IDP delivers data-driven marketing solutions focused on the art & science of data mining analytics. By focusing on ‘invasive’ data management and deep-dive data mining, our custom solutions target your specific marketing directives.
Retailers should be able to get insights to what triggers consumer motivation to purchase, how they go about making a purchase decision, how they decide where and when to purchase, and even how they use the products they purchase. — Forbes, Oct 2015
Data Mining Analytics — Challenges
Effective use of customer data is a competitive weapon. And marketers rely heavily on their specific-purpose analytic weapons. Yet, marketers don’t get the deep-dive analytics they need. Retailers don’t have a good track record for deriving insights from data they hold — even when that data was only purchase history, tracked through loyalty programs. Data volumes and data complexities overwhelm marketers. And the lack of internal data management skills stymie their initiatives. Many marketers aren’t even sure of the data they do have. They don’t believe they have the data they need. And much of what they have, they question the quality — or they can’t access. The roadblock isn’t statistics, but the efforts to integrate data into actionable analytical data sets that generate analytics.
Central banks are collecting data that is transaction by transaction, trade by trade, asset by asset, mortgage by mortgage, loan by loan, allowing them flexibility to answer questions that could not be answered before.” — BloombergTechnology, Nov 2017
Business analysts don’t engage in data mining analytics for many reasons, from poor data quality to no data management skills. Analysts might be Excel wizards. But they’re not trained in deep-dive analytics. They can’t manage complex data structures, or disparate data sources. So they create incomplete and error-filled reports. Executives don’t get the customer picture they need and lose confidence in both reports and data.
Data Mining Analytics — IDP
Far too much handcrafted work — what data scientists call ‘data wrangling’, ‘data munging’ and ‘data janitor work’ — is required. Data scientists spend from 50% to 80% of their time mired in this mundane labor of collecting and preparing unruly data. — NYT, Aug 2014.
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, but on making recommendations from analytics. We manage large data volumes with complex structures and integrate disparate data sources to build rock-solid analytical data sets, even when facing difficult data. And we always collaborate with marketing to generate actionable results.