use Case

Our Big Data solutions connected with Intracom Telecom's Cognitiva™ Enterprise-ready AI Application Suite provide advanced analytics capabilities based on ML, DL and AI technologies to solve major business challenges, such as Customer Network Experience perception, leading to (2) maximized revenue through optimized network planning, and (2) minimized churn through proactive customer care.

Our specialized application for customer network experience, Cognitiva™ Customer Experience Suite, is designed as a unified framework on the grounds of satisfaction metrics that relates deterministic RAN performance to Customer Network Experience. Cognitiva™ allows operators to shift to a customer-centric paradigm that enables them not only foresee RAN disruptions with anomaly detection but also assess their impact on customer experience, identify their root cause and recommend resolution actions. This proactive framework is powered by Big Data to drastically reduce human intervention in RAN management, augment operational expenditure (OPEX) efficiency and optimize capital expenditure (CAPEX) network investments.