Customer analytics allow you to learn more about the people buying your products including the most valuable ones to your business. Their activities – whether making purchases, calling customer service, engaging with online and in-store experiences – leave a digital footprint providing valuable insight into the customer journey when harnessed properly.
Easily analyze various sources of customer analytics and take action. Instead of waiting to collect and transfer customer data from a range of geographically distributed sources, quickly analyze and apply machine learning to newly generated data. Identify demographic, regional and seasonal trends to deliver the most relevant offers that drive additional sales. Predict customers most likely to leave and develop an appropriate strategy that will convince them to stay.
The data warehouse is a critical system for providing business intelligence, tying together large amounts of cross-departmental data. However, a large number of data warehouse initiatives ultimately fail as they are unable to deliver on the promise of unifying different systems and data formats. Or are unable to offer a fast, easy means for extracting insight from data once it’s stored.
Overcome the problems with traditional, centralized data warehouses and knock down your data silos. Keep data close to where it’s generated (on-premise, in the cloud, and/or at the edge). Input data simply with flexible, configurable input for any business application and data type. Access all data, whether seconds or years old, using standard SQL commands and BI tools. Realize low TCO with minimal hardware resources and by not requiring a DBA. Obtain the fastest response to any query.
It’s forecasted that more than two thirds of all IoT workloads will be initially analyzed at the edge. Analysis at the edge is required because the time required to transfer data back to a centralized location takes too long for time-sensitive applications. In addition to those generated by IoT devices, other forms of data also benefit from edge/fog computing architectures to address similar latency, cost and privacy concerns.
Harness a federated, distributed analytics architecture that moves your storage, processing and data analytics closer to where data is generated. Perform stream processing on data that requires automated, real-time analysis. Aggregate data from devices and gateways to retain data cost efficiently at the edge. Deploy in combination with edge computing software within devices and gateways to unlock new insights and to connect edge data on a global scale.
With more than 30 billion consumer and industrial IoT devices expected to be connected to the Internet by the end of this decade, the Internet of Things (IoT) has the potential to transform nearly every industry in the foreseeable future. The majority of IoT devices, however, collect data that never gets analyzed to its fullest extent. Large industrial devices generating terabytes of data daily put new requirements on the analytical infrastructure required to process, scale and turn data into insight.
Make sense of the enormous volumes of data generated by IoT devices and achieve desired business outcomes. Create new data sets that provide a competitive advantage and new services to offer customers. Seamlessly integrate IoT devices with different formats and analyze instantly close to where the data is generated. Perform automated stream processing in real-time and send alerts based on incoming IoT data. Analyze IoT devices with the ease of SQL syntax and without any additional expertise in embedded system design or specialized programming languages.
Data can’t always be easily shared across geographic borders due to government policies – making it expensive or often illegal to ship data outside of the country. This requires data to be kept locally within the country’s borders. There are other instances where the sensitivity of data restricts transferring of data to another location or a multi-tenant platform.
Contain your data within any geography. Analyze data from afar, from a central location, without having to transfer data across geographic borders. Apply encryption to all data for security and analyze any type of data that has to be kept local such as those which contain personally identifiable, financial and government information.
Machine learning will have a profound impact on business. But machine learning models are only as good as the data used to build them. For machine learning to be highly accurate, it requires algorithms that have been trained and refined on very large sets of data. Data scientists are faced with an unwanted tradeoff on deciding which data to sample – because it is often impractical to move it all to a central location.
Overcome the problems associated with having to sample data so you can develop highly accurate machine learning applications. Train and refine machine learning algorithms on a complete, continuous dataset applied directly against a geographically distributed database. Apply machine learning to improve business operations such as predictive maintenance, sales forecasting, inventory management and customer support. Reduce risk and lost revenue by applying to security analysis and for identifying fraudulent transactions.
Cybersecurity incidents cost individual companies millions of dollars in lost revenue and brand damage. To proactively combat against these incidents, it requires detection of suspicious behavior in real-time and also the ability to retain data for extended periods of time to perform reconnaissance and governance. Surveillance applications require real-time collection of all data derived from location aware devices, cameras and sensors.
Power your DIY and cloud-based security and surveillance services with an analytics infrastructure able to provide real-time, granular visibility into activity. Consume distributed data sources at network speed, retain for months or years while being highly responsive to broad and highly granular forensic queries. Respect the privacy of individuals not part of an investigation by not shipping sensitive data to a central location.