* Analytics and the configuration management database
The notion of a configuration management database (CMDB) is becoming a core area of interest for the industry as it represents the coming together of two key requirements: the architectural integration of various management technologies, and the need to invest in process awareness to improve IT efficiencies and relevance. But while I’ve been spending a lot of time (and expect to be spending more) learning about and giving guidance to CMDB deployments, I would like to call out the underlying importance of analytics.
I am using the term “analytics” loosely – albeit focused in this market – to mean any “set of functions or algorithms from which data processing and analysis can be constructed to achieve desired IT management applications or processes.” Basically, anything that can move beyond raw data to invest it with relevance and support for decision making and/or automated actions.
A reminder about the importance of analytics helps to also clarify the role of the CMDB. The IT Information Library’s CMDB is ideally a trusted, multi-dimensional and current view of inventory, configuration, topological, service, organizational, business and policy-related information. It is a Holy Grail that will tax existing technology and not-yet-existing standards for at least the next decade. And yet, in itself, by itself, for itself – so to speak – it’s valueless. The CMDB is a grand enabler for consistent management processes on the one hand, and efficient sharing of information across multiple and multi-vendor management applications on the other. But without any action taken – whether for monitoring or active change management – the CMDB would remain a big, complex and probably federated lump of data.
What is the relationship between the CMDB and analytics? Well, actually it’s a complex one and one that I suspect will be an evolving area of dialog and innovation for the next decade (and beyond) as well. On the one hand, certain types of analytics are involved with capturing the relationships among and across infrastructure components and their relevance to services can help to populate the CMDB. But even more broadly – the CMDB system can be viewed as an enabler for a whole layer of cooperative analytic engines designed to support every task from service assurance, to infrastructure optimization, to accounting and chargeback and dynamic asset planning, to service- level management planning and business service management, to life-cycle application planning and management, to life-cycle information management, to security management – just to name a few of the broader examples.
What then, does the analytic landscape (as EMA defines it at least) look like today? It’s a complex and varied landscape indeed, with everything from policy-based event filtering, to correlative algorithms, to “if-then” analytic capabilities, with heuristic roots in areas as far a field as data mining, chaos theory, fuzzy logic, neural networks, and case-based reasoning – just to name a few of the more obvious examples. Sorting through this chaos may require just a little “chaos theory” in itself – and I welcome your thoughts about what technologies you are aware of, trust and use today – and what remain merely pretty visions in the sky.
