Fairly obviously, we at Matillion are in the cloud Business Intelligence business. You want a cloud-based data warehouse? That’s us. You want cloud-based self-service reporting? That’s us. You want cloud-delivered dashboards and KPIs? That’s also us.
But some customers want more. They want data mining, advanced analytics, and high-end data visualisation tools. They’ve heard the buzz around the ‘R’ analytics language, seen the power of SAS, SPSS, or Qlikview, and would like to see if those sorts of capabilities would deliver value for their businesses, too.
In which case, they wonder, does an investment in Matillion cloud Business Intelligence serve to preclude cloud data mining?
The answer: absolutely not.
In fact, Matillion delivers Cloud data mining in three different ways.
Cloud data mining, direct.
Engage with Matillion for Business Intelligence and self-serve reporting, and we install on your enterprise system a small box about the size of a modem.
And in many ways, that’s what it is: specifically, the job of the on‑premise Matillion Data Accelerator (MDA) device is to facilitate the data transfer process between your enterprise system and the Cloud data warehouse that we build for you.
But your data mining tool of choice—SAS, SPSS or whatever—can also be directly connected to the MDA, and you can use it to access and mine the Cloud data warehouse.
And as such, all data mining options and data visualisation tools are open to you. If you want to do this once or twice, or every so often, we’ll open the connection as a favour. If it’s an everyday requirement, then there’s a small additional charge on your monthly subscription.
Cloud data mining, delivered.
But not every customer has the in-house capabilities to exploit data mining to its best effect.
That’s why Matillion also undertakes data mining for our customers, and why we have data scientist capabilities on our staff. Using tools such as ‘R’, our data mining specialists undertake analytics and data mining assignments either as one-off exercises, or as a recurring service.
One typical requirement: Affinity Analysis and Basket Analysis. In other words, carrying out Amazon-style ‘Customers who bought this are likely to want to buy that’ analyses. Customers ask us for help with this sort of analysis so that they can sell more products to existing customers, increase transaction sizes to new customers, and target promotional activity.
Another typical customer data mining requirement: RFM Cluster Analysis—in other words, using Recency, Frequency, and Monetary data mining algorithms to identify groups of ‘buyer types’, thereby enabling more focused and effective marketing. Nor are these purely static analyses—another common requirement is to then publish buyer-type information back to the data warehouse fact tables, for example by marking a customer as ‘Gold’ based on the results of the data mining analysis.
We charge for data mining as a fixed price service. Tell us, for instance, that you want to data mine your last year’s transaction history for affinity analysis, and we’ll quote you a fixed price for the job.
Cloud data mining, de-mystified.
While these two data mining options suit many customers, sometimes we’re asked for a third option.
Namely, to help customers develop their own data mining capabilities, so that they don’t have to use (and pay for) our data scientists.
Naturally, we’re happy to oblige. In the usual friendly Matillion way, we’re happy to show customers how to do their own analyses, so that they can self‑serve in future.
And while we don’t offer a full certified course in data mining, experience shows that this helping hand often proves perfectly adequate for customers to begin exploring data mining on their own.
Cloud data mining: the bottom line.
So there we have it. Cloud data mining is a reality, and Matillion can help you to achieve it.
And achieve it whatever your data mining or visualisation tool of choice, and whatever your preferred path for exploring just what data mining, advanced analytics, and data visualisation can deliver for your business.