guide to cloud data
If this is the age of information, which it is, then surely we can reasonably suggest that all businesses need to get more closely acquainted with the data streams traversing their operational models. Getting better acquainted with information in real terms means understanding data at a more granular level. But don’t panic, this does not require a degree in computer science or a PhD in systems analytics.
Business managers, salespeople and every company stakeholder through to administration staff can and should now develop a rudimentary working understanding of what size, shape and flavour their firm’s data exists in. We all need to travel this learning curve because cloud computing services enjoy increasingly widespread acceptance across all transepts of business.
As cloud data is processed, analysed and ultimately stored back in the cloud datacentre, it has become a more fundamental and integral part of business. Technology analysts suggest that firms in all verticals will soon start to qualify and quantify the data they hold as an item on the balance sheet. Knowing what type of data you own has suddenly become very important.
As we start to understand the difference between distinct chunks of data, we can begin to understand how to treat each piece or chunk of cloud differently. This has huge implications for how we approach the security, privacy and identity management of every piece of cloud data we create.
The challenge we are faced with is one of serving the right data dish to the right table at the right time
As a starter menu guide, structured data belongs to an application and has a specific value, size and meaning. Unstructured or semi-structured data also often belongs to an application, but it could take the form of sound, video or some other more difficult to quantify and quality block of information.
Deeper down we find time series data, which in simple terms is just information with a note of when it was created. Spatial or geospatial data usually refers to descriptions of physical objects. Orphan data, you guessed it, doesn’t have a home application or wider data set family that it belongs to. Then there’s meta data, which is higher level data dedicated to providing information about other pieces of information.
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