As the world grapples with the escalating climate crisis, many industries are re-examining their operations to identify and implement sustainable practices. The telecommunications industry is no exception. Telecom companies face growing pressure from consumers, investors and regulators to reduce their carbon footprint and achieve net-zero emissions. This shift towards sustainability aligns with environmental responsibility and presents lucrative business opportunities for telecoms.

Sifting through vast amounts of data for usable information is both challenging and time-consuming for independent software vendors (ISV) in today’s fast-paced market. But without a continuous search for timely insights, you risk your end-users missing critical business opportunities, or failing to address emerging issues in their data promptly, potentially leading to dissatisfaction and churn.

Data is often described with cliches like, it's "the new oil" or “the new air.” No matter how you describe it, there is no denying the increasing importance of data monetization across every industry. Forward-thinking organizations recognize data apps as both a revenue stream and a differentiated service to increase customer loyalty.

Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. The introduction of ChatGPT, Microsoft Copilot, Midjourney, Stable Diffusion and many more incredible tools have opened up new possibilities we couldn’t have imagined 18 months ago. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.

At Databox, we put our company values at the forefront of everything we do. Prioritizing customer impact is one of the values we focus on the most, so taking the time to really understand our customers is paramount and we employ multiple strategies, frameworks, and initiatives on a daily basis to achieve this. One of those strategies is our Customer Lifecycle Framework (CLF), which reflects our dedication to prioritizing the needs of our customers at every stage of their interaction with us.

Nearly every facet of society has felt the impact of AI since it burst into the mainstream in late 2022 with the public launch of ChatGPT. In 2024, the retail and consumer goods industry is expected to experience massive upheaval due to the proliferation of generative AI (gen AI) tools as well as changes in customer engagement and the general manner in which products are now sold.

‍For almost a decade now, Countly has been extendable through the plugin system. But it also had its cost. In this article, we will explore how it worked before, what the limitations were, and what we did to improve the situation.

Financial services companies are leveraging data and machine learning to mitigate risks like fraud and cyber threats and to provide a modern customer experience. By following these measures, they are able to comply with regulations, optimize their trading and answer their customers’ needs. In today’s competitive digital world, these changes are essential for ensuring their relevance and efficiency.

In the past, querying a database required Structured Query Language (SQL) skills, or knowledge of other database query languages, such as Kibana Query Language (KQL). Today, with the emergence of generative AI (GenAI), teams can query their analytic database using natural language — and get plain English results in return. Or, if you prefer to still use SQL, many teams use GenAI for database query optimization, making queries faster and more efficient.