{"id":3131,"date":"2023-10-04T10:50:49","date_gmt":"2023-10-04T17:50:49","guid":{"rendered":"https:\/\/44.203.207.232\/?p=3131"},"modified":"2023-10-04T10:50:50","modified_gmt":"2023-10-04T17:50:50","slug":"top-8-uses-of-configuration-data-that-youre-missing-out","status":"publish","type":"post","link":"https:\/\/webdev.siff.io\/top-8-uses-of-configuration-data-that-youre-missing-out\/","title":{"rendered":"Top 8 Uses of Configuration Data that You\u2019re Missing Out"},"content":{"rendered":"\n

According to Gartner Insights, a staggering 80% of all incidents stem from planned and unplanned configuration changes. <\/p>\n\n\n\n

\u201cThrough 2015, 80% of outages impacting mission-critical services will be caused by people and process issues, and more than 50% of those outages will be caused by change\/configuration\/release integration and hand-off issues.\u201d<\/p>\n\n\n\n

– Gartner RAS Core Research Note, Ronni J. Colville, George Spafford<\/em><\/p>\n\n\n\n

Despite that, most approaches to troubleshooting and handling incidents are reliant on alerts and performance monitoring rather than asking quite literally, any recent changes in configuration.<\/p>\n\n\n\n

In other words, no matter what, the majority of outages almost always have something to do with configuration changes. But that fact is often ignored, understandably due to a lack of actionable configuration data.<\/p>\n\n\n\n

Enter SIFF,<\/a> a network configuration management system that manages rapid changes in DevOps, network, and IT operations offering visibility into IT environment changes by providing an audit trail for all configuration changes. The SIFF platform scrutinizes data from a diverse range of sources and then distinguishes between planned and unplanned configuration changes, aiding in the isolation and identification of complex incident root cases.<\/p>\n\n\n\n

Below, we outline the best uses of configuration data and why harnessing the power of SIFF will help minimize outages, accelerate troubleshooting faster, and automate policy compliance.<\/p>\n\n\n\n

1. Utilize Configuration Data for Faster L2\/L3 Support Resolution<\/h2>\n\n\n\n

In most scenarios, L2 \/ L3 support is confronted with outages or disruptions and struggles with limited visibility into the configuration changes that have transpired across their network infrastructure. They\u2019re forced to diagnose outages without the ability to access valuable information siloed within the various departments.<\/p>\n\n\n\n

Think about it another way: Imagine a doctor attempting to diagnose a patient without permission to ask questions about what has recently changed for the patient \u2013 their living conditions, what they\u2019ve been eating, and other pertinent questions. There\u2019s no structure or logical decision-making to narrow the scope of the problem.<\/p>\n\n\n\n

By understanding that the majority of outages are attributed to configuration changes, it\u2019s quite obvious that change data can help support teams identify the problem. Still, the information you need is most likely fragmented across various tools and silos within the organization.\u00a0<\/p>\n\n\n\n

Even if support teams start asking questions about recent configuration data, it\u2019s still a bit of a needle-in-a-haystack approach. It requires bridge calls across departments and teams \u2013 increasing resource waste and taking up valuable time to inevitably find resolution.<\/p>\n\n\n\n

SIFF does this by collecting all configuration changes<\/a> and making the data easily accessible through a change activity stream where users can correlate service-impacting changes <\/strong>with actual change details and DIFF comparisons. The synergy between real-time visibility and configuration data in a solution like SIFF contributes to faster resolution of Level 2 and 3 support in a few ways:<\/p>\n\n\n\n