The data collected from different sources must be functional according to the anticipations and needs of enterprises. It is critical to render the data beneficial for corporations by making them available for use through several analytics. Redington makes data valuable for corporations by means of the analytical solutions provided in cooperation with global brands.
AIOps or artificial intelligence for IT operations is a term first coined by Gartner. It is the application of advanced analytics—in the form of machine learning (ML) and artificial intelligence (AI), towards automating operations so that your ITOps team can move at the speed that your business expects today. AIOps uses artificial intelligence to simplify IT operations management and accelerate and automate problem resolution in complex modern IT environments.
User Behaviour Analytics detects unknown threats and anomalous behavior using machine learning. UBA has three main advantages: * Advanced Threat Detection: Discover abnormalities and unknown threats that traditional security tools miss * Higher Productivity: Automate stitching of hundreds of anomalies into a single threat to simplify a security analyst’s life * Accelerate Threat Hunting: Use deep investigative capabilities and powerful behavior baselines on any entity, anomaly or threat
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (columns) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set.
The Internet of things (IoT) describes the network of physical objects—“things” or objects—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet. Things have evolved due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the "smart home", including devices and appliances (such as lighting fixtures, thermostats, home security systems and cameras, and other home appliances) that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers. IoT can also be used in healthcare systems.
İBusiness intelligence platforms enable people import, clean, and analyze data from databases, emails, videos, survey responses, and more. These data analyses provide mobile, desktop and real-time business intelligence so decision makers can act on insights to improve their organization. BI platforms allow users to customize dashboards, create stunning data visualizations, build scorecards, and compare them to key performance indicators (KPIs).
