Collecting vital business metrics from every possible source
Businesses of all size, from young, fast-moving start-ups to massive enterprises rely on the data they collect, store and process for driving their activity forward based on valuable knowledge.
Data Lakes are utilized to collect massive amounts of seemingly unrelated data, across multiple channels and vectors, only to analyze at a later time.
Data Lakes, while requiring very little complexity to implement at the start, can potentially become very expensive to operate and highly redundant if not architected with efficiency in mind.
Our team has experience with many Data Lake systems, ikigai.net can analyze your business data collection, storage and tagging practices, in order to optimize the process in the long run.
In our experience, the vast amount of data stored in customer-created Data Lakes which do not employ ingestion-level tagging or de-duplication suffer from a high amount of redundancy.
While redundancy can be challenging in many ways, it can also render the entirety of the data collection efforts as unsustainble. At ikigai.net, our process always involves creating clear data Storage, Retention and Analysis Plan to reduce the amount of unnecessary data collected and retained.
Once collected in Data Lakes, highly valuable company data is unusable unless processed, as soon as possible. Certain types of businesses, especially ones responding to real-time events, cannot afford to have stale data fed to current activity streams.
Each company has it’s own processing, storage and analysis requirements and this is where an experienced team can assist your business make more sense of the valuable data you already own.
Within fast-moving businesses, such as Events, eCommerce or Content Exploration, having access to Real-Time Analysis can become an indispensable business advantage.
Such kind of analysis requires a dedicated network, capable of ingesting event streams and generating real-time responses at a very high speed. ikigai.net can assist with the architecture level requirements of a real-time analytics platform.
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