In this era of technology, business organizations heavily depend on data analytics tools to ensure business continuity. The power of high-end data analytics in driving business continuity is no longer hidden. IT teams depend on high-end data analytics to gain visibility into their IT infrastructure and services. With rich observability, IT teams can make better business decisions that boost service availability. However, the challenge is to decide what kind of data analytics should be performed and which tools should be used. If you only focus on predictive analytics models and ignore prescriptive analytics, you might not get the right result. There needs to be a perfect blend of different data analytics techniques to achieve better results. Let us see how prescriptive analytics helps in achieving ITOps excellence.
Tiers of data processing
In terms of business organizations, there are three major types of data analytics. The basic type of data analytics is descriptive analytics which focuses on collecting user and organizational data. Descriptive analytics only tell us about what has already happened within the organization. Once descriptive analytics is completed, IT teams move towards predictive analytics models. With the help of historical data, predictive analytics models make predictions about the future.
When both predictive and descriptive analytics are performed, prescriptive analytics begins. Prescriptive analytics is the final step in business analytics and talks about what should happen within the organization. Let us know more about prescriptive analytics for ITOps.
Understanding prescriptive analytics
Prescriptive analytics starts with working on modelling structures to determine business outcomes. The business outcomes are those that satisfy the management, stakeholders, and owners. Once the preferred business outcome is determined, prescriptive analytics finds the best practices to achieve that outcome. Prescriptive analytics uses AI and ML algorithms to determine the best course of action to achieve a favourable outcome. In short, prescriptive analytics focuses on what should be done to achieve business outcomes.
With the results of prescriptive analytics, you can streamline your IT activities to achieve ITOps (IT Operations) excellence. Once prescriptive analytics is completed, you can shape your IT operations to achieve favourable goals. Prescriptive analytics has helped IT teams optimize business activities according to the business goals. Many business organizations have started using AI tools in IT operations management in recent years. You can also remove data silos in your organization and determine the best course of action via prescriptive analytics.
Why should businesses invest in prescriptive analytics?
One may think that what is the need for prescriptive analytics when predictive analytics models are already telling us about the future? Well, you need prescriptive analytics to achieve the favourable outcomes shown by predictive analytics models.
How prescriptive analytics helps in achieving ITOps excellence is as follows:
- Prescriptive analytics tools pull the infrastructural data together and set the roadmap for IT operations. With the help of AI data analytics monitoring tools, prescriptive analytics can tell you about the steps required to avoid failure or achieve ITOps excellence. If any IT operation, service, or microservice is being performed for the first time, prescriptive analytics will help you in doing it right.
- With prescriptive analytics results, you can take real-time decisions to achieve a quick outcome. You can also shape your IT operations to achieve a long-term result with prescriptive analytics. Positive business outcomes that seem far-fetched can be achieved via prescriptive analytics.
- With prescriptive analytics, IT teams and data analysts spend less time on problems concerning IT operations. Instead, IT teams think about the possible solutions with prescriptive analytics results. Also, AI data analytics monitoring tools curate the data easily, thus saving the time of IT teams.
- ML/AI data analytics monitoring tools are typically used to perform prescriptive analytics. With advanced algorithms, the chances of human error are reduced during prescriptive analytics. Your data analysts do not have to spend hours aggregating and analysing data.
Challenges with prescriptive analytics
If prescriptive analytics is beneficial, why is not every business organization performing it? Well, business organizations do not have an answer to the challenges posed by modern-day IT infrastructures. Gone are the days when business organizations had a small amount of data to perform analytics. At present, the amount of infrastructural data is massive and is increasing day by day. Traditional prescriptive analytics tools are incapable of dealing with such massive amount of data. It is why business organizations ignore prescriptive analytics and end up deteriorating their service availability. Not to forget, the IT infrastructure is not as straightforward as before. Even the best data analysts are hurried by the complexities of modern-day IT infrastructures.
You cannot use legacy data analytics tools to handle dynamic infrastructural data. Another reason is that business organizations must hire many data analysts if they want to focus on all the infrastructural data produced. No business organization has unlimited funds to keep hiring data analysts as the magnitude of data increases. Considering the current scenario, ML/AI data analytics monitoring tools are the only solutions for dealing with operational challenges.
How to leverage the power of AI/ML for prescriptive analytics?
Traditional data analytics tools will hold no value in the current scenario of the dynamic IT landscape. Recent developments in AIOps (Artificial Intelligence for IT Operations) have proved to be viable solutions for prescriptive analytics. Prescriptive analytics with an AIOps based analytics platform will provide results in real-time. AIOps-based data analytics platforms will help in aggregating the entire infrastructural data in one place. Data aggregation for prescriptive analytics can be done without manual support with AIOps.
AIOps based analytics platforms will handle the complexities among data sets easily. You can streamline IT operations to achieve a single goal with AIOps. Also, you can control your IT operations and microservices from a single point of control with AIOps. Any amount of data can be easily analysed via AIOps based analytics platforms.
In a nutshell
You don’t have to hire many data analysts to perform prescriptive analytics. With AIOps, you can reduce the burden on data analysts and provide them with real-time insights. Adopt prescriptive analytics to achieve ITOps excellence!