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The TidalScale Blog

    In-memory analytics keeps high-risk industries from going up in smoke

    Authored by: Gary Smerdon



    As of Jan. 1, it’s now legal to purchase marijuana from licensed dispensaries in Illinois. The same goes for newly approved cannabis cafes in Colorado. And Hawaii is now saying “Aloha” to marijuana by decriminalizing the substance.

    As a fresh wave of 420-friendly legislation washes over the land, more state regulatory agencies—and the banks and payment companies that serve legal cannabis businesses—are having to find ways to ensure that previously illegal enterprises are now operating in full compliance with the law.  And that’s where NCS Analytics comes in.

    Identifying suspicious behaviors using big data

    NCS Analytics is a leader in providing dynamic, comprehensive, data-driven solutions for governments and financial institutions as they interact with traditionally high-risk industries, such as cannabis and other highly regulated businesses. 

    “When you have an industry that is highly taxed or highly regulated, you’ll have someone who is incentivized to cheat the system,” notes Adam Crabtree, CEO of Denver-based NCS Analytics. 

    The NCS team and their patent-pending analytics utilize a multitude of data sources to provide holistic insights to clients about the markets in which they work. Detailed alerts inform NCS customers about suspicious behaviors of various market participants. 

    For example, an alert may pinpoint a cannabis grow operation whose reported yields don’t match standard expectations for the variety they’re growing – something a regulator may want to follow up on. Through the NCS Platform, customers can see the specifics around each alert, along with questions intended to help facilitate meaningful conversations with licensees.

    “We help ensure that these business communities are able to sustain their operations legally and that financial institutions can serve them with confidence,” says Crabtree.

    Bogged down with data

    The NCS Analytics team creates complex models that ingest data from an array of sources including socioeconomic, bank, tax, utility, licensing, seed-to-sale tracking and point of sale data. Due to the limited abilities of traditional computing systems, the  models previously became so large that some took 30 days to fully process. 

    From 30 days to 3 hours

    All this changed when the company implemented Software-Defined Server solutions from TidalScale. TidalScale software combines the resources of multiple commodity servers into a single system to provide as much memory, cores and I/O as organizations need to handle even their largest workloads.

    With a Software-Defined Server, what once took 30 days now takes just three hours.

    “TidalScale gives us a competitive advantage,” says Crabtree. “Because we can run our predictive analytics engine entirely in memory, other companies just can’t compete with our analytics.”

    Faster processing is just one of the benefits NCS realized after implementing TidalScale’s Software-Defined Server solutions. Read the full case study to learn how NCS used TidalScale to deliver even more insights, lower costs, and further hone their competitive edge.





    Topics: virtualization, software-defined server, in-memory computing, data growth, R & Python on Ubuntu Linux, in-memory analytics, data analytics