Getting Actionable Insights from Your Data

Getting Actionable Insights from Your Databy Sue Dunnell.

The days when IT operations only existed to support the back office are over. Today IT is a driver for meeting organizational strategy, revenue and customer satisfaction goals.

New technology certainly plays a role in IT’s increasing importance. Apps can be delivered more quickly with the new tools and processes for DevOps implementation.

Organizations can rapidly scale to meet demand with cloud deployments, automation has accelerated productivity  and more personalized and engaging customer experiences can be built by leveraging AI.

But new tools, automation, and other technologies are only one aspect of the force behind the expanding role of IT.

The key determinant of success is data.

IT began using data to drive efficiency by automating processes and the business insights realized were valuable.  This led to more automation and monitoring of process, giving IT the ability to better predict and prevent outages or failures – which led to more data.

More data can often lead to more noise.

As IT environments have become more complex – spanning hosting sites and technology stacks – new tools emerged to track user behavior, monitor multiple sites, and integrate new and legacy systems. In addition to a lot more data, there is a lot more noise, too.

Complexity coupled with a lack of insight and context brings risk. As the amount of data captured across IT continues to increase exponentially, it is becoming more difficult to manage and understand. Relationships are hard to identify and explore and the business contexts are not captured in traditional IT tools.

Finding insight from your data

The good news is that IT can leverage existing tools and get more value out of them. At TDS, we built a platform, TransitionManager, designed to pull in the data from your current tools, visualize relationships of all IT assets across hosting sites, quickly identify business facts such as RTOs, RTOs or compliance requirements, perform queries, and model changes to your environment.

For example, a large healthcare organization got stuck while trying to consolidate a data center and adopt cloud hosting. Their autodiscovery tool showed one application had over 4,200 dependencies – too many to manage and unravel. When the data was pulled into TransitionManager, where it could be filtered, viewed in context, and only application level dependencies were considered, only two connections were found critical to making the move.

Check out TransitionManager today. Sign up for a demo to see how we can help you sift through the noise and bring meaning to your data.

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