@RalphFinos The data is pretty good - the problem is in understanding what the data means in terms of actual operations and whether it is useful for any analytic-driven operations purpose
How much "YES" can I add here? Even as a relatively new tech, in the grand scheme, the immediate effects are huge. Changes the entire paradigm of how we approach issues/monitoring/data/device management. Massive value here and more to come
This is where 2016 made things interesting...when machine learning made it to IT Ops in a broad way. We're just now starting to see the potential / implications IMO.
We're looking at this question closely: ITOM may be one of the "killer apps" for machine learning. Nobody wants to invest in IT, but avoiding it puts any digital business at risk.
@plburris Yep. From my perspective, this will become another vector for competitive advantage and much of IT will either figure this out or struggle to catch up.
@mcauth "Struggle to catch up" is putting it nicely. IMO they'll either figure it out, or become obsolete. It's that big of a differentiator. Not many things are "must have" items. This truly can/will be with data rates increasing.
@yaronhaviv Immediate business justification: GRS as data use and portfolio complexity explodes. Slightly longer: binary ability to run the digital business.
Sometimes overlooked in the Data Lake conversation - Archive Data or Legacy and Retired Systems of record - Data is still very useful especially for long term trend - but how to place it in the Data Lake and how to control it properly
@mcauth Data retention is a key issue, especially for the scads of infastructure data too low-level (or not relevant to compliance) enough to merit long-term retention. There's just so much log data that has marginal value for retaining.
Look, the proper place to USE that data is still a data warehouse, and they have overcome many of their drawbacks. I say, cloud DW, not data lake if you think it will be used.
@NeilRaden but surely Clolud DW, Data Lake, EDW are all part of the Enterprise Data Fabric - should we not worry where the data is held as long as it can be used across the Enterprise?
In other words, how much friction is there for people trying to find the data and analytics: Do they need to know a query language? Can they easily search? How long do they have to wait?
I'll parse this along the boundaries of data sources: Machine data (logs, snmp, etc.) Agent data, synthetic data, and wire data. Most are quite easy to get at, it's harder to wrangle it into value.
Varies *wildly* with tooling and instrumentation. For some I've spoken with it's hopeless in their current deployment. For others it's seconds to visibility and insight. It's all about surfacing insights rapidly, automatically.
@dorkninja Great point! But as IT resources become more core to business behavior, a more diverse user community will demand simple ways to find, collaborate on, and act on answers.
@yaronhaviv Agree. There is a "half-life" to data value and it differs based on the analytics use case. With incident response and downtime, the half-life of data value is shorter, but for long-term capacity planning the half-life is longer.