Analytics Product Marketing Manager, Pivotal
Over the last five years, mobile network operators (MNOs) realized 15% lower compound revenue growth on average than other types of communication service providers. With few exceptions, MNOs globally have seen a long-term decline in average revenue per user (ARPU).
To reinvigorate growth, innovative MNOs are searching for new ways to exploit data to better serve their customers and drive new revenue opportunities. But pulling together data scattered across legacy transactional and BI systems is difficult and slow. Worse, these systems were not designed to analyze the volumes of unstructured and semi-structured data exhaust from user plane and control plane traffic. But this data offers the potential for deep, integrated insights for operational, customer care and marketing needs. Hortonworks HDB, powered by Apache HAWQ (incubating), is a Hadoop-native SQL database that provides a new approach for analyzing mixed petabyte-scale data with blazing speed for timely, customer-centric insights.
A number of market headwinds are having profound effects on MNOs’ ability to continue to grow revenue. These include:
To compensate, MNOs typically run pricing promotions or other financial incentives to improve subscriber acquisition. Unfortunately, the impact is usually short-term: financial incentives train subscribers to shop on price alone and position network services as commodity products. This tends to make subscriber churn even worse. MNOs instead need to find ways to position their services as high-value offers worthy of higher pricing.
Advanced analytics put the focus on customers
Innovative MNOs are trying to change the rules of the industry from competing on price to competing on quality of experience, in an attempt to keep subscribers loyal and justify premium margins for next-generation services. In addition to investing in advanced services like LTE and 5G, this includes looking for new ways to exploit MNO’s data assets for insight. Specifically, operators are enriching structured data from legacy BSS/OSS systems (e.g., CRM, billing, provisioning, device inventories, or trouble ticketing) together with unstructured and semi-structured data from clickstreams, data flows, signaling data, sentiment analysis, and voice of the customer solutions (e.g., post-call surveys). Innovative MNOs analyze these new data sets to support a number of emerging use cases, including:
MNOs’ legacy BI systems remain essential for user productivity with core analytics, but they are not optimized or capable of coping with the variability, velocity, and volumes of data generated by clickstreams, flows, and other unstructured sources.
This data also needs to be analyzed in time to be relevant and useful. For example, operators would typically expect their customer service agents’ dashboards to be updated with current performance statistics in a sub-minute time frame. A high degree of concurrency of requests for aggregation or filtering from different users running in parallel is also necessary.
As a result, business users struggle to apply general purpose BI tools to these new use cases. Querying tools in the Hadoop ecosystem are capable of working with petabyte-scale data, but they typically offer a reduced SQL dialect, no in-database machine learning functions, and limited ability for rapid, iterative querying.
HDB: The Hadoop-native SQL database to bridge BI and advanced analytics
To bridge the worlds of traditional BI and unstructured Big Data – and support the types of innovative use cases highlighted above – we offer HDB, a high-performance analytics database that runs within a Hadoop cluster. HDB combines the familiarity of a full ANSI SQL interface, the performance of a massively parallel processing (MPP) engine, and the power of in-database functions for advanced analytics, data science, and machine learning at scale.
To deliver faster time to insight, HDB executes SQL inside the Hadoop cluster. It operates directly on Hadoop Distributed File System (HDFS) data without translating queries to MapReduce. HDB can query all data in HDP, regardless of format. HDB can quickly and efficiently federate queries across Hive, HBase, and HDFS via the HAWQ eXtension Framework (PXF). It also works with file formats like JSON, Parquet, ORC, and Avro.
To deliver advanced analytics use cases, like predictive analytics, data scientists can access Apache MADlib (incubating), a library of highly parallel machine learning algorithms, from within HDB via familiar SQL syntax. By running analytics within the database, analysts can leverage the entire data set where it resides in the HDP cluster, rather than relying on sampling. This helps data scientists create and deploy better models more quickly.
For ease of management, HDB integrates with Hadoop ecosystem components such as Ambari system management and YARN for resource management, to provide a completely open, integrated, enterprise-class data platform.
Taking the low-risk road to analytics-driven innovation
HDB is proven with mobile operators. For example, one mobile operator wanted to achieve more real-time reporting on call faults, analyze network traffic patterns for optimization, and broker subscriber data on web and app usage to advertising partners. To meet their requirements for fast querying and analysis, the customer built an application using the Spring IO development framework, Pivotal GemFire for real-time event notifications, and HDB for analysis of aggregated data in Hadoop. With this advanced analytics application, the customer was able to improve reporting on call faults, and to broker analytics on customer footfall traffic for their enterprise customers.
In another case, an operator was suffering from poor network quality, dropped calls, and loss of customers. Its legacy data warehouse could not keep up with the volume of network data being generated — about 2 billion call detail records (CDRs) per day. A lot of valuable network data was simply dropped due to lack of storage and a lengthy ETL process. With HDB and HDP, the operator was able to mine all their CDRs, analyze dropped calls, and improve network service. Reports that had taken two months to generate are now delivered in a day. Better yet, the operator was able to do all this at a much lower cost than with their legacy data warehouse.
Innovative operators are starting to mine the vast troves of unstructured data now available to them to help develop compelling customer experiences and uncover new revenue opportunities. On February 7, join us for a webinar to learn how HDB’s in-database analytics enable advanced use cases in network operations, customer care, and marketing for better customer experience. Register now, and get started on your advanced analytics journey!