Here is what you need to know
Your current discovery tool is not up to the task – period. Discovery tools are very good at performing the task for which they were designed – discovery. They are designed to look into a defined environment to identify, inventory and classify known types of objects and their direct connections and dependencies. What they don’t do is combine the data about the discovered objects with all of the other data about your environment to give you the big-picture perspective needed to enable effective decision making. This is a core requirement for nearly every business with which we’ve recently spoken, and the existing discovery tools in use are consistently insufficient for the task.
Your Discovery Tool is part of the problem
Discovery tools are contributing to your big-data problem by making data collection easier and faster, but failing to help you interpret that data and convert it into actionable information insights. Here are 5 ways your current discovery tools are giving you what you think you need, but are actually incapable of fulfilling your true needs.
- Discovery tools can indicate what is present, but not how it is used. A full appreciation of your IT environment requires an understanding of both its content (the objects within the environment) and context (the activities taking place). Discovery tools do a good job of capturing the content, within specific parameters (that is, discovery tools are often optimized to discover specific classes of CIs). Contextualization, however, often involves correlation of multiple discovery systems and integration with, for example, known business processes, job functions, etc. This operational context is critical to convert data into actionable insights.
- Discovery tools can’t indicate what was intended, only what is operating. Most IT environments were not created based upon a “grand-design,” but are the result of an evolutionary process over a number of years. Discovery tools can provide valuable insight into the objects that are operating in the environment today, but they are incapable of capturing those objects that may have been present during the past and the impact their legacy is having on the present. Fragmented implementations, historical technology limitations and design decisions are all likely responsible for why your environment is the way it is today. Historical perspective and intent cannot be captured by only looking at the present environment, and yet is critical as a basis for informed decisions about the future.
- Discovery tools can’t indicate what is missing, only what is present. Because discovery tools provide a single point of view of the operating environment, they can’t capture objects that are expected to be present in the environment, but for whatever reason are no longer present. Absence of data creates a gap that discovery tools are incapable of processing, which means the completeness of data is now at constant risk. Reconciling overlapping data sets from multiple sources/points of view is required to answer the completeness question.
- A single discovery tool won’t capture your entire environment; you will need many. Discovery tools are designed to look for a discrete set of objects of known characteristics, which means discovering tools are adept at finding and inventorying those specific classes of objects. With the diversity of modern IT ecosystems, it is practically impossible for a single discovery tool to understand and process all of the object types that are present. Some discovery tools are very good at capturing physical objects, while others capture software, etc. Discovery tools should be treated as specialists and the discovery process as a team sport – leveraging the capabilities of multiple players.
- Discovery tools don’t react to unknown objects very well. They are great for automating the discovery of things that are well known and easily identifiable, but nuanced variation among objects and the capture of new object types may require manual activities and/or additional tooling. At their core, discovery tools are rules-based systems. As the definitions of the rules improve, discovery tools will have the capability to capture and classify a greater percentage of the objects in the environment, but there will always be exceptions. These exceptions are often the most meaningful for providing environmental insights.
An important part of modern IT management
Discovery tools are important in the modern management of IT environments; however, independently they are clearly incapable of satisfying the overall need of most organizations. To gain the most value from discovery tool investments, companies must look at how they use multiple discovery tools together to provide a broad and holistic perspective on the environment.
As the data from discovery tools is integrated with known data from existing sources, integration, reconciliation of conflicts, addressing gaps and putting data into the correct context is critical. Blazent is an industry leader in providing the solutions needed to gather and focus on your discovery data and resolve the quality issues that are restraining you from achieving your goals of information insights and data-driven decision making.