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Data slicing&dicing
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Data crunching
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Real-time Data Analysis
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Data modelling, definitions and refinement
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AI to data streaming – local and post-collection
We decided to launch a page about data analytics, where all our postings about the subject will be gathered together. For a business, and not only, if any criteria used for performance is not measurable and quantified it is not relevant.
If you want to include driver’s efficiency in wage formula then you have to clearly show how it is measured, be transparent about the process and outline the best practices.
If you want to measure the cost-per-customer of your website, you have to measure customer’s behavior, its business cost and to analyze ways on creating value, for you and him. Sometimes, different tools can be employed like Google Ads than simple performance metrics.
New job names for handling just data
Analytics is an important component for applications collecting data and analyzing it in real time, IoT is just a simple example. The deluge of data – where data is the competitive advantage – creates competitive advantages but also huge problems for storing it, querying or even creating reports. The sudden surge in customer’s requests or the crash of the websites under high traffic forced the industry to consider and implement solutions. Besides data modellers and data architects it even created a new job in the industry – DevOps. Ten years ago, that job fell on the shoulder of developers or network admins, right now it has its own tools and ways to confront big problems. It is not only about big data, but about everything related to high surge in resources – storage, memory, bandwidth, computational power:
- C10K – handle a large number of clients at the same time
- your mobile provider was down for 10 hours and your “store-and-forward” devices are just coming to dump onto your servers a deluge of data – I will call it Verizontrina
- have customers who are loving the “deals-of-the-day” and keep clicking on the refresh button like is no tomorrow
- bandwidth hungry apps which are not easily scalable – embedded video content
- automatically detect bottlenecks and scale the solution (in the cloud or not)
- manage computational solution in a scalable design (sometimes by re-coding the whole solution)
- third-party API bottlenecks
To be continued
We will try to show the role of analytics from a few perspectives :
- the start-up challenges in collecting data and analyze it – what is relevant or not
- options in handling data, SAAS companies offering products just for it
- our customer’s interest in analyzing data we collect for them – reports or reports correlated with free data
- our internal day-by-day collected data in monitoring our cloud services – use & abuse
- an example of our customer’s effort in trying to capture the behavior of their employees or analyzing asset utilization