Data Mining Analysis Techniques

Data mining analysis techniques combine the arts & sciences of ‘invasive’ data mining and deep-dive analytics to generate in-depth data-driven marketing solutions.

It’s not whether companies have enough data; what matters is how they use the data. Data has become a “competitive weapon” that can give business owners an advantage over competitors. It’s about giving customers what they want in a way that’s better and faster than competitors. — 1to1 Media, Oct 2015

Data Mining Analysis Techniques — Challenges

To stay competitive, marketers rely on custom built, specific-purpose, analytics like customer behavior. But in-house reporting tools can’t deliver deep-dive marketing analytics. And business analysts, who derive insights from data, can’t manage complex data or disparate data sources. The roadblock isn’t statistics, but rather, the efforts spent building actionable analytical data sets — data tables that generate analytics. Data mining analytics often require ‘invasive’ data management and data mining far beyond the reach of reporting tools and ‘data scientists’.

far too much handcrafted work — what data scientists call ‘data wrangling’, ‘data munging’, ‘data janitor work’ — is required … spending from 50% to 80% of time mired in preparing unruly data… — New York Times, Aug 2014

This “heavy lifting” — joins, sorts, aggregations, etc. — can include hundreds of millions of data rows. These are the processes of discovering new data relationships, creating new transformations, and developing marketable analytics. Without these data management skills, business analysts never discover the right interactions between customer and product. So marketers fail to get the insights they need. And lose confidence in both reporting and data.

Increasing data volumes and complex data structures overwhelm marketers. And it’s true that many marketers aren’t even sure of the data they have. They don’t believe they have the data they need. And what they do have, they question the quality. Or worse, can’t access.

There are tons of roadblocks. Data is fragmented. There’s no linking data across channels. There are no internal data management skills. data mining analysis techniquesAnd, of course, data quality is poor.

Data Mining Analysis Techniques — IDP

Effective use of customer and transaction data is a competitive weapon. At IDP, we know that your data is challenging and that you have limited resources.

We remove the requirement that business analysts manage large data volumes or complex data structures — bridging the gap between data management and analytics. So analysts focus, not on data preparation, but on making business recommendations from the analytics.

We manage large data volumes with complex structures. We integrate disparate data sources and build rock-solid analytical data sets. And of course, we always collaborate with marketing to generate consistent, marketable results. IDP empowers marketing executives to take control of their data, their analytics and their marketing technology.

When the supporting data is rock solid, then Data Mining Analysis Techniques become much, much easier!