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% over the forecast period 2022-2026. Telecommunications, or telecom, companies face many operational challenges, including building digital connectivity everywhere, deploying 5G and Software-Defined Wide Area Network (SD-WAN) networks, and retaining customer loyalty. clients. 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 reputed to be rich in spatial data. These include, for example, the movements of mobile customers over time, the location of cellular towers and booths, broadband networks, mobile network coverage, fiber optic networks and streets providing navigation and traffic information. Faced with the profusion of data consumed, operators find it difficult to make the most of all the spatio-temporal data at their disposal.
But more data means more challenges. In this context, the telecom market is still reluctant to rely on its data to make decisions. More and more telcos are wondering if their solutions are based on accurate data and if they can leverage localization in analytics and modeling processes to gain competitive advantage.
In addition, the pandemic has created new challenges and needs. Organizations are adopting technological innovations to support the analysis of new concepts such as the “Internet of everything and anything” (Internet of Everything or IoE) which makes it possible to understand the omnipresence of digital in the lives of individuals and data management, as well as the IoB (Internet of Behaviour).
Faced with the ever-increasing amount of data, it is more essential than ever to have a clear data strategy when it comes to geospatial intelligence. It plays a key role in business projections, risk mitigation, and obtaining competitive advantages. This builds confidence in operational data for informed decision-making.
Geospatial intelligence is more than how to visualize data on an interactive map. It also covers how to operationalize and analyze spatiotemporal data easily and efficiently, helping organizations analyze data through heatmaps and white and hot spots, calculate a distance matrix between points of presence, analyze a time series of consumer data, and calculate the line of sight of a cell tower.
Thanks to geospatial intelligence, operators are able to assess whether their offers are aimed at the right targets. They can predict areas where the number of potential consumers is likely to increase and verify that organizations are getting the bandwidth they need. Providers can also estimate the number of devices connected within an area (and affecting bandwidth), calculate distances and costs involved between broadband site locations, or investigate how to enrich data and get more profit from other markets.
Development of artificial intelligence (AI) and machine learning (ML)
Adding insights from location intelligence to AI/ML, data science, and predictive analytics drives profitable decisions. Adding spatial analytics to AI/ML can indeed help the telecom industry create robust operational models. Which help to understand consumer behavior, predict where to establish a network presence, determine where customers are most likely to purchase new 5G services. In addition, meeting the fluctuating expectations of consumers helps to win new subscribers, to anticipate as well as to prevent unsubscriptions. Finally, predicting network performance using the data the organization already has provides insight into the usage patterns of billions of subscribers and enables fine-tuning of strategy.
Ultimately, the challenge posed by geospatial intelligence lies in the processing and management of massive volumes of dynamic data in space and time, which geospatial intelligence tools can solve.
Data, strategic monetary value
Geospatial intelligence also helps monetize high-value data and services. Businesses collect a huge amount of data from the corporate network, connected devices and IT. In addition, anonymization, spatial processing and enrichment of georeferenced and temporal data is a successful geospatial intelligence strategy. The sale of the resulting enriched datasets can thus generate considerable revenue.
For example, traders can learn more about their potential markets by looking at where people go and when. Anonymized mobile data can track high-value consumers passing through a particular area at a specific time of day or on a specific day of the week, giving insight into the ideal location of an outlet.
Auto insurance companies can more accurately predict risk and better calculate premiums by analyzing rich IT data related to driver behavior and habits. Data such as destinations visited, roads taken, vehicle speed, time of day and day of week help establish profiles of safe or reckless drivers. Dealerships can further analyze the links between vehicle driver profiles and their travel 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 greater bandwidth, while coping with the unprecedented effects of streaming services and an interconnected world of sensors, vehicles and people. Data has a vital role to play. The ability to integrate, validate, and manage data while performing large-scale analytics in cloud-native environments is critical to accelerating the development, deployment, and adoption of future mobile and broadband services. By combining data quality, geocoding and spatial processing – while enabling customers, sales and marketing teams to access self-service tools – businesses stay competitive and profitable because they are able to effectively manage and share data, target new subscribers and improve the quality of their service.
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