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Beyond IT Operations: Using Predictive Analytics to Deliver Better Business Value

In this special guest feature, Pritika Ramani, Product Analyst at ManageEngine, provides four areas where enterprises can leverage predictive analytics to broaden the focus of IT operations analytics (ITOA) from IT operations so that it delivers value for business as well. Pritika is a product analyst at ManageEngine, the real-time IT management company and division of Zoho Corporation, where she applies both her technical and brand marketing expertise to IT management. She is passionate about trends in analytics and business intelligence and is currently on the Analytics Plus team. Pritika enjoys reading, blogging and cooking when she is not working on expanding analytical insights into other IT management arenas.

IT operations analytics (ITOA) involves constantly monitoring IT systems in order to collect, analyze and interpret data from various IT operational sources. Organizations use ITOA to predict potential system issues, reduce response times and enable teams to make better decisions. However, traditional ITOA systems have limited visibility; their focus is more on analyzing an enterprise’s operations and less on its business strategy.

Is there a way to broaden ITOA’s focus from IT operations so that it delivers value for business as well? Predictive analytics is a good place to start.

Predictive Analytics and What It Means for Business

Predictive analytics is the method of analyzing relationships between multiple data points to accurately predict future application behavioral trends and data anomalies that might affect end-user experience. It identifies growth opportunities and risks, which is of critical value to business and IT stakeholders.

Here are some ways enterprises can leverage predictive analytics to their advantage:

1. Prevent rather than react

Using predictive analytics, businesses can move from being reactive to being preventive with their data. Instead of reacting to events, organizations can use predictive analytics to prevent unforeseen service outages from having a critical impact on their business. Predictive analytics uses adaptive algorithms to analyze existing historical data to observe past and current behavior from applications and networks and to discover any potential problems before they develop.

Using predefined key performance indicators (KPIs), the algorithm measures the observed values against the normal standards. If there is any deviation between the values, a notification is immediately sent out to the IT admin, warning them of a potential issue. By allowing IT users to predict such issues, enterprises can take stock of those issues before they impact customers.

2. Improve customer experience

By applying predictive techniques in its sales and marketing processes, an enterprise can improve customer experience by leaps and bounds.

  • Marketing: Predictive analytics can analyze emails and social media feeds, illuminate areas related to customer satisfaction and depict ways to engage customers better. By understanding the needs of prospects and customers alike, enterprises gain a competitive edge. They can choose better techniques to promote products and services that will win them more customers.
  • Sales: Predictive analytics enables sales admins to identify leads, allowing them to focus on potential leads that have maximum propensity to buy and filter out those who may drop out.

3. Manage your resources for better business impact

In a bid to optimize cost and ensure high quality of service, predictive analytics can be used to accurately track resources that are close to capacity or need to be restocked. Understanding the future requirements for resources, like storage, allows teams to make informed investments at the right time. This ability is critical, as it allows businesses to scale their infrastructure in accordance with their user growth.

4. Protect your enterprise

Detecting a security attack and estimating data loss from that attack can be challenging. Data attacks happen rather quickly and are widely distributed across networks, applications and servers. By collating event data from multiple endpoints, predictive analytics searches for any possible vulnerability in the system to determine the probability of such attacks.

The Future

Whether they need to visualize diverse types of data, add more customizations or develop accurate prediction models, most enterprises have needs that will drive them to adopt predictive analytics. Predictive analytics will likely become a mandatory requirement for every organization and can align with any enterprise’s strategy. Although predictive analytics is expensive today, the investment makes sense for companies willing to pay top dollar for competitive advantage. Going forward, technology advances will surely drive costs down, making the power of predictive analytics available to all.

 

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