Intelligent Analytics: Types and Benefits

We live in a time where sophisticated analytics are the norm. In the age of big data, analytics, and artificial intelligence, intelligent analytics is a new paradigm. 

 

Intelligent analytics is a type of intelligence that combines artificial intelligence, analytics, data, information, knowledge, intelligence, and wisdom to provide smart services for bettering business, management, and governance utilizing sophisticated ICT computing.

 

Work, life, business, management, and organization, as well as healthcare, finance, e-commerce, and web services, have all been transformed by intelligent analytics. 

 

Healthcare, online services, service computing, cloud computing, the Internet of Everything (IoE), and social networking computing all benefit from it.

 

What is Intelligence Analytics?

 

Intelligent analytics (IA) is a science and technology that collects, organizes, and analyzes big data, big information, big knowledge, and big intelligence as well as big wisdom in order to discover and visualize patterns, knowledge, and intelligence as well as other information within big data, information, knowledge, and intelligence using big analytics, artificial intelligence (AI), and intelligent systems.

 

At the very least, intelligent analytics encompasses intelligent data analytics, intelligent information analytics, intelligent knowledge analytics, and intelligent wisdom analytics, all of which are supported by intelligent statistical modeling, machine learning, intelligent visualization, and intelligent optimization. 

 

1. Big data, intelligent big data analytics, intelligent data analysis, intelligent data warehousing, intelligent data mining, and intelligent data visualization are all examples of intelligent data analytics.

 

2. Big data, intelligent information analysis, intelligent information warehousing, intelligent information retrieval, and intelligent information visualization are all examples of intelligent information analytics. 

 

3. Big knowledge, intelligent knowledge analysis, and engineering, intelligent knowledge systems, intelligent knowledge retrieval and warehousing, and intelligent knowledge visualization are all examples of intelligent knowledge analytics. 

 

4. Big wisdom, intelligent wisdom analysis, engineering, intelligent wisdom systems, intelligent wisdom retrieval, and intelligent wisdom visualization are all examples of intelligent wisdom analytics. 

 

Intelligent analytics for big data, information, knowledge, intelligence, and wisdom are now classified as intelligent descriptive analytics, intelligent diagnostic analytics, intelligent predictive analytics, and intelligent prescriptive analytics. 

 

Because of the rapid growth of big data and big data analytics, intelligent big data analytics has achieved astonishing results. (source)

 

Because we are still engrossed in the age of big data and ignore the dawning age of big information, big knowledge, big intelligence, and big wisdom, intelligent information analytics, intelligent knowledge analytics, and intelligent wisdom analytics have received little attention in academia and industry. 

 

Big data is built on a foundation of large amounts of information, knowledge, and intelligence or wisdom. As a result, we are still in the early stages of the developing age of big data, big knowledge, big intelligence, and big wisdom, and we are concerned. 

 

(Also read about: Applications of Big Data Analytics in Cybersecurity)

 

3 Types of Intelligent Analytics

 

There are various ways that may be employed while examining the data. Intelligent analytics will come in helpful whether you're attempting to develop behavioural patterns or describe and categorize a specific problem. 

 

Three of the most prevalent forms of intelligent analytics are listed below.

 

1. Descriptive analytics

 

The most popular and fundamental type of analytics used by businesses is descriptive analytics. Any part of the organisation may utilise descriptive analytics to keep track of operational performance and trends. 

 

Descriptive analytics includes KPIs such as year-over-year percentage sales rise, revenue per customer, and the average time consumers take to pay bills. Descriptive analytics is used in financial statements, numerous reports, dashboards, and presentations.

 

Examining hundreds of individual sales transactions for the most recent quarter will not tell you how much consumers spent on average or if total sales were greater or lower than in previous quarters. 

 

Descriptive analytics is the initial step towards making sense of such unstructured data. It typically uses basic mathematical techniques to generate summary data — such as average revenue per client — in order to have a better grasp of your company's current situation.

 

2. Predictive analytics

 

Predictive analytics is a type of advanced analytics that uses historical data, statistical modelling, data mining techniques, and machine learning to create predictions about future events. Companies use predictive analytics to identify hazards and opportunities by looking for trends in data.

 

Big data and data science are frequently linked to predictive analytics. Data is awash in today's businesses, which may be found in transactional databases, equipment log files, pictures, video, sensors, and other data sources. 

 

Deep learning algorithms and machine learning algorithms are used by data scientists to detect patterns in the data and generate predictions about future occurrences.

 

Linear and nonlinear regression, neural networks, support vector machines, and decision trees are examples of these techniques. Predictive analytics learning may then be used to prescriptive analytics to drive actions based on predicted outcomes.

 

3. Prescriptive analytics

 

Prescriptive analytics is a statistical strategy for determining the best course of action or action required for a certain circumstance based on data. Both descriptive and predictive analytics are used in prescriptive analytics, but the focus is on actionable insights rather than data monitoring. The output of predictive analytics algorithms is the input of prescriptive analytics.

 

You not only foresee what the future holds, but you also use that foresight to determine the best course of action. Prescriptive analytics is a statistical method used to provide suggestions and enhance decision-making based on the computational outputs of algorithmic models. According to a more formal definition.

 

Consider the following scenario: a human resources manager is entrusted with upskilling a team under his supervision. He recognizes, however, that a team member who lacks a certain skill set may not be able to participate in the upgrade course he envisions. 

 

Prescriptive analytics may be useful in determining how he should proceed. If a team member lacks the requisite abilities, an algorithm can recognize them and provide them with an automatic recommendation to learn the necessary skills in another course before enrolling in this one.

 

Intelligent analytics benefits for enterprise

 

For business functions, easy-to-use, intelligent analytics tools become a necessity. This is particularly true in IT service management operations such as planning, providing, managing, and upgrading end-user IT services.

 

It's a huge difficulty to access and evaluate data in one location in a complex, heterogeneous ecosystem of tools, apps, and infrastructure components. This lack of visibility may be a significant impediment to efficient monitoring, lowering productivity and driving up service delivery costs. Intelligent analytics technologies can be useful in this situation.

 

• Increased productivity and service reliability

 

Racking memory and disc use patterns might show unique patterns in a company's storage needs. It can assist IT departments in predicting storage capacity requirements. 

 

According to Accenture researchers, having a comprehensive grasp of storage capacity utilization trends helps firms to establish efficient backup plans in the event of an emergency. 

 

IT departments may monitor peak business activity and manage technicians' workloads using real-time dashboards. This ensures that IT personnel are adequately staffed and do not become overburdened with routine requests. It also boosts service desk efficiency and lowers costs inside the company.

 

• Superior customer experience

 

Analytics technologies can assist in estimating the likelihood of service disruptions and average incident ticket levels. They may also be used by IT teams to investigate the relationships between ticket resolution times and reopen rates. 

 

High quantities of reopened tickets have a detrimental influence on technicians' work schedules and SLA compliance.

 

• The reduced total cost of ownership and maintenance

 

IT analytics systems can track apps and data, allowing businesses to see patterns in usage and make more strategic decisions. Through data analysis, businesses are beginning to see the true cost of operating their businesses. 

 

IT departments may plan and optimize the use of cloud infrastructure to save money. By performing internal evaluations, analysts may also assist organizations in planning for future license purchases and maintaining compliance rates.

 

• Improved SLA compliance

 

A real-time SLA dashboard may track and allocate tickets, as well as compare service desk performance to end-user service levels. Teams may use this data to define realistic SLA targets, automate and route ticket assignments, convey the consequences of SLA breaches, and plan escalations ahead of time. 

 

Understanding the number of alerts and requests may assist businesses in planning and optimizing resource allocation so that high-priority problems are handled quickly and SLAs are met.

 

• Increased network uptime and resource application

 

It's critical to keep an eye on alerts for unusual trends and swings in network and resource usage. The IT department reaps various benefits from proactive application and network management. Being proactive in operations management can be difficult for many IT teams, but using unified insights can help managers assure high availability.

 

Big data is widely acknowledged as the engine that propels smart business choices. Big data may assist businesses in determining ways to avoid bad occurrences or replicate successful ones once they have occurred. 

 

Because of a combination of variables, such as higher-power computer systems, reduced storage prices, cloud-based services that enable endlessly compute and storage power, and developments in the capabilities of analytics systems, we now utilize big data to see forward.

(Also read: 10 Top Data Mining Tools)