Service Level Agreement (SLA)
A service level agreement is a formal agreement between two parties, usually a service provider and customer, that contains requirements, responsibilities and standards of service. SLA establishes communications between the party and provides clarity on any responsibility between the parties. Thus, it helps to reduce conflict between the parties and controls the overall expectations.
Basic components of SLA
SLA contains some essential elements to serve as a well-organized arrangement. They are:
- Overview: serves as an introduction to the agreement and includes necessary information such as names of the parties, starting and ending dates of the agreement.
- Goals and objectives: it mentions the overall purpose behind drawing up the contract and ability to obtain an agreement.
- Stakeholder: it mentions all the probable parties who may be involved in the transactions between the parties (Peoples et al.,2019).
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- Service agreement: this is the essential part of the transaction. It contains vital components for which the service provider is responsible, such as the services offered with the agreement, customer requirements, service provider’s requirements in case of incidents, and situations and protocols under which services can be changed.
- Service management: includes service availability and service requests.
- Periodic review: this states that the SLA and all the Key performance indicators (KPIs) mentioned under it would be reviewed regularly.
- Termination of contract: this part covers the situations under which the agreement can be terminated or deemed invalid. Both parties should mention their notice periods.
- Signature: this part contains signatures from representatives of both the parties agreeing to follow the contract and its every detail.
Predictive analysis
Predictive analysis is the use of previous data, machine learning techniques, statistical modelling and other methods to predict the probable future outcome of a process based on pre-recorded data. With the help of predictive analysis tools and models, companies can predict future trends in businesses by analyzing previous and current data with maximum levels of precision. This can be useful to recognize patterns in large data samples, which may help further in identifying potential risks and opportunities for a company.
Predictive analysis has a wide range of applications in various industries, who use it for detecting frauds and studying consumer buying patterns (Kumar et al., 2018). Some of the examples include hotels, banks, retails outlets, oiling corporations, manufacturing plants, healthcare industries, and others.
Importance of predictive analysis in IT operations
IT professionals and managers can use predictive analytics within the company in many ways. Predictive analysis can help in improving pattern detecting operations, which can help in detecting cyberattacks on the company. High-performance predictive analysis methods can identify patterns in computer systems and networks and can spot any anomalies (Manuel, Cordeiro, & Silva, 2017). This can be done in real-time and quickly to prevent external attacks on the system.
For many companies, predictive analysis can also help in regulating and forecasting inventory resources. This improves operations and improves productivity. Predictive analysis helps to collect analytical resources from a various business point within the company. Also, it can be used for advertising campaigns by studying customer buying patterns.