According to IDC, the global LTE/5G private wireless infrastructure market is expected to reach $8.3 billion by 2026. This market is expected to grow at a five-year compound annual growth rate (CAGR) of 35.7% during the 2022-2026 forecast period. . Telecommunications or telecom companies face many operational challenges, including digital connectivity everywhere, deployment of 5G and software-defined wide area network (SD-WAN) networks, and maintaining customer loyalty. Faced with these challenges, many organizations are turning to data-driven decision making.
Knowing that 90% of their data has a spatial or temporal component, telecom operators are considered to be rich in spatial data. These include, for example, the movement of mobile customers over time, the location of cell towers and booths, broadband networks, mobile network coverage, fiber optic networks, and streets providing navigation and traffic information. Faced with the abundance of data being consumed, it is difficult for operators to make the most of all the spatiotemporal data at their disposal.
But more data means more problems. In this context, the telecommunications market remains reluctant to rely on its data for decision making. More and more telcos are questioning whether their decisions are based on accurate data and whether they can use localization in their analytics and modeling processes to gain a competitive advantage.
In addition, the pandemic has created new challenges and needs. Organizations are implementing technological innovations to support the analysis of new concepts such as the Internet of Everything (Internet of Everything or IoT), which helps to understand the omnipresence of digital technologies in people’s lives and data management, and IoB (Internet of Behavior).
Faced with an ever-growing amount of data, it’s more important than ever to have a clear data strategy when it comes to geospatial analytics. It plays a key role in business forecasting, risk mitigation and competitive advantage. This builds trust in real-time data for informed decision making.
Geospatial intelligence is more than visualizing data on an interactive map. It also explains how to operationalize and analyze spatiotemporal data easily and efficiently, helping organizations analyze data with heat maps and whites and hotspots, calculate distance matrix between points of presence, analyze time series of consumer data, and calculate cell tower line of sight. .
With geospatial analytics, operators can assess whether their offerings are targeting the right targets. They can predict areas where the number of potential customers could increase and check whether organizations are getting the bandwidth they need. Providers can also estimate the number of connected devices within an area (and the impact on bandwidth), calculate distances and costs between broadband access points, or figure out how to enrich data and get more revenue from other markets.
Development of artificial intelligence (AI) and machine learning (ML)
Adding information from location analytics to AI/ML, data science, and predictive analytics helps you make profitable decisions. Adding spatial analytics to AI/ML can really help the telecom industry build robust operating models. Which help understand consumer behavior, predict where to establish a presence on the network, determine where customers are most likely to purchase new 5G services. In addition, meeting changing consumer expectations helps win new subscribers, anticipate and prevent unsubscribes. Finally, predicting network performance using the data your organization already has gives you insight into the usage patterns of billions of subscribers and allows you to fine-tune your strategy.
Ultimately, the challenge of geospatial intelligence is to process and manage the vast amounts of dynamic data in space and time that geospatial intelligence tools can solve.
Data, strategic monetary value
Geospatial analytics also help monetize valuable data and services. Businesses collect vast amounts of data from the corporate network, connected devices, and IT. In addition, anonymization, spatial processing, and enrichment of geo-referenced and temporal data is a successful geospatial intelligence strategy. Thus, the sale of the obtained extended data sets can generate significant income.
For example, traders can learn more about their potential markets by looking at where and when people come. Anonymous mobile data can track high-value shoppers passing through a particular area at a particular time of day or on a particular day of the week, giving insight into the ideal outlet location.
Auto insurance companies can more accurately predict risks and calculate premiums better by analyzing rich IT data related to driver behavior and habits. Data such as destinations visited, roads driven, vehicle speed, time of day, and day of the week help establish profiles of safe or reckless drivers. Dealerships can further analyze the links between vehicle driver profiles and their driving habits and use this information to improve their operations and increase customer satisfaction by matching the right vehicle to the right customer.
For telecom operators, the ongoing challenge is whether they can deliver on the promise of a more efficient network with more bandwidth, by coping with the unprecedented impact of streaming services and an interconnected world of sensors, vehicles and people. Data plays a vital role. The ability to integrate, validate, and manage data while performing large-scale analytics in cloud environments is critical to accelerating the development, deployment, and deployment of future mobile and broadband services. By combining data quality, geocoding and spatial processing, and giving customers, sales and marketing teams access to self-service tools, businesses remain competitive and profitable because they can efficiently manage and share data, target new subscribers, and improve the quality of their services. service.