We are all familiar with predictive models for weather forecasting. So why can’t we do the same for SAP systems?
Database and resource utilization are different in every application and are affected by the frequency and amount of saved data and the way it is being used. A database that holds production data - producing millions of records and logging information every minute will grow in a different rate and will require a different amount of resources than a database that holds employees information and monthly paycheck processing, running monthly reports.
Based on the server size, memory, CPU utilization and used storage growth rate, CIOs, IT directors and Basis managers need to be able to plan their budget and work plans. A budget needs to include any funds for adding new resources (new servers, cloud space, VMs, hard drives or memory). Work needs to planned to allocate the relevant people at the right time ensuring that engineers are expanding the system before the systems get to be a potentially catastrophic situation of insufficient resources.
But before we dive into SAP predictive analysis. Let's start with the basic:
What is predictive analysis?
Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Predictive analysis uses historical data to predict future events. A predictive model is built using mathematical models that capture historical trends and patterns. That predictive model is then used on current data to predict future trends and to suggest timely actions to take for optimal outcomes
Predictive analysis is often discussed in the context of big data. SAP business information might include transactional production data, sales results, human resource data, financial information, customer complaints, and marketing information. Companies need to find ways to ensure that their team is focusing on the most important infrastructure in which a failure will have the most impactful effect and prevent database resource issues before they occur.
Smaller IT and Basis teams
With pressure on costs, IT and Basis teams are often being forced to do more with fewer people. Data-driven predictive models can help those teams solve long-standing problems in new ways, saving them and the company time and money. Better planning allows companies to maintain their infrastructure in levels that ensure they will not run out of resources.
It goes without saying failures can be very expensive. A recent Gartner report mentioned that unplanned IT system downtime costs organizations: $5,600 per minute, $300,000 per hour. IDC, on the other hand, estimates that system outages can cost a company between $500,000 to $1 million or more per hour. Monitoring SAP system resources can prevent or fix failures before they spiral to a full production outage.
When databases or servers are getting low on resources, weird things can happen which are hard to troubleshoot, including slow batch jobs, incomplete transactions, partial reports and many other problems. Even worse, if it happens during off-hours, it might mean getting people to work in the middle of the night paying for overtime, making it an even more expensive issue.
No one wants to see a level 17 error message: Insufficient Resources so how do you prevent it from happening?
SAP systems are complex and include a huge amount of elements, getting visibility to the complete landscape is hard to reach. Properly planning for the future and prioritizing different tasks over others is not a simple task.
Using Syslink Xandria, teams can get complete visibility to the SAP landscape as each agent automatically identifies the type of elements that reside on each server. Getting this information using any other tool will require a lot of manual work.
The system then provides a detailed view of each server and its expected future growth in one pane-of-glass including:
It allows you to drill into the specific server and see the expected growth and past behavior over different periods of time.
Similar elements may behave in a different way in different scenarios. Your team needs to know how to prioritize and allocate their tasks based on timing of expected growth and expected impact. If you knew the expected time that the storage would fill on each server in your SAP landscape, you could prioritize and remedy the situation before it impacts your business.
During the past holiday season, one Xandria customer in the retail industry saw faster than expected growth in their database due to increased sales and new functionality in their SAP environment. Waiting for a standard threshold to notify on this growth would not have provided ample time to correctly remedy the issue. By using Xandria’s predictive resource planning they were able to see these new growth trends, predict the max capacity date and mediate the issue well in advance. Correcting this issue before it was a problem prevented downtime during their busiest time of the year, which would have been detrimental to their business.
Syslink Xandria's predictive analysis provides indications of the expected growth of various SAP and infrastructure resources. With your new knowledge of predictive resource growth, you can make informed budget and resource planning decisions and prevent failures.
Learn more about Syslink Xandria's predictive analytics by watching this 15-minutes demo