In the digitised world, businesses are finding innovative ways to utilise data. Data is abundant, and processing power is at an all-time high. This has facilitated the practical application of machine learning and natural language understanding.
ServiceNow Harnessing Data Science
ServiceNow, a digital workflow company, uses data science effectively to enhance businesses’ efficiency and customer satisfaction, even if these businesses need to gain extensive data science knowledge. So, how does ServiceNow achieve this? Let’s examine how data science principles apply to ServiceNow.
Data Science To Make Sense Of Data
Data science involves using scientific methods and systems to extract valuable knowledge and insights from structured and unstructured data. It spans different areas like machine learning, data mining, and predictive analytics, employing various methods such as classification, clustering, and regression to make sense of the data.
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ServiceNow’s Methods
Classification, clustering, and regression – are machine learning algorithms used in ServiceNow. Classification and regression fall under supervised learning, where the model is trained using labelled data. On the other hand, clustering is an unsupervised learning algorithm where the model identifies patterns in the data without needing labelled examples.
Classification
Classification is a fundamental concept in data science and machine learning. It belongs to supervised learning, a subset of machine learning where the model is trained on a labelled dataset.
ServiceNow classification is applied to sort, direct, and prioritise work automatically. This method becomes a part of the solution when the output variable contains discrete values. Enhancing problem resolution speeds contributes to improved customer service. Moreover, it reduces errors and costs, freeing staff to concentrate on more critical tasks.
The Similarity
In data science and machine learning, similarity refers to a set of techniques used to quantify how alike two data objects are. This can be particularly useful when recommending items or grouping interchangeable data points.
The similarity used in ServiceNow provides content recommendations to agents, thereby accelerating problem resolution. By identifying similar incidents, cases, and alerts, the system can propose new significant incidents or link them to existing ones. This process aids agents in resolving issues faster.
Clustering
Clustering is a concept in data science and machine learning that falls under the umbrella of unsupervised learning. Unlike supervised learning, where models are trained using labelled data, unsupervised learning methods like clustering deal with unlabelled data.
Clustering groups similar records together in ServiceNow. This approach enables easier identification of patterns and potential automation opportunities. It also facilitates prioritising the creation of articles for the Knowledgebase.
Regression
Regression is a statistical method used in data science and machine learning that falls under supervised learning. In regression, the aim is to predict a continuous or quantitative output based on one or more input features.
In ServiceNow, regression is used when there’s a need to predict continuous output variables. ServiceNow uses this approach to estimate variables like the time needed to resolve a problem. It allows for improved planning by forecasting future events.
Automation Discovery
ServiceNow’s Automation Discovery feature identifies optimal automation solutions. It helps highlight common ITSM use cases and offers detailed suggestions based on the analysed data.
A real-world example of these principles in action is seen in Accenture, which uses ServiceNow to manage a high volume of issues and requests daily. They leverage machine learning for automatic issue assignments, allowing their staff to focus on more complex tasks.
“Auto-assignment is allowing teams to better focus on complex incidents and to support increased demand with the same capacity.” — Tom Bruss, Director of Global IT for ServiceNow, Accenture
ServiceNow’s application of data science principles paves the way for businesses to work more effectively. As ServiceNow expands its data science capabilities, we can expect even more significant improvements in efficiency, innovation, and user experiences, marking a promising future for the digital workplace.
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Read my other articles on ServiceNow:
GenAI: Developing Impactful Consultancy Services as a ServiceNow Partner
The Advent of Generative AI in ServiceNow: Insights from Knowledge 23
Boosting ServiceNow Productivity with Synthetic Data
ServiceNow ATF: Role in test processes and accelerating digital transformation