Fuzzy Logix: Why Big Data Is Like A Pint Of Guinness


Adrian Bridgwater

July 20, 2016

The trouble with getting a good pint of Guinness wherever you go around the world is that it just doesn’t travel well. The famous black brew from Ireland is well known for its aversion to transportation. Scientists have even carried out studies to deduce whether a pint of the black stuff really does taste better in Ireland.

North Carolina headquartered software intelligence company Fuzzy Logix feels somewhat the same about big data analytics i.e. it’s better if you do it ‘in situ’, where it happens, inside the database.

Not the only vendor championing so-called in-database analytics (Oracle does it — and IBM has its Netezza Analytics embedded platform, but it uses Fuzzy Logix as its Intel Inside to do it along with tech from Zementis, Revolution Analytics and SAS), Fuzzy Logix is probably shouting about this approach louder than some just now.

Indeed, late last year the firm raised $5.5 million in series A funding from New Science Ventures to support business growth. Life is sweet in Fuzzy Logix big data land, or so it seems.

What is in-database analytics anyway?

So what is in-database analytics and does Fuzzy Logix actually do something different in this crowded marketplace?

The firm’s flagship product is called DB Lytix (Data Base anaLYTICS, get it?) and it is explained as an analytics engine that will sit inside a database to perform analytics on location, as it were. Fuzzy Logix claims that this proximity eliminates the (no doubt time consuming and expensive) need for data extraction, middle-tier analytics servers and redundant data storage.

“Data is too large and results are needed quickly – businesses can no longer afford to move data to separate analytics servers. Plus… big data analytics problems are growing exponentially in scale and complexity. Organizations, for example, are looking at how analytics impact daily weather predictions on the inventory needed in thousands of retail locations. How to cost-effectively prevent breakdowns on a worldwide fleet of trains based on input from hundreds of sensors on each train. Or how a health insurer can determine which of its millions of customers would best be served by a new class of medications. Fuzzy Logix ensures that analytics are performed where the data resides,” said the company, in a press statement.

But, we asked, is Fuzzy Logix actually doing something different in this crowded marketplace? Click onwards to find out…

Tiny big data super heroes

The answer is yes, there is arguably special work going on here… but it’stechnically complex. So let’s explain it in business language.

The firm has released in-database analytics library consisting of more than 700 ‘analytical methods’ that work inside other database platforms. These ‘methods’ are best described as smaller software component processes for big data analytics. Think of them as tiny analytics superheroes with special skills, if you will. These methods can be sent on ‘missions’ to execute functions inside other database platforms, which in this case include Teradata, Actian Matrix, SAP IQ (formerly Sybase IQ), MySQL, SQL Server, Aster Data and… interestingly, the aforementioned Netezza.

Company CEO Partha Sen says that having a common set of Fuzzy Logix DB Lytix functions available on Teradata Aster Analytics significantly helps in building and optimizing advanced analytic models using DB Lytix and Aster functions before operationalizing them in the data warehouse.

“The end result is that data scientists and analysts are offered a holistic suite of analytic capabilities to select, test and operationalize multiple analytic techniques that are ‘best fit’ predictive models. In real-world deployments, DB Lytix, a software only solution, has proven to be 10 to 100 times faster than conventional methods and highly cost effective because there is no need for additional hardware installations,” said Sen.

A further benefit of the DB Lytix approach is that all advanced analytics are executed on the entire database rather than on a limited sample, which, in theory, results in more accurate models in certain cases

Companies can use this kind of approach to in-database big data analytics where the software applications used by the staff base need intensive and/or high performance processing. Examples might include fraud detection, credit scoring and pattern recognition… Fuzzy Logix claims its technology has been used in banking, insurance, retailing, manufacturing, marketing services and healthcare

The pathway to the pervasively predictive enterprise

The questions we are trying to draw out of this are a) does in-database analytics help us work with big data in new ways and does it change the way firms operate and b) does Guinness taste better in Ireland?

a) Yes in some (but not all) cases in-database analytics will allow more controlled management of the procedures and actions that govern any given firm’s life as it seeks to become what we can call a ‘pervasively predictive enterprise’ that knows what is about to happen next.

b) Yes it does… and if you ever get to drink one in the bar at the actual St James’ Gate brewery you’ll feel like you’ve died and gone to stouty heaven.


This article was written by Adrian Bridgwater from Forbes and was legally licensed through the NewsCred publisher network.

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