Utilize Predictive Analytics for High Velocity IT Service Management

The recent improvement in IT management and the use cases

In order to increase business growth, it’s essential to analyze velocity to ensure performance efficiency and be prepared for potential risks and threats. Velocity exists in all business functions, including products of service, sales, and marketing. Customers’ needs and expectations gradually increase through time and organizations need frequent changes to align their business with these demands. Organizations have always relied on IT Services for conducting day-to-day operations, but IT Service Management is now being challenged to create, adjust, and deliver IT services faster than the speed of business (ServiceNow. 2019).

Organizations and potential businesses are starting to utilize new technologies and innovations to accelerate and strengthen the sales and marketing processes. Therefore, IT Service Management is now accelerating its value, from the traditional workflow that only delivers technology solutions to focusing more on delivering business value. With proper technology utilization and adjustment, the organization can develop a high velocity IT Service Management to improve visibility into operations and performance in the competitive business environment.

Data Science in ITSM

Exponential growth of data is fueled by the exponential growth of the internet and digital devices (Jeble. 2016). In addition, with the disruption of pandemic conditions, organizations are forced to remodel their IT operations to support business activity in the midst of complex and challenging environments. Organizations realize the potential within big data in generating valuable insights that could facilitate them in achieving business objectives.

As an effect, organizations begin to increase their investments, specifically in data analytics and data science to achieve primary business objectives and overcome challenges in the upcoming business environment. Several companies such as Microsoft, Amazon, and Spotify have applied data management to grasp more understanding on their customer behavior in order to develop personalized products and services. With the advancement in providing precise datasets and reports, the implementation of big data makes strategic decision-making in a company effortless and trouble-free.

Definition of Predictive Analytics

In the last decades, the field of big data has made great progress in the predictive analytics sector by employing advanced statistical modeling techniques and various different algorithms to support ongoing research (Wazurkar. 2018). Modeling, machine learning, data mining, and game theory are just a few of the statistical methods used in predictive analytics to assess current and past data and forecast future events.

According to Abbot (2014), Predictive analytics is defined as the process of discovering meaningful patterns using data recognition techniques, statistics, machine learning, artificial intelligence, and data mining. Predictive analytics primarily deals with predicting or anticipating future outcomes based on the mining of existing data (Jeble. 2016). To put it simply, predictive analytics develops a model to predict future outcomes based on variable inputs. Predictive analytics includes many statistical and other empirical methods that create various data predictions as well as different methods for assessing predictive power (Wazurkar. 2018). It doesn’t presume anything about the data, rather, predictive analytics employs statistics, machine learning, neural computing, robotics, computational mathematics, and artificial intelligence techniques to explore all the data, instead of a narrow subset of it, to ferret out meaningful relationship and patterns (Eckerson. 2007).

Predictive analytics is able to not only deal with continuous changes but discontinuous changes as well (Mishra. 2012). Predictive analytics can help companies optimize existing procedures, grasp a better understanding of customer behavior, identify unexpected opportunities, and anticipate problems before they happen (Eckerson. 2007).

Predictive Analytics in ITSM

Nowadays, marketing is the biggest user of predictive analytics with cross-selling, campaign management, customer acquisition, and budgeting and forecasting models top of the list, followed by attrition and loyalty applications (Eckerson. 2007). Predictive analytics is beneficial for the IT service management team to create a more resilient IT infrastructure by providing advanced statistical modeling techniques and various algorithms to gain valuable insights to the customers and market demands. Predictive analytics also supports all activities provided for the end-to-end service lifecycle, from design through deployment to continuous improvement and termination.

The concept of predictive analytics workflow has been introduced in ITIL, known as SKMS, or Service Knowledge Management System. SKMS is introduced to create a database containing information on everything IT needed easily to operate daily, such as configuration management database (CMDB) and combining operational data from incidents, problems, changes, service requests, procurement, contracts, etc, so that IT could realize the insights from collected data. SKMS is a group of systems, tools, and databases required for successful knowledge management. Once IT collected sufficient data, it could be used to generate appropriate decisions and demonstrate a level of operational expertise resembling insights. According to ITIL, the SKMS is comprised of all the other data retention/categorization mechanisms used by service management, including but not limited to:

a. Service Portfolio

b. Configuration Management System (CMS)

c. Configuration Management Database (CMDB)

d. Supplier and Contract Management Information System (SCMIS)

e. Availability Management Information Systems (AMIS)

f. Capacity Management Information Systems (CMIS)

g. Security Management Information Systems (ISMIS)

h. CSI Register

Technical Management Practices

Following the technological advancement, several tools are now provided to cover those needs and added with predictive analytics capabilities to create predictive capabilities and deliver extended functionality. Hence, the implementation of Predictive analytics is necessary to enhance IT Service Management process to monitor IT service deliverance, as well as predict and respond to incidents faster.

How Predictive Analytics can be beneficial in ITSM

a. Event Management

Event Management facilitates the early detection and even prediction of incidents, following problem management. Predictive analytics enables organizations to search the CMDB, events, incidents, and problem records using AI and affected configuration items to provide a high-level alert of its potential impacts. Predictive analytics also leverages IT Services performance by ensuring its availability and accuracy, as well as delivering better root cause determination and automation.

b. Incident Management

Predictive analytics enables incident management teams to identify the major incidents more quickly than standard call-processing, as well as analyzing several larger systematic issues that accelerate response to incident logging. Supported with virtualization, automation, and big data, predictive analytics specifically have empowered IT capacity planning to extend into day-to-day management at a more granular and forensic level, rather than only focusing on strategic activities. Predictive analytics gives organizations the freedom to manipulate data in a variety of ways, employing algorithms and pattern matching to analyze, and uncover information about an organization’s IT operation that might otherwise go unreported.

c. Change Management

Every time a change is reported against a CI, predictive analytics can calculate the likelihood of success as a “change success rate” by scanning the history of previous changes and counting the successful deployments. Supported by deep learning, predictive analytics are able to create a predictive model based on historical data based on the prior failure, including application logs, network logs, and error logs. The model will later learn those patterns and continue to monitor for similar circumstances and predict future failures before they happen.

d. Security Operations

Similar to event monitoring, it can be challenging for organizations to screen through hundreds of records to identify the most important vulnerabilities to fix when a vulnerability is reported to the NIST database and discovered by security scanning. Predictive analytics can resolve this and produce an impact rating for the organizations by understanding how the CI is used and fixing the most vulnerabilities first using AI and estimating its impacts.

Conclusion

In a complex, heterogeneous environment of tools, infrastructure and organizations, there are various methods available to analyse IT Service Management data. The collaboration of Data Science in accelerating IT Service Management will continue to be in the focus of public attention. There are many ways that could be utilized to improve predictive models that can benefitted to infrastructures and overall business objectives. It’s helpful for organizations to understand how Predictive analytics works and check for data readiness, as it takes minimum of 35,000 to 50,000 records to generate insights and reports. As mentioned, predictive analytics enables involved teams in IT Service Management to increase effectiveness in daily operations. Furthermore, the implementation of predictive analytics in IT Service Management provides several opportunities to study, investigate, and develop high-performing business model.

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