Making Analytics Smarter: How Smart Data Fuels Effective Analytics
For service providers, smart data is key to analytics, automation, and machine learning.
For communication service providers, the wealth of untapped data within their networks can provide valuable insights and drive new opportunities—if it can be harnessed effectively. That, however, is no easy task. If you want to build from the most accurate data, IP packet data provides the best source of truth, because it represents what actually happened on the network. The sheer volume of data being captured, however, makes it nearly impossible to distill the relevant data and mine it for actionable insights.
This is where smart data comes in. By extracting and parsing the payloads and adding intelligence to packets, you can produce key performance indicators (KPIs) that power smart analytics. In this way, smart data begets smart analytics that service providers can use to take full advantage of artificial intelligence (AI) and machine learning (ML) tools.
Enriching Data Lake Operations
By utilizing smart data with smart analytics, service providers can bring enriched, multidimensional information to enhance data lake operations. Data lake are a centralized repository containing all of a provider’s unstructured, semi-structured, and structured data, which is then used for downstream applications. A service provider’s data lake already contains useful dimensions, such as subscriber and device identifiers. This data can be further enriched with information from the user plane and control plane packets to include network-specific details, including equipment vendor, radio access technology (RAT) extended geographic and application identifiers, network and device behavior, user experience, and more.
This enriched data record offers a new level of visibility that eliminates borders between vendors, radio generations and network types, handsets, and other technologies such as over-the-top video. Service providers can customize the feed, exporting only the data sets they need, reducing data size by removing fields they don’t want, and hiding personally identifiable information (PII) from records.
This refinement also lowers data lake costs by reducing feeds down to the information that is specifically useful to various functional groups of the service provider.
Enabling Data Science
When smart data is combined with smart analytics, service providers can make clean and highly curated data available for use by AI and ML network automation orchestration tools, ensuring high-quality output. Without smart data, the output is uncertain. To put it bluntly: garbage in, garbage out.
Smart data is key to enabling effective data science. Smart data powers new algorithms and automated analytics that provide observability into a provider’s services as well as data awareness beyond traditional operational insights from fixed workflows. The components of smart data are the foundation that allows data scientists to build data applications.
For data scientists, the ability to see inside a service and understand what’s happening under the hood based on available clues is invaluable. Smart data allows data scientists to solve business questions. A centrally managed data lake enables service providers to apply custom policies, information lifecycle rules to whole swaths of data all at once. Then different data sources can be blended together, exposing unique and interesting opportunities for the business.
When smart data is used to create smart analytics, service providers are able to make the most of their data lakes to enable all data stakeholders—from operations to data scientists to business users—to answer their troubleshooting, analytical, and executive questions.
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