@dorkninja Hilariously data is the thing that allows you to replace everything else. Or fix it. Or learn from it. Or ... #AllTheThings. Data is the truth/life/love. #BringMeData#AndBacon
@RalphFinos you have to know how to use and what you are doing. data itself is kind of useless unless you know what you are doing and can use the right tools
@plburris Agree Peter, regulatory compliance rules (GDPR et al) will start to rain this problem in I think. It will be interesting to see if it has an affect on the use of data in the Data Lake as this is the usual place the data is just thrown
@RalphFinos Agree. But there are also cases where data that's useless today, and tomorrow, and the next day - becomes invaluable 10 years from now. See: The CDC.
@dorkninja This is interesting, and 100% true. Data's value changes over time depending on the use case. Too few take this into consideration, let's hope that changes :)
@dorkninja I'd argue that's not data that's useless today or tomorrow. It's just a single data point that is useful in a trend, rather than on its own. Also something we can catalog, capture and covet.
@NeilRaden From an ops perspective - as an ex-Ops person myself, a very simple value model I use is (velocity / friction ) * the number of users that can put the data into action. Not academically rigorous but folks find it useful.
@mcauth - There should be some conformity, or canonical models, in industry verticals. There may be different valuation models across departments. Unless there is a market for data, all data valuation models will be subjective
@dorkninja Data often indicates a state, status, or condition of the infrastructure at a point in time, or it can represent a trend over time. For infrastructure, all s essential for historical analysis, real-time monitoring, and preventive.
most applications today are vomiting data - state, status, condition in a vacuum. not actionable - need AI to synthesize and deliver accelerated insight
@Jshoc Largely true, especially given sheer volume. But it's also possible to - from a practice level - extract the stuff that you know matters and present it proactively, pre-AI.
Question #2 is? (1) Do you aggregate data from all your ITOps/DevOps, etc. tools into some data aggregation platform? (2) Is your data still stuck in some monitoring tool? (3) Are you beyond (1) and run some algorithms against your data?
I feel super lucky that our IT team here is definitely a facilitator. In personal experience that is the exception to the rule, unfortunately. "Old" IT hats focus on ops, escalations and security. Data mining not pri 1.
How about some #machinelearning questions? It would be great to know who you all represent... Respond with: (1) Data scientist/Machine Learning Engineer (2) Compsi proessor (3) IT Admin (4) Directory/VP/C-level