Data & Analytics

Data management is one of the most important aspects of administering telecommunications as network elements, customer care systems, application and services all require instructions to operate effectively. While some data is static, such as the location of a router or the core features of a subscriber, other data is dynamic and interrelated such as user generated data in social media.

Big data technologies, tools, and techniques have been applied with great success in many industries to gain insights and to support data analytics necessary for decision making. The term “big data” pertains to a combination of complex physical infrastructure, data collection/mining techniques and integration, data security infrastructure, data storage management, and analytics, and data consumption.

Big data technologies are needed when there is a massive volume of data (too large for traditional data processing methods) and/or involve complex computations (beyond the scope of traditional data management tools), and/or require non-obvious (unstructured data) inferences between disparate data.

Various artificial intelligence technologies may be applied towards big data analytics to improve the ultimate conclusions, recommendations, and decision making. This is especially important when data veracity is in question, which is often the case with unstructured data.

A representative sample of Mind Commerce data analytics reports that cover a range of data management and information services topics include:

• Data Center Storage Market Outlook and Forecasts
• Big Data in Financial Services Industry: Market Analysis and Forecasts
• Enterprise Data Management: Business Intelligence, Analytics, and Data Discovery
• Big Data in Leading Industry Verticals: Retail, Insurance, Healthcare, Government, and Manufacturing

Cloud infrastructure is perhaps best known today for support of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

With virtually everything stored in the cloud, and accessible as a service, there is an almost limitless market for “Data as a Service” (DaaS) in which anything may be stored in a database. The data itself may be either structured or unstructured (e.g. requiring big data technologies) and the mean of realizing valuable information from the raw data will vary from solution-to-solution and scenario-to-scenario.

In addition to centralized cloud services, it is also important to consider cloud computing at the edge of networks. For example, the term “fog computing” was coined by Cisco to refer to a decentralized computing network in which a portion of computational infrastructure is dispersed to the edge of networks. Fog Computing is closely associated with Internet of Things (IoT) networks as it is anticipated that there will be a need for many distributed computing nodes throughout IoT systems.

Another example of distributed cloud computing is Multi-access Edge Computing (MEC), which is a term defined by the European Telecommunications Standards Institute (ETSI) to refer to edge computing in wireless networks including both cellular and non-cellular wireless. ETSI has defined MEC to support IoT as well as many other wireless and wired network applications and services.

Data analytics at the edge of networks is very different than centralized cloud computing as data is contextual (example: collected and computed at a specific location) and may be processed in real-time (e.g. streaming data) via big data analytics technologies.