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Yes, it's a bold statement.
But as a matter of fact, doing things the "Yamanu-way" your data-adaptation might be the last transformation you need. No exceptions apply, let me explain:
Data analytics at its core tries to identify contextual relationships; locally and across various data sources and silos.
Reason for that is to make sense of "it", namely information, allowing human operators (users and agents) to infer new information and perhaps act on them.
As a result consider the following requirements:
data warehousing
data analysts
corporate data manager
dashboard designer
internal or external business consultants,
All geared towards "fishing for", interpreting and translating results and making recommendations.
After considerable amount of effort, even more work is required:
Developers and Engineers to optimize corporate information infrastructure
Business Developers and Mangers to re-align procedural behaviour
Team Leaders to enforce policies - and check for execution
Simple Example: Correct handling of Excel and Word documents to make Information accessible
The trouble with this is timing, computational power, lack of information access and a few more, as well as data residing in purpose-designed databases and local documents.
Sound familiar?
Thought so!
-- DRAFT --
Worst case: daily business.
There is only one:
Consider looking at everyting at once, from any given and new angle.
Why? (nearly always the same) the reasons:
- to stay ahead of the competition
- identify defects and declines as early as possible
- identify improvement/opportunities as quickly as possible
in general:
"to make the best possible choices at any given time"
The simple IoT Demo already shows the power of "instant access", for a lack of a better term.
Any attribute can be treated as Dimension. More importantly, they can be filtered, displayed, combined and even used for logical operations, natively and deliver connected information to the user proactively.
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